towards a wireless eeg system for ambulatory mental …...towards a wireless eeg system for...

Towards a Wireless EEG System for Ambulatory Mental Health Applications

by

Gregory Jackson

A thesis submitted in conformity with the requirements for the degree of Master of Health Science in Clinical Biomedical Engineering

Institute of Biomaterials & Biomedical Engineering University of Toronto

© Copyright by Gregory Jackson 2013

ii

Towards a Wireless EEG System for Ambulatory Mental Health

Applications

Gregory Jackson

Master of Health Science in Clinical Biomedical Engineering

Institute of Biomaterials & Biomedical Engineering

University of Toronto

2013

Abstract

The purpose of this thesis was to create and test a proof-of-concept novel ambulatory EEG

system to monitor emotional valence in real-time. A qualitative comparison of a wireless EEG

acquisition system by the imec group to a gold standard laboratory EEG system was successfully

performed. A new wireless transmission system was created using the Texas Instruments’

ADS1299 EEG front-end chip and quantitatively compared to the gold standard system. This

system and the ADS1299 performance demonstration kit were used to evaluate several equations

for emotional valence classification. Three of these equations were able to correctly classify

emotional valence on a positive-neutral vs. negative basis over 90% of the time on the

performance demonstration kit and over 90% of the time on the wireless system. The wireless

data was acquired and saved on a novel BlackBerry application that also allowed emotional self-

assessment by the user during testing.

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Acknowledgments

I would like to thank my supervisor, Dr. Joseph Cafazzo, and the other members of my

committee Dr. Paul Ritvo and Dr. Jeff Daskalakis for their guidance and support during this

project.

I would also like to thank my main collaborators on the system development side of this project:

Kevin Tallevi for his hardware design and tireless work in getting the wireless system up and

running despite repeated and unexpected difficulties; John Li for his work on the BlackBerry 10

software and significant work in troubleshooting all aspects of the final system; Kevin Armour

for his design work on the headset prototype; and Nathaniel Hamming for his help in

troubleshooting unexpected issues while trying to get the final system working.

Additionally, I would like to thank Natasha Radhu and Yinming Sun at the CAMH for their help

with scheduling and assisting with EEG testing, all volunteers who gave their time to be tested

on all systems in this thesis, and the imec group for their support in testing their wireless EEG

system and for providing the background for the emotional valence work.

Finally, I would like to thank my fiancée Ruth for her infinite patience and support during the

ups and downs of this project. I would also like to thank all of my family and friends for their

support and encouragement through this whole educational journey, and specifically thank

Kenneth Dodd for his editing expertise in getting this thesis to print.

BlackBerry (Research in Motion) and Healthcare Support through Information Technology

Enhancements (hSITE) provided generous financial support to this project.

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Table of Contents

Acknowledgments.......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Figures ............................................................................................................................... vii

List of Tables ................................................................................................................................. ix

List of Appendices ......................................................................................................................... xi

List of Abbreviations .................................................................................................................... xii

Chapter 1. Introduction ..............................................................................................................1

1.1 Research Rationale...............................................................................................................1

1.1.1 The Cost of Mental Illness in Canada ......................................................................1

1.1.2 Quantifying Mental Illness with EEG......................................................................4

1.1.3 Ambulatory Physiological Monitoring Systems ......................................................6

1.2 Problem Statement and Objectives ......................................................................................8

1.3 Scope ....................................................................................................................................9

1.4 Overview of Thesis ............................................................................................................10

Chapter 2. Relevant Literature.................................................................................................11

2.1 Wireless EEG and Capacitive EEG Electrodes .................................................................11

2.1.1 Development of Wireless EEG Systems................................................................11

2.1.2 Dry and Capacitive EEG Electrode Technology ...................................................15

2.2 Emotional Valence, Brain Asymmetry, and Mental Illness ..............................................19

2.2.1 A Psychological Perspective on Emotions ............................................................19

2.2.2 A Neurological Perspective on Emotional Valence...............................................21

2.2.3 Emotional Valence and Asymmetry Related to Mental Illnesses ..........................27

v

Chapter 3. Technical Background ...........................................................................................32

3.1 EEG Background and Specifications .................................................................................32

3.1.1 Digital EEG Requirements ....................................................................................32

3.1.2 Standard Electrode Placement and Electrode Montages .......................................33

3.1.3 EEG Signal Processing ..........................................................................................35

3.1.4 Expanding on Clinical EEG Bands ........................................................................38

3.2 Statistical Analysis and Correlation Methods ....................................................................39

Chapter 4. Comparitive Evaluation of an MBAN EEG Platform vs. Clinical Gold

Standard [23] .............................................................................................................................41

4.1 Introduction ........................................................................................................................41

4.2 Participants .........................................................................................................................42

4.3 Equipment ..........................................................................................................................42

4.4 Testing Method ..................................................................................................................43

4.5 Analysis Method ................................................................................................................44

4.6 Results ................................................................................................................................45

4.7 Conclusion .........................................................................................................................46

Chapter 5. System Development .............................................................................................47

5.1 Hardware Development .....................................................................................................47

5.1.1 Acquisition System ................................................................................................47

5.1.2 Capacitive Electrodes.............................................................................................49

5.1.3 Wireless Cap and Electrode Placement .................................................................50

5.2 Software and Signal Processing Development ..................................................................51

Chapter 6. Testing and Validation ...........................................................................................54

6.1 Participants .........................................................................................................................54

6.2 Testing ADS1299 Demonstration Kit ................................................................................54

6.3 Testing Electrodes ..............................................................................................................56

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6.4 Emotional Valence Testing Protocol .................................................................................57

6.5 Emotional Valence Data Analysis .....................................................................................59

6.6 Testing Full Mobile System ...............................................................................................60

Chapter 7. Results ....................................................................................................................61

7.1 Validation of Performance Demonstration Kit ..................................................................61

7.2 Emotional Valence Testing ................................................................................................65

7.3 Proof-of-Concept Data Validation Results ........................................................................68

7.4 Proof-of-Concept Emotional Valence Results ...................................................................72

Chapter 8. Discussion and Conclusions ..................................................................................73

8.1 Discussion of Results .........................................................................................................73

8.1.1 Validation of ADS1299 Performance Demonstration Kit .....................................73

8.1.2 Comparison of Emotional Valence Calculation Methods .....................................74

8.1.3 Proof-of-Concept System Testing..........................................................................75

8.2 Limitations and Difficulties Encountered ..........................................................................77

8.3 Future Directions ...............................................................................................................79

8.4 Conclusions ........................................................................................................................81

8.5 Summary of Contributions .................................................................................................82

References ......................................................................................................................................83

Appendices .....................................................................................................................................97

vii

List of Figures

Figure 1 - Estimated Wage-Based Productivity Impact for Mental Illnesses in Canada (left);

Estimated Total Cost of Mental Illnesses in Canada (right) [4] ..................................................... 2

Figure 2 - Estimated Reduction in Total Direct Costs for Mental Illnesses in Annual Future

Value Terms, Four Scenarios [4] .................................................................................................... 3

Figure 3 - Examples and Definitions of EEG Bands ...................................................................... 4

Figure 4 - Medical Body Area Networks [15] ................................................................................ 6

Figure 5 - Epidermal Electronics [18] ............................................................................................ 7

Figure 6 - Diagram of Proposed System ......................................................................................... 9

Figure 7 - Annotated Image of Batteryless 19 uW Energy Harvesting BSN [39] ........................ 14

Figure 8 - a) Conventional gel electrode functionality b) Dry electrode with microtips [51] ...... 16

Figure 9 - 12-Point Circumplex Model of Affect [67] ................................................................. 20

Figure 10 - Self-Assessment Manikin: Valence, Arousal, Dominance [70]................................. 21

Figure 11 - Sectors of the prefrontal cortex: lateral view (left), ventral view (right) [74] ........... 22

Figure 12 - Statistical maps showing blood oxygen signal intensity associated with (left) valence

ratings, and (right) arousal ratings [77]......................................................................................... 24

Figure 13 - Standard 10-20 Electrode Positioning with ACNS Modification [103] .................... 33

Figure 14 - Equipment setup (left to right) a) Imec 8-channel EEG ASIC; b) NeuroScan

SynAmps connection; c) NeuroScan QuikCap 64-channel EEG cap; d) Custom-made 80-pin

connection board ........................................................................................................................... 43

Figure 15 - Electrodes from 10-20 system used for testing .......................................................... 43

Figure 16 - 3D Representation of Wireless EEG Acquisition System ......................................... 47

viii

Figure 17 - Functional Block Diagram of ADS1299 [122] .......................................................... 48

Figure 18 - Design of Active Capacitive Electrode ...................................................................... 49

Figure 19 - Electrode Placement for EEG Cap ............................................................................. 50

Figure 20 - Images of Headset Prototype ..................................................................................... 51

Figure 21 - Screenshots of EEG App on BlackBerry 10 - Bluetooth Device Selection (left);

Graph of time signals (centre); Emotional Self-Assessment (right) ............................................. 53

Figure 22 - Self-Assessment Manikin (Pleasure) [70] .................................................................. 57

Figure 23 - Emotional Valence Experimental Protocol: ............................................................... 57

Figure 24 - Emotional Valence vs. Emotional Arousal Scatterplot of IAPS Images ................... 58

ix

List of Tables

Table 1 - Estimated 12-month prevalence of any mental illnesses in Canada [4] .......................... 2

Table 2 - Relative Risk of Adult Mental Illnesses Given Prior Adolescent Illness [4] .................. 3

Table 3 - Information on EEG Bands [109].................................................................................. 38

Table 4 - Significance of Different Correlation Coefficients [114] .............................................. 39

Table 5 - Correlation for 2-second windows: Pearson’s Correlation Coefficient and Confidence

Valuesa .......................................................................................................................................... 45

Table 6 - Correlation for 10-second windows: Pearson’s Correlation Coefficient and Confidence

Values b

......................................................................................................................................... 45

Table 7 - Number of Segments Used for Correlation Analysis .................................................... 56

Table 8 - Emotional Valence Equations Tested ............................................................................ 59

Table 9 - Pearson's Correlation (r, p) for Amplitude and Power Spectra of 4-second and 20-

second windows with Averaged and Scaled ADS1299 Data ....................................................... 61

Table 10 - Pearson's Correlation (r, p) Repeated with Raw ADS1299 Data ................................ 61

Table 11 - Lin's Covariance Correlation Coefficient (Rc) for Amplitude and Power Spectra of 4-

second and 20-second windows with Averaged and Scaled ADS1299 Data ............................... 62

Table 12 - Lin's Covariance Correlation Coefficient Repeated with Raw ADS1299 Data .......... 62

Table 13 - Intraclass Correlation Coefficients (ICC) for Amplitude and Power Spectra of 4-

second and 20-second windows with Averaged and Scaled ADS1299 Data ............................... 63

Table 14 - Intraclass Correlation Coefficients repeated with Raw ADS1299 Data ..................... 63

Table 15 - Emotional Valence Testing on ADS1299 Demonstration Kit .................................... 65

Table 16 - Testing Emotional Valence Equations on DEAP Dataset ........................................... 66

x

Table 17 - Pearson's Correlation for Subject 1 on Proof of Concept System (Averaged and Scaled

Data) – Effective Sampling Rate Calculated as 234 Hz ............................................................... 68


Data) - Effective Sampling Rate Calculated as 234 Hz ................................................................ 68







Table 22 - Lin's Correlation for Subject 1 on Proof of Concept System (Averaged and Scaled










Table 27 - Emotional Valence Classification for Proof-of-Concept System Test ........................ 72

xi

List of Appendices

Appendix 1 - Register Settings for ADS1299............................................................................... 97

Appendix 2 - IAPS Images Used in Emotional Valence Study .................................................... 98

Appendix 3 - Screening Form for EEG Study Participants at CAMH ....................................... 100

xii

List of Abbreviations

ACNS American Clinical Neurophysiology Society

ADC Analog-Digital Converter

ADHD Attention Deficit Hyperactivity Disorder

AgCl Silver chloride (Silver-silver chloride electrodes)

ASIC Application Specific Integrated Circuit

ATM Acquisition and Transmission Module

BCI Brain-Computer Interface

BTLE Bluetooth Low Energy (Bluetooth 4.0)

bps Bits per second (also kbps, Mbps, etc.)

CAMH Centre for Addiction and Mental Health

CMRR Common-Mode Rejection Ratio

DFT Discrete Fourier Transform

ECG Electrocardiogram / Electrocardiography

EEG Electroencephalogram / Electroencephalography

EOG Electrooculogram / Electrooculography

ERG Electroretinography

FCC Federal Communications Commission (United States)

FFT Fast Fourier Transform

fMRI Functional Magnetic Resonance Imaging

GB Gigabytes (230

bytes or 2,000,000,000 bytes; also MB, kB, etc.)

Hz Hertz, unit of frequency equal to 1/s (also kHz, MHz, GHz, etc.)

IAPS International Affective Picture System

ICC Intraclass Correlation Coefficient

IFFT Inverse fast Fourier transform

KNN K-Nearest Neighbour

mAh Milliamp hours (measure of battery power)

MBAN Medical Body Area Network

MDD Major Depressive Disorder

MEG Magnetoencephalography

MHCC Mental Health Commission of Canada

xiii

NIH National Institute of Health (United States)

OCD Obsessive Compulsive Disorder

ODD Oppositional Defiant Disorder

OSET International Organisation of Societies for Electrophysiological Technology

pF Picofarad (capacitance; also F, etc.)

PET Positron Emission Tomography

PTSD Post-Traumatic Stress Disorder

RF Radio Frequency

SAM Self-Assessment Manikin

SoC System on Chip

SSVEP Steady-state visually evoked potentials

SUD Substance Abuse Disorder

SVM Support Vector Machines

USB Universal Serial Bus

V Volts (also mV, kV)

µW Microwatts (also mW, W)

1

Chapter 1. Introduction

1.1 Research Rationale

1.1.1 The Cost of Mental Illness in Canada

In 2002, Health Canada produced A Report on Mental Illnesses in Canada [1], which included

detailed statistics on mental illnesses in Canada. Health Canada investigated mood disorders,

schizophrenia, anxiety disorders, personality disorders, eating disorders, and suicidal behaviour

in particular. Their report also stated that over 10% of all hospitalizations in general hospitals

for patients between 15 and 44 are due to one of seven mental illnesses, and 3.8% of

hospitalizations across all ages are due to mental illnesses [1]. The Government of Canada

followed up on this report in 2006 with a report titled The Human Face of Mental Health and

Mental Illness in Canada [2], which referenced Health Canada’s report in producing further

statistics.

Following the 2006 research, the MHCC was created by Health Canada to “create a mental

health strategy, work to reduce stigma, advance knowledge exchange in mental health, and to

help people who are homeless and living with mental health problems” [3]. The MHCC

commissioned a study in 2010 to fill a gap in information about people living with mental

illnesses and the associated costs of these illnesses today [4], which was published in 2013. The

study showed that the economic cost to Canada of mental health problems and illnesses is at least

$50 billion per year, or 2.8% of Canada’s 2011 gross domestic product, and that over the next 30

years the total economic cost will add up to more than $2.5 trillion. The MHCC also determined

that the cost to business was at least $6 billion in lost productivity from absenteeism,

presenteeism, and turnover in 2011, and that the cumulative cost over the next 30 years may add

up to nearly $200 billion. The MHCC explain that over the next 30 years the total economic cost

of mental illnesses in Canada will add up to more than $2.5 trillion.

In figure 1, shown below from the MHCC report, the left side shows the estimated annual future

value and cumulative present value of wage-based productivity loss due to mental illnesses. The

right side shows the cumulative present value and annual future value of the total cost of mental

illnesses in Canada.

2

Figure 1 - Estimated Wage-Based Productivity Impact for Mental Illnesses in Canada

(left); Estimated Total Cost of Mental Illnesses in Canada (right) [4]

The report states that, in any given year, one in five people in Canada experience a mental health

problem or illness, including more than 28% of people aged 20-29. By the age of 40, roughly

half of people in Canada will have had or have a mental illness. The impact of mental illnesses

is greatest in workplaces and working aged people, as 21.4% of the working population in

Canada currently experiences mental health problems and illnesses, which account for

approximately 30% of short- and long-term disability claims.

The MHCC produced updated numbers of 12-month prevalence of mental illnesses including

mood and anxiety disorders, schizophrenia, SUD, ADHD, oppositional defiant disorder ODD,

conduct disorders, and dementia, which are shown with future estimates in the table below with

no interventional changes.

Table 1 - Estimated 12-month prevalence of any mental illnesses in Canada [4]

Total (%) 2011 2021 2031 2041

Males 3,178,446 (18.7%) 3,415,276 (18.3%) 3,736,764 (18.6%) 4,044,688 (18.9%)

Females 3,619,181 (20.9%) 3,994,881 (21.0%) 4,448,014 (21.6%) 4,866,402 (22.2%)

Total 6,797,627 (19.8%) 7,410,157 (19.7%) 8,184,778 (20.1%) 8,911,090 (20.5%)

Furthermore, many mental illnesses start in the young (those from ages 9-19). Mood and anxiety

disorders affect 11.7% of people in Canada across all ages, and 12.1% of those aged 9-19, while

substance use disorders affect 5.9% of people across all ages, and 6.8% of those aged 9-19.

Adolescents who suffer from mental illnesses are at significantly increased risk of suffering more

mental illnesses as adults. Table 2, below, shows the specific relative risk ratios of adults

developing specific mental illnesses if they suffered specific disorders as adolescents.

3

Table 2 - Relative Risk of Adult Mental Illnesses Given Prior Adolescent Illness [4]

Male Female

Prior Adolescent Illness Anxiety Mood

Disorders

SUD Anxiety Mood

Disorders

SUD

ADHD 2.21 1.23 2.23 2.00 1.22 2.52

Anxiety - 2.43 1.04 - 2.33 1.05

Conduct Disorders 1.78 1.74 2.81 1.67 1.69 3.46

Mood Disorders 3.05 - 1.38 2.70 - 1.44

ODD 2.74 2.08 1.84 2.40 1.99 2.03

SUD 2.59 1.88 - 2.31 1.81 -

The MHCC estimates that by reducing the number of people experiencing a new mental illness

in a given year by 10%, there would be an economic saving of at least $4 billion per year after 10

years, and over $20 billion in 30 years. Providing early access to healthcare to keep people out

of hospitals or the criminal justice system can generate cost savings, and improving mental

health management in the workplace can significantly reduce losses in productivity. The

estimates are shown in figure 2 below, from the MHCC report.

Figure 2 - Estimated Reduction in Total Direct Costs for Mental Illnesses in Annual Future

Value Terms, Four Scenarios [4]

The Canadian Mental Health Association’s Framework for Support [5] discusses the three pillars

for recovery for people with mental illness, which are a Personal Resource Base, a Community

Resource Base, and a Knowledge Resource Base. The Personal Resource Base refers to the

person being in control of his or her own life, and the ability to understand the illness and

perform self-care behaviour greatly improves the lifestyle of someone living with mental illness.

Ambulatory monitoring can contribute significantly to this pillar.

4

1.1.2 Quantifying Mental Illness with EEG

EEG is the measurement of scalp electrical activity generated by neuronal activity in the brain.

In particular, the electrical activities of large, synchronously firing groups of neurons are

measured with electrodes placed on the scalp [6]. The activity is measured in real time and is

converted to the frequency domain for clinical applications. The frequency data is

conventionally divided into five bands as specified in a set of example EEG signals in figure 3,

below. There is occasional divergence on the precise EEG band definitions, but these values are

commonly used in major publications including Brain [7] and Journal of Neurophysiology [8].

Figure 3 - Examples and Definitions of EEG Bands

Research has demonstrated that each frequency band is related to different levels and types of

arousal. In particular, alpha waves are seen most prominently during early sleep stages, low

arousal, and when the subject’s eyes are closed. Beta waves are most prominent during resting

wakefulness [9]. In healthy subjects, delta and theta rhythms are greatest during deep sleep, as

they are normal signs of deactivated brain networks. When they are prominent in waking states,

they are considered abnormal. They can be caused by structural cortical lesions including stroke,

tumors and scars, and concentrations of slow wave activities (delta and theta, particularly) can be

found in individuals with psychiatric disorders who do not have obvious structural brain damage

[10].

5

In schizophrenia in particular, a study found that resting EEG readings can contain abnormalities

including increased power in low frequency waves (delta and theta) and diminished alpha-band

power. These findings may indicate the presence of thalamic and frontal lobe dysfunction which

appears to be unique to schizophrenia [11]. Another study examining the distribution of slow

wave activity in a group of healthy subjects, patients with schizophrenic or schizoaffective

diagnoses and patients with affective or neurotic/reactive diagnoses using resting MEG (which

contains similar features to EEG) found significantly more intense slow wave activity,

particularly in frontal and central areas. By comparison, affective disorder patients showed

fewer slow wave generators in frontal and central regions compared to both healthy subjects and

schizophrenia patients. The regions of abnormal slow wave activity corresponded to gray matter

loss in schizophrenic patients, suggesting that this activity may be used as a measure of altered

neuronal network architecture [12].

Another recent study evaluated whether the abnormal frequency composition of the resting state

EEG in schizophrenia and bipolar disorder showed similarities to first-degree biological relatives

of patients. Schizophrenia patients and their relatives showed increased beta frequency activity,

which suggests that disturbances in resting state brain activity may be specific to genetic liability

for schizophrenia. This similarity was not, however, seen in bipolar patients or their relatives.

Furthermore, schizophrenia patients had increased low-frequency activity, as mentioned

previously, which was not seen in bipolar patients or either group of relatives. The study

determined that excessive high-frequency EEG activity in frontal brain regions may reflect

genetic vulnerability to schizophrenia, while low-frequency abnormalities are more related to

disorder-specific pathophysiology [13]. Furthermore, patients with schizophrenia were found to

have significant deficits of cortical inhibition of gamma waves in the dorsolateral prefrontal

cortex compared to healthy subjects and patients with bipolar disorder, but no significant deficits

were found in the motor cortex. This finding suggests that there may be an important frontal

neurophysiological deficit that contributes to symptoms of schizophrenia [7].

Quantitative EEG analysis of patients with OCD revealed greater slow-wave activity and less

alpha activity in left frontotemporal locations compared to control subjects. Furthermore, the

dysfunction was found to be greater in female patients, and correlated positively with the

severity of the OCD. There was also greater dysfunction found in patients who responded to

OCD treatment [14].

6

1.1.3 Ambulatory Physiological Monitoring Systems

The rapid progression of computing technology and wireless handheld devices in particular, has

allowed for the development of small sensors to monitor illnesses and chronic conditions.

MBANs, which have recently been approved by the United States’ FCC for protected wireless

spectrum, are networks of physiological sensors worn on or implanted in the body. These

sensors can monitor different vital signs, such as temperature, blood pressure, and glucose levels,

and transit the information to a central node such as a smartphone [15]. Shown in the figure

below is an example of an MBAN with sensors to monitor glucose, toxins, blood pressure, ECG,

EEG, hearing, vision, positioning, and more.

Figure 4 - Medical Body Area Networks [15]

A transition document released by the FCC [16] discusses some demonstrations of MBAN

technology, including Fetal Telemetry to noninvasively monitor fetal health while allowing a

mother to move freely; LifeLine Home Care Pendants, which collect health information for

elderly patients and patients with chronic diseases, allowing them to live independently; and

Predictive and Early Warning Systems to provide continuous monitoring for the prevention of

sudden and acute deterioration of patients’ conditions. The FCC estimates that prevention of

unplanned transfers of patients could save up to $1.5 million USD per month in healthcare costs.

Furthermore, disposable sensors could save an estimated $2,000-$12,000 per patient, or over $11

billion USD in the U.S. Remote monitoring of patients with congestive heart failure alone could

save over $10 billion USD per year in the U.S. The FCC also found that patients accessing their

7

health data on mobile phones increased by 125 percent from 2010 to 2012, and that mHealth

(mobile health) could be a $2-$6 billion USD industry by 2013. Finally, and most significantly,

industry estimates show that remote monitoring of four chronic conditions could save just under

$200 billion USD from 2008 to 2033 [17]. These 4 conditions, and their expected savings, are:

Congestive Heart Failure $102.5 billion USD

Diabetes $54.4 billion USD

Chronic Obstructive Pulmonary Disease $24.1 billion USD

Chronic Skin Ulcers $16.0 billion USD

Sensors are also shrinking to the point that there are now sensors that have similar thicknesses,

elastic moduli, bending stiffness and mass densities to skin [18]. The epidermal electronics

created by Kim et al. incorporate electrophysiological, temperature, and strain sensors, as well as

transistors, light-emitting diodes, photodetectors, radio frequency inductors, capacitors,

oscillators, and rectifying diodes. Solar cells and wireless coils provide the power supply. The

sensors measure electrical activity produced by the heart, brain, and skeletal muscles, and are

shown in the figure below.

Figure 5 - Epidermal Electronics [18]

Monitoring EEG with MBANs is relatively new, as the earliest literature result was published in

2004 by Berka et al. [19], in a study that presents an integrated hardware and software solution

for acquisition and real-time analysis of EEG to monitor indices of alertness, cognition, and

memory. Berka et al. use a six-channel EEG headset with a sampling rate of 256 Hz and find it

to be a robust and reliable method for monitoring alertness and cognitive workload. Berka et al.

also discuss future developments including more complex monitoring systems that connect to a

wider range of receivers.

8

1.2 Problem Statement and Objectives

The purpose of this thesis is to create and test a proof-of-concept novel ambulatory EEG system

to monitor emotional valence in real-time. The system would take the concepts put forward by

Brown et al. in their paper on wireless emotional valence [20] and attempt to reproduce the

results in a more fully ambulatory system, as their work required the wireless receiver to be

attached to a computer. To assess the performance of emotional valence monitoring, it is

important to verify signal quality in the acquisition platform, and to have the user complete an

emotionally affective task and calculate the response.

From a clinical perspective, this system would be a stepping stone to a full MBAN system used

to monitor patients with diagnosed mental illnesses and improve their daily lives by providing

them with a means for self-care at home and at work. Presently, there is a shortage of

ambulatory monitoring for people with mental illnesses. In order to be monitored and receive

care, they need to be in a hospital or clinical setting. Providing a means of detecting adverse

events may allow a patient to perform self-care and self-regulating behaviour as well as notifying

their responsible clinician. As with other conditions such as heart disease or diabetes,

preventative care can help reduce the economic cost to the healthcare system and the personal

cost to people living with illnesses. It can also provide them with a means of continuing with

their normal daily lives.

In order to measure emotional response in a repeatable way, the presentation of affective stimuli

is often used. In particular, one of the most common systems is the IAPS [21], which is a large

series of images that have been rated by a large number of users on their emotional valence and

emotional arousal score and an average score is provided. These images can be used to provide

an emotional stimulus with an expected response to the user. The user can prove his or her

response to the stimulus using a feedback method that can be matched to the appropriate window

of measured EEG data.

The objective of this thesis is to determine whether emotional valence can be monitored with

acceptable accuracy in real-time using an ambulatory EEG system connected to a smartphone

while allowing the user to annotate emotional events.

9

1.3 Scope

It was decided that the scope of this thesis would focus on the creation of an ambulatory EEG

system that could transmit signal to a smartphone for data storage and analysis and that could

monitor emotional valence in real-time. The system diagram shown in figure 6 summarizes the

components and basic operational principles of the ambulatory EEG system. The headband

contains four EEG measurement electrodes as well as bias and reference electrodes. These

electrodes are connected to an acquisition board using an analog-to-digital conversion chip that

transmits the digitized EEG signal by BTLE to a smartphone. The smartphone stores the EEG

signal and calculates the emotional valence value while also allowing the user to annotate

emotional events to compare self-assessments to measured values in post-processing.

+

-

+

-

R

1

2

B

+

-

+

-

ADC

AVDD/2

CONTROL

ADS1299

Instrumentation AmplifierG=100

Instrumentation AmplifierG=100

IN A333

IN A333

Direct Connection

Bluetooth Low Energy Connection

Provide Emotional Valence feedback to User

Annotate emotional events

Figure 6 - Diagram of Proposed System

For the hardware portion of the project, after testing several wireless EEG acquisition systems, it

was decided that a custom system would be created as most available wireless systems used

wireless protocols that were not directly compatible with smartphones. As part of the testing, a

complete qualitative evaluation of an ultra-low-power wireless EEG acquisition system by the

imec group [22] was performed [23] with volunteers who passed a screen process that ensured

their eligibility as healthy control subjects. This testing protocol was also used to validate the

analog-to-digital front-end chip used in the custom-made EEG acquisition system. The system

10

uses Bluetooth Low Energy communication which is compatible with a number of cutting edge

smartphones. Furthermore, it was decided that dry electrodes would be preferred as they are

easier to apply and are less untidy. While research was done into capacitive electrodes and

prototypes were created, the technology available in the budget and the timeline for this thesis

were not able to provide a sensitive enough response to adequately measure EEG signals. This

situation meant that conventional gel electrodes had to be used.

On the software side, a basic signal processing application for BlackBerry 10 was created in

order to take the EEG signal in its raw time-domain form and convert it to frequency domain in

order to measure the emotional valence in short time windows. The application also allowed the

user to annotate events with an emotional valence rating to be read in post-processing.

Finally, the whole system and emotional valence measurement were tested using more volunteer

test subjects. This testing involved the acquisition of baseline readings followed by the

presenting of IAPS images with the subject rating each image after its presentation. The

emotional valence was measured in real-time and verified afterwards and compared to the

subjects’ self-assessment ratings.

1.4 Overview of Thesis

In Chapter 2, an overview of literature is presented on the subjects of wireless EEG technology

and emotional valence as a monitor of mental illness. Chapter 3 provides technical background

on EEG technology, signal processing for EEG data, and statistical analysis and correlation

methods. Chapter 4 is a comparative evaluation of a wireless EEG system to a gold standard

laboratory system, which was published as a conference proceeding. Chapter 5 describes system

development, including hardware development as well as software and signal processing. The

hardware development was led by technologist Kevin Tallevi. The software development was

led by programmer John Li. My responsibilities were to provide EEG-specific hardware

requirements, signal processing code, and to test the system and its components. Chapter 6

describes the testing and validation of the individual components of ambulatory EEG system,

including the emotional valence score calculations, as well as the full ambulatory system.

Chapter 7 presents the results of testing and validation. Finally, Chapter 8 contains discussion,

summary of contributions of this work, conclusions, difficulties encountered, and future

directions of this work.

11

Chapter 2. Relevant Literature

2.1 Wireless EEG and Capacitive EEG Electrodes

2.1.1 Development of Wireless EEG Systems

Electroencephalography of humans was first performed by German psychiatrist Hans Berger in

1924 [24], who published some 20 scientific papers on EEG afterward. In recent years, digital

EEG recording has become the most popular method, leading to guidelines for digital EEG being

created by the OSET [25]. Guidelines and ideal settings for EEG are discussed further in section

3.1 This section also includes recommendations on analog to digital conversion, reference and

channel placements, analysis, and advantages and cautions of digital EEG.

The newest evolution of EEG has been the creation of wireless acquisition systems, which allow

more freedom of movement to the subject and provide the opportunity for ambulatory

monitoring. The imec group from the Netherlands has produced significant research into ultra-

low-power wireless EEG ASIC. In 2006 [26], the imec group presented a custom-built 300 µW

eight-channel front-end ASIC to implement a portable EEG acquisition system. Each channel of

the ASIC contained an instrumentation amplifier, spike filter, and variable gain stage with very

low noise and very low power consumption. The system was capable of operating more than

seven months from two AA batteries. In 2008 [27], the system was refined to 200 µW power

consumption. Calibration and Electrode Impedance Measurement Modes were added to the

ASIC to increase the ease of use of the system. The ASIC had a wireless radio added to it for

signal transmission. The signal quality of the EEG is evaluated in Chapter 4 of this thesis by

quantitatively comparing it to a clinical gold standard system [23].

In the literature published since 2010, much of the research into wireless EEG systems is often

focused on either brain-computer interface devices or on patient monitoring. Brain-computer

interfaces are becoming more sophisticated in the research field. While commercial systems

often focus on gaming or control of computers, research systems are more concerned with

offering communication and control to motor-disabled subjects. One study by Filipe et al. in

2011 presents a custom ASIC supporting RF transmission of 32 channels of EEG [28]. This

system records EEG at 1 kHz sampling rate with 12-bit resolution and transmits using the

Medical Implant Communication Service at 402-405 MHz.

12

A system implemented by Kim et al. in 2012 [29] included multi-channel EEG and head motion

signal acquisition by adding a Bluetooth communication module and head motion tracker to a

commercial EEG BCI headset. The system was tested indoors and outdoors to prove the validity

of a portable multi-channel signal acquisition system.

Another wireless EEG based BCI system created in 2013 by Lin and Huang [30] to control

electric wheelchairs through a Bluetooth interface for paralyzed patients. This system used two

EEG channels and a signal processing unit to extract EEG and winking signals to transform them

into control signals that drive the electric wheelchairs.

From a monitoring standpoint, in 2010 Verma et al. created a micro-power EEG acquisition SoC

to focus on seizure detection [31]. This chip corresponds to one EEG channel, but up to 18

channels can be worn by a single patient to detect seizures as part of a chronic treatment system.

This system focuses on amplification, filtering, feature extraction, and minimizing power

consumption.

From an MBAN perspective, Chen et al. [32] published research in 2010 on a flexible wireless

body area network node platform using ZigBee wireless technology for EEG monitoring. Their

EEG conditioning system includes pre-amplification and filtering that is able to pass signals with

minimal attenuation from 2-50 Hz with notch filtering for electrical noise. This system connects

to a centralized MBAN node, which sends signals a ZigBee wireless internet gateway that can

connect to a database as well as clinicians and relevant parties.

In 2010, Dilmaghani et al. presented a wireless multi-channel EEG recording device [33]. This

system includes analog filtering and gain amplifiers to filter noise and amplify the EEG signals.

The microcontroller digitizes the analog signal and digital filtering removes power-line

interference. The system transmits data with Bluetooth to an end device where the data can be

post-processed. The system was tested with sample voltage rather than live subjects.

Chen and Wang from the University of British Columbia created a wearable, wireless EEG

acquisition and recording system in 2011 [34]. Their system was broken down into a data

acquisition circuit and a data transmission and receiving unit. The acquisition circuit includes a

voltage follower, instrumentation amplifier, DC restorator, right-leg drive or bias circuit, band

pass filter, and power supply on a PCB. The data transmission and receiving unit consists of an

13

ADC and a ZigBee wireless module. Preliminary testing showed that high quality physiological

signals could be acquired while the subject performed daily-life tasks.

In 2012, Thie et al. created a system with four bipolar channels for biomedical signal acquisition

usable for EEG, ECG, and ERG [35]. Their focus was on consistent data transmission for

reliable recording of visually evoked potentials. Data was continuously streamed at 915 MHz

and a constant delay of 20 ms was added to remove distortion and minimize error rates.

Wang et al. at the University of California, San Diego and National Chiao-Tung University in

Taiwan created a cell phone based drowsiness monitoring system in 2012 [36]. This system uses

non-prep dry EEG sensors and Bluetooth transmission protocol with an Android smartphone.

The headset has four channels with a microprocessor, pre-amplifier and battery charger, 24-bit

ADC, Bluetooth module, and dry spring-loaded EEG sensors. The Bluetooth module transmits

data to an Android smartphone with no loss in signal processing performance. The Drowsiness

Monitoring and Management system continuously observes EEG dynamics and delivers arousing

feedback to users if they are experiencing drowsiness or a cognitive lapse. The system is then

able to assess the effect of the feedback in near real-time.

In 2012, Boquete et al. presented an 8-channel system for capturing bioelectric signals using

ZigBee wireless transmission protocol that can be used for ECG, EEG, EOG, and more [37].

This system focused on an ATM and a PC host to process, store, and display the data sent by the

ATM. The PC host can also set the parameters of the ATM. The ATM contains an analog front

end, a microcontroller, and a ZigBee transceiver. The analog front end has eight differential

inputs connected to a multiplexer which is then connected to an ADC chip. Boquete et al. were

able to sample 8 channels at 400 Hz each with 12 bit resolution and run the system for 68 hours

on a 6800 mAh battery.

Sawan et al. presented a wireless recording system for NIRS and scalp EEG for non-invasive

monitoring or intracerebral EEG for invasive monitoring in 2013 [38]. The system uses

Bluetooth to transmit at 2 kHz with 24-bit resolution. The system can run 8 EEG and 32 NIRS

channels simultaneously. The wireless signals were compared to a wired system and normalized

root-mean square deviation was under 2%. Sawan et al. also presented a wireless EEG recording

system for epilepsy.

14

In 2013, Zhang et al. presented an ultra-low power batteryless energy harvesting body sensor

node SoC capable of acquiring, processing, and transmitting ECG, EMG and EEG data using RF

transmission [39]. This node is fabricated on commercial 130 nm CMOS technology and is

designed so that multiple chips can be integrated to create a flexible and reconfigurable wireless

system with autonomous power management and operation from harvested power. The chip was

shown to perform ECG heart rate extraction and atrial fibrillation detection while consuming just

19 µW, allowing it to run only on harvested energy. The system is reconfigurable for EEG and

MEG applications and a four-channel front-end with ADC, filtering and data memory is

contained on a 2.5mm x 3.3mm board. An image of this chip is shown below, in figure 7.

Figure 7 - Annotated Image of Batteryless 19 uW Energy Harvesting BSN [39]

From a commercial perspective, there are several systems that have been created primarily for

BCI and game design applications. One of the most commonly used commercial systems in

research is the Emotiv Epoc, a 14-channel EEG headset that acquires and digitizes EEG signals

using felt covered sensors and a proprietary USB based wireless communication protocol. To

date there are over 30 papers published using the Emotiv system [40] and it is being used for BCI

and gaming design. The Emotiv Epoc system was qualitatively tested during this project, but

was ultimately not used due to the USB requirement in the wireless communication. The Epoc

samples at 2048 Hz initially and then downsamples the data to 128 Hz for output and BCI

applications at 14 bits. The system is able to resolve to 0.51µV at a bandwidth of 0.2-45 Hz.

NeuroSky is a company that has several one-channel EEG headsets, including the MindWave,

MindWave Mobile, and MindSet, as well as the ThinkGear ASIC Module for EEG acquisition.

Their products are used in mobile gaming and BCI applications and have had several academic

15

papers make primary use of them [41]. As they are only single-channel forehead systems, they

are not ideal for clinical applications; however, the ThinkGear ASIC can be used to acquire EEG

channels at 512 Hz as part of a full system.

Muse by InterAxon is a flexible four-sensor headband designed to work with applications for

brain health, fitness training, stress management, and more. The Muse headset samples at up to

600Hz. InterAxon is aiming to have the device commercially ready by late 2013 [42].

The X-Series EEG Headset Systems from Advanced Brain Monitoring come in 4-, 10-, and 24-

electrode configurations. The systems can be used for neurofeedback, BCI applications, ERP

analysis and more [43]. The X-Series headsets sample at 256 Hz with 16-bit resolution and have

at least six hours of battery life in the 10- and 24-electrode configurations. The data can be

transmitted via Bluetooth Class 2 or saved to an onboard SD card for longer battery life.

In a partnership between imec, Holst Centre and Panasonic, an eight-channel wireless EEG

headset was created using the ultra-low-power EEG monitoring chipset mentioned previously

[22]. This system is currently being made available to industrial research partners [44]. The

battery lasts up to 22 hours with 8 channels of EEG and impedance levels can be monitored.

The g.Nautilus from g.tec is a 32-, 16-, or 8-channel wireless EEG platform available with

g.tec’s own active or active dry electrodes and contactless charging using a 2.4GHz wireless

transmission band [45]. The system samples at 250 or 500 Hz with 24-bit resolution and can

record continuously for up to eight hours.

Enobio from Neuroelectrics is an 8- or 20-channel EEG system using Bluetooth communication

with MicroSD data storage capability [46]. It samples data at 500 Hz with 24-bit resolution and

very low noise. The battery can last 16 hours on Bluetooth or over 24 hours with SD storage.

2.1.2 Dry and Capacitive EEG Electrode Technology

In order for an EEG system to be fully ambulatory for non-clinical use, dry electrodes will be an

essential component. Conventionally, EEG electrodes are made of one of silver-silver chloride,

gold plated silver, tin, silver, sintered silver-silver chloride (AgCl), platinum, or stainless steel.

These electrodes are used with gel, which reduces the impedance so that a clean signal can be

detected from the scalp [47]. The NeuroScan Quik-Cap used for the comparative evaluation of

16

the imec system in Chapter 4 and the validation testing of the ADS1299 Performance

Demonstration Kit in section 6.2 has silver-silver chloride electrodes that provide the lowest

offset voltage, rate of drift, noise level, and have the best suitability for DC-coupled recording

and long time-constant AC-coupled recording.

Dry electrodes are less common than wet electrodes, but come in several different forms. An

early example of a dry EEG electrode was presented by Taheri et al. in 1994 [48] that used a 3

mm stainless steel disk as a sensing element with a nitride coating on the contact side. The

power spectra of the prototype electrode compared well to conventional gel electrodes.

A common format of dry electrode being used recently is silver-silver chloride resistive

electrodes with special contact posts [49]. The contact posts allow the electrode to make contact

with the scalp through hair, and the electrodes provide reasonably good performance compared

with conventional electrodes using gel and skin preparation. The electrodes are, however, not

very comfortable as they make contact with the skin. Another similar dry contact electrode is the

g.Sahara from g.tec. The electrodes are designed with long pins to make contact through the hair

to the skin, and provide good performance for SSVEPs despite having significantly higher

impedance than traditional gel electrodes [50].

Figure 8 - a) Conventional gel electrode functionality b) Dry electrode with microtips [51]

An example of how these electrodes work is shown in figure 8, above. On the left, a

conventional gel electrode is shown, where the gel allows the surface of the electrode to be

connected to the stratum corneum and the epidermal layer. On the right, a dry electrode has

small sharp microtips that penetrate to the epidermis to read the EEG potentials. These

electrodes have equal frequency response in conductivity and permittivity to traditional silver

chloride electrodes [51].

17

A similar electrode created by Salvo et al. in 2012 [52] contained 180 conical needles 250 µm

apart on a circular base. This electrode showed comparable results to conventional wet

electrodes for ECG and EEG applications. Similarly, Liao et al. in 2011 designed, fabricated and

validated a dry-contact EEG sensor [53] that contained 17 spring contact probes on each sensor.

Each probe included a probe head, plunger, spring, and barrel and was inserted into a flexible

substrate. Liao et al. found that the sensor was reliable in measuring EEG signals with no skin

preparation or gel when compared to conventional wet electrodes.

A study by Mihajlovic et al. on replacing conductive gel electrodes with dry and water based

electrodes [54] used SSVEPs to evaluate and compare the electrode performance. Mihajlovic et

al. found that the dry and water based electrodes had acceptable classification accuracy with

somewhat lower communication speed compared to the gel electrodes, and could be used in

brain-computer interface application if the lower communication speed was acceptable. A

methodological review of dry-contact and noncontact biopotential electrodes by Chi et al. [55] in

2010 compared the performance of silver-silver chloride wet electrodes to dry electrodes and

non-contact electrodes for EEG and ECG applications. Chi et al. found no clinical dry or

noncontact EEG devices on the market at the time, though there were commercial devices

focusing on entertainment, sleep, and marketing. They concluded that there was a need for

greater emphasis on materials, packaging, and signal processing and systems development.

A different form of dry electrode in a number of sources uses textile sensors or conductive

threads. In 2010, Löfhede et al. presented textile electrodes for use in EEG monitoring [56].

They tested three different types of textile electrodes. The first type was metallic silver thread

knitted in a circular knitting machine into a mesh fabric. The second type was woven from a

nylon substrate coated by pure silver, from Less EMF Inc. The third type was 15% nylon, 30%

conductive fibers, 20% Spandex, and 35% polypropylene from Textronics, Inc. Their results

showed that type I produced poor signal quality with both saline solution and gel. Electrode type

II produced good signal quality using gel (nearly equivalent to standard electrodes) but less well

with saline. Electrode type III showed equally good results with both gel and saline. Löfhede et

al. concluded that soft conductive textile materials can be used in EEG applications, and may be

particularly useful for long-term monitoring.

18

Another approach to gel-free electrodes that has been developed recently is capacitively coupled

active electrodes. These electrodes make use of an extremely high input impedance operational

amplifier (1013

Ω in one study [57]) with a very low input capacitance (1 pF in the same study)

that allows the electrode to acquire EEG signals through hair without a conductive contact to the

copper plate. These electrodes have the advantage of not needing scalp preparation and of not

causing discomfort with sharp probes. The electrodes do show a high susceptibility to motion

artifact, however, though more research is being done in terms of deformable electronics that can

fit more closely to the head.

Researchers at the University of California, San Diego produced significant developments in

capacitive electrodes. In 2007, these researchers presented a non-contact EEG/ECG sensor [58]

with on board electrode to capacitively couple to the skin. They produced the next generation of

the non-contact electrode in 2009 [59], followed by further improvements in 2010 [60]. In 2011,

they reported on an ultra-high impedance front-end for capacitive electrodes [61] that produced

stable frequency response to below 0.05 Hz with extremely low noise.

In 2012, with Chi et al., the researchers developed a mobile brain-computer interface system

using the ADS1298 analog-to-digital conversion chip from Texas Instruments to compare dry

electrodes and capacitive electrodes to a reference wet electrode [62]. Their dry electrodes used

spring-loaded pins to go through the subject’s hair and make contact with the scalp. The

noncontact electrode achieved extremely high input impedances through a custom integrated

circuit design. The system transmitted the data with Bluetooth to a cellular phone for processing.

They found that the dry electrode performed better than the non-contact electrode, but the non-

contact electrodes still had a mean correlation of 0.80 with the wet electrodes, and a similar

signal-to-noise ratio. They also found that while the noncontact electrode showed more signal

degradation and susceptibility to movement artifacts than the dry electrode or the wet electrode,

it could still be successfully utilized for controlled BCI applications.

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2.2 Emotional Valence, Brain Asymmetry, and Mental Illness

2.2.1 A Psychological Perspective on Emotions

Emotions are often categorized in literature on the basis of valence and arousal, often placed on a

polar scale as by Russell in 1980 [63]. Russell suggested that the affective dimensions of

emotion are interrelated in a systematic fashion: that is, emotions have a positive and negative

dimension, and an activation dimension. In the simplest terms, the relationship can be

represented by a spatial model with the affective concepts on the following points of a circle:

pleasure (0o), excitement (45

o), arousal (90

o), distress (135

o), displeasure (180

o), depression

(225o), sleepiness (270

o), and relaxation (315

o). This model was updated by Russell and Barrett

in 1999 [64], where the Y-axis of the circle is labeled Activation – Deactivation and the X-axis is

Unpleasant – Pleasant, and four different emotions are in each quadrant. Russell and Barrett

mention that the pleasure-displeasure dimension is also called “valence, hedonic tone, utility,

good-bad mood, pleasure-pain, approach-avoidance, rewarding-punishing, appetitive-aversive,

positive-negative”.

In 2003, Russell discusses the psychological construction of emotion [65], where he calls core

affect the “neurophysiological state consciously accessible as the simplest raw (nonreflective)

feelings evident in moods and emotions.” Russell describes it as similar to what others call

activation, affect, mood, and most commonly feeling. The raw feeling can at any time be a blend

of two dimensions, pleasure-displease (or valence) and arousal.

Posner, Russell, and Peterson in 2005 examine the circumplex model of affect as a way to

integrate affective neuroscience, cognitive development, and psychopathology [66]. Posner,

Russell, and Peterson propose that in the circumplex model of affect, all affective states come

from interpretations of core neural sensations that are produced by two independent

neurophysiological systems. While most theories of basic emotions suggest that every emotion

is created by a discrete and independent neural system, many findings in behavioral, cognitive

neuroscience, neuroimaging, and developmental studies of affect are more consistent with this

circumplex model.

Most recently, Yik, Russell, and Steiger produced an updated 12-point circumplex model of core

affect to integrate major dimensional models of mood and emotion [67]. Yik, Russell, and

20

Steiger examine emotions from a psychological perspective, and propose a 12-point model that is

plotted around a circle where the vertical dimension remains activation or arousal; the horizontal

dimension is pleasure-displeasure, or valence; and each quadrant is separated into three sections.

The model is displayed in figure 9, below.

Figure 9 - 12-Point Circumplex Model of Affect [67]

Colombetti’s Appraising Valence in 2005 [68]

describes emotional valence as the “positive” or

“negative” character of an emotion. Colombetti’s study explores the uses in the term valence in

psychology and emotion theory, the problems with these uses, and the utility of the notion of

valence. The first use of “valence” in psychological literature was used mainly as a synonym of

“charge”. In different studies, valence is used to describe objects or directions of behavior, while

in others (particularly later studies), it is used more for positive and negative emotions. Most

often, it is now used as “affect valence”, or how good or bad an emotion experience feels.

Another way to rate emotions is the SAM [69], which is used to rate affective emotional

dimensions of valence, arousal, and dominance. The SAM is used to measure emotional

responses to pictures, sounds, advertisements, painful stimuli, and more. It has also been used

with children, anxiety patients, analogue phobics, psychopaths, and other clinical populations. A

continuous nine-point scale can be used, and is shown in figure 10, below. The top row is

valence, the second row is arousal, and the third row is dominance. It has also become a useful

21

and easy to implement tool for measuring responses in marketing and advertising research as it is

usable in cross-cultural contexts [70].

Figure 10 - Self-Assessment Manikin: Valence, Arousal, Dominance [70]

2.2.2 A Neurological Perspective on Emotional Valence

A number of different approaches are used to calculate emotional valence. One of the more

common tools is the Valence Hypothesis, which states that there is “hemispheric specialization

for positive and negative emotions.” The valence hypothesis holds that the left hemisphere of

the brain is dominant for processing positive emotions while the right hemisphere is dominant

for processing negative emotions [71]. Furthermore, anatomical studies show that emotions are

processed primarily in the pre-frontal cortex with high asymmetry, though this may vary due to

varying underlying structures. The frontal lobe regulates voluntary movement, consciousness,

emotional response and more. Observed problems in the frontal lobe include inability to focus

on task, mood changes, change in personality, and changes in social behavior [72].

A major article discussing brain asymmetry as it relates to emotions is Davidson from 1992 [73],

which assigns primary roles in approach and withdrawal behaviours to the left and right frontal

and anterior temporal regions of the brain. Individual differences in emotional reactivity are

associated with stable differences in baseline asymmetry measurements in the anterior regions.

Davidson associates increased activation or decreased alpha activity in the left frontal region (F3

electrode, for example) with happy or positive emotions, and increased activation in the right

22

frontal region (F4 electrode, for example) with unhappy or disgust emotions. The individual

differences in asymmetry patterns are found to be stable over time and can predict features such

as an individual’s dispositional emotional profile, emotional reactivity and mood.

In Davidson and Irwin’s 1999 review [74], they look at the functional neuroanatomy of

emotions. Davidson and Irwin emphasize the prefrontal cortex and the amygdala as key

components of emotional circuitry. In figure 11, below, the brain is shown with the dorsolateral

region highlighted in blue, the orbitofrontal region in green, and the ventromedial region in red,

which are all sectors of the prefrontal cortex. In the ventral view on the right, the amygdalae are

identified by the yellow arrows.

Figure 11 - Sectors of the prefrontal cortex: lateral view (left), ventral view (right) [74]

Davidson and Irwin explain that many studies show that the left anterior areas are activated by

positive emotions and the right anterior areas are activated by negative emotions, though spatial

resolution is limited in electrophysiological measurements. The regions highlighted in figure 11,

above, are found to be important in human affective response, though very few studies have been

designed to manipulate subcomponents of emotions specifically in order to investigate the

individual areas in more depth. Davidson and Irwin summarize evidence for the lateralization of

emotional valence, particularly that the right prefrontal cortex is responsible for aversive

emotional responses.

Davidson’s article on affective neuroscience and psychophysiology, based on the Presidential

Address to the Society for Psychophysiological Research, was published in 2003 [75]. Here,

23

Davidson emphasized the role of asymmetry in the prefrontal cortex for approach and

withdrawal and the role of the amygdala in direction attention to affectively salient stimuli.

In 2004, Davidson published a commentary on what the prefrontal cortex specifically does in

affect [76]. Davidson discusses that research in frontal EEG asymmetries have made

considerable progress since the first reports 25 years before, but that there has been an absence of

connection with neuroscience research on the structure and function of the prefrontal cortex. He

also states that while most research into emotions have focused on alpha band power,

asymmetrical effects have been seen in other bands including theta, beta and gamma, and that

further investigation into these frequency bands may provide additional information in emotional

processing. Other issues discussed are bilateral variations in the prefrontal cortex, the problem

of inconsistent reference electrode placement.

Colibazzi et al. reported on the neural systems that are responsible for valence and arousal in a

2010 report [77]. The study used fMRI to identify the neural networks subserving valence and

arousal by assessing the associations of blood-oxygen level-dependent response, which is an

indirect index of neural activity, with ratings of valence and arousal during induced emotional

experiences. In particular, Clibazzi et al. found that unpleasant emotional experiences were

associated with increased blood-oxygen intensity in the supplementary motor, anterior

midcingulate, right dorsolateral prefrontal, occipito-temporal, inferior parietal, and cerebellar

cortices. Furthermore, high arousal was associated with increased blood-oxygen intensity in the

left thalamus, globus pallidus, caudate, parahippocampal gyrus, amygdala, premotor cortex, and

cerebellar vermis. Further analysis found that pleasant emotions involved the midbrain, ventral

striatum, and caudate nucleus, which are portions of a reward circuit. The findings suggest that

distinct networks subserve valence and arousal. In particular, arousal is mediated by midline and

medial temporal lobe structures, and valence is mediated by dorsal cortical areas and mesolimbic

pathways. In figure 12, below, statistical maps show where blood-oxygen intensity is associated

with valence values on the left, and arousal ratings on the right.

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Figure 12 - Statistical maps showing blood oxygen signal intensity associated with (left)

valence ratings, and (right) arousal ratings [77]

In 2009, the United States NIH produced a review of measures of emotion [78]. In the review,

they explore self-reported measures of emotion, autonomic measure of emotion, startle response

magnitude, brain states including EEG and neuroimaging such as fMRI and positron emission

tomography (PET), and behaviour. Of most interest is the review of EEG measures of emotion.

The measures of emotion with EEG are typically contrasting activation in large regions of the

brain. This contrast can be anterior vs. posterior in combination with the distinction between

left-sided and right-sided hemispheric activation. It is common to measure valence using alpha

band power (8-12 Hz), which is inversely related to regional activation: that is, increased alpha

band power in one region of the brain suggests less emotional activity. The review focuses

particularly on “frontal asymmetry”, comparing alpha power in the left frontal region of the brain

vs. the right frontal region. Studies in the review found that greater activation in the left side

suggests greater positive emotional response. Further studies showed that frontal EEG

asymmetry may more accurately represent approach (left side) vs. avoidance (right side) rather

than emotional valence.

Brown et al.’s study on wireless emotional valence detection [20] describes emotional arousal as

the “level of physiological activation in response to a stimulus,” and emotional valence as

“commonly attributed as the psychological appraisal given to the stimulus”. Using an approach

based on the Valence hypothesis, Brown et al. quantify emotional valence based on positive

emotions being processed primarily in the left hemisphere and negative emotions being

processed primarily in the right hemisphere. Their approach uses alpha power ratio features in

25

asymmetrical electrode pairs, particularly F3/F4 and F7/F8. In particular, they calculate

maximum and kurtosis of alpha power ratio and the number of peaks in the alpha power ratio to

create a profile of the dynamics of the alpha left/right ratio during the period of recording

including maximum, kurtosis, and peaks per minute where peaks are two standard deviations

from the mean. The subjects were tested using emotionally affective film clips. Using QDC,

SVM, and KNN classifiers to compare results, they were able to achieve 82% correct emotional

classification with KNN classifiers when viewing films clips.

Using the Valence Hypothesis Ramirez and Vamvakousis created an equation to quantify

emotional valence using EEG electrodes on the prefrontal cortex [79]. Cited research shows that

beta waves are associated with an alert mental state while alpha waves are associated with a

relaxed state or less mental activity. In particular, an increase in alpha activity together with a

decrease in beta activity demonstrates cortical inactivation. Since the prefrontal lobe plays an

important role in emotion regulation, the F3 and F4 electrode positions are mostly used to

measure alpha activity. By comparing hemispheric activation, emotional valence can be

computed by comparing the alpha and beta power in channels F3 (left side) and F4 (right side).

In particular,

Equation 1 - Emotional Valence [79]

Using SVM classification, they were able to classify high vs. low arousal with 77.82% accuracy

and positive vs. negative valence with 80.11% accuracy while collecting responses to 12

different sound stimuli from 6 different subjects.

Schmidt and Trainor presented results of frontal brain activity distinguishing valence and

intensity of emotions elicited by music in 2001 [80]. Schmidt and Trainor recorded EEG during

60-second musical excerpts from asymmetrical frontal (F3, F4) and parietal (P3, P4) electrode

positions referenced to the Cz position. They saw significantly lower left frontal alpha power vs.

right frontal alpha power during positive emotions and the opposite during negative emotions,

confirming the usual Valence hypothesis. They also found that the overall frontal activity was

directly related to the intensity of the emotion. In particular, there was significantly greater

overall activity in the frontal area as the intensity of the stimuli increased.

26

In 2004, Rusalova and Kostyunina produced extensive spectral correlation studies on emotional

states related to different EEG electrode positions [81]. In their study, emotional responses were

measured by 14 electrodes and activity between electrode pairs was correlated for baseline, anger

and fear states. Fear conditions showed an extensive distribution of intracortical connections in

the δ range including the frontal, central, temporal, parietal, and occipital areas. Rusalova and

Kostyunina found that the anger emotion produced changes in the pattern of the distribution of

intracortical connections in the α-frequency range, with strong connections formed in the frontal

areas. An even larger number of connections was found in the high β (20-30 Hz) range.

Winkler et al. classified emotional valence using frontal EEG asymmetry in a 2010 article [82],

but found that affective pictures did not reliably cause changes in frontal asymmetry. Winkler et

al. were unable to replicate predicted asymmetry averaging within subjects or on a single trial

basis, and only found better-than-chance performance in two of nine subjects. They suggest that

stronger emotional elicitation might be necessary as some of the brain activity related to

emotions is created by deeper brain structures, so images alone may not produce sufficient

emotional engagement.

In 2011, Petrantonakis and Hadjileontidis proposed a novel emotion elicitation index using

frontal brain asymmetry [83]. Petrantonakis and Hadjileontidis created an AsI by analyzing

information from FP1, FP2 and F3/F4 sites. To evaluate the asymmetry index, they created a

classification process using two feature-vector extraction techniques and a SVM classifier in the

valence/arousal space. They reported up to 62.58% classification in user independent cases and

94.40% classification in the user-dependent case.

In 2012, Schuster et al. presented findings on EEG-based valence recognition and the influence

of individual specificity [84]. Schuster et al. recorded event-related potentials from subjects

stimulated by pictures from the International Affective Picture System. They found support

vector machine classifications based on intraindividual data showed significantly higher

classification rates than global ones, showing that classification accuracy can be boosted with

individual specific settings.

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2.2.3 Emotional Valence and Asymmetry Related to Mental Illnesses

A variety of literature on emotional valence, emotional processing, and cerebral asymmetry in

different mental illnesses exists. In particular, this section will include examples of depression,

PTSD, schizophrenia, bipolar disorder, dementia, and other studies of interest.

In 2001, Knott et al. published a report on EEG characteristics in male depression [85]. In

particular, they focused on EEG power, frequency, asymmetry and coherence. They compared

resting EEG from 70 male, unmedicated, unipolar major depressive disorder outpatients and 23

normal control male subjects. The patient population showed greater overall relative beta power

and greater absolute beta power at bilateral anterior regions with a faster mean total spectrum

frequency. Knot et al. also noted inter-hemispheric alpha power asymmetry differences, as

controls showed relatively lower left hemispheric activation as well as widespread reduction of

delta, theta, alpha and beta coherence indices. Patients showed intra-hemispheric theta power

asymmetry reduction, and right hemisphere dominant beta power asymmetry. Using

discriminant analysis, 91.3% of both patients and controls were correctly classified.

Mathersul et al. investigated EEG alpha asymmetry in depression and anxiety in a 2008 study

[86]. Mathersul et al. categorized 428 participants on the basis of both negative mood and alpha

EEG to investigate the relationships in nonclinical depression or anxiety and lateralized

frontal/parietotemporal activity. They found that anxious participants showed greater right

frontal lateralization, depressed or comorbid participants showed symmetrical frontal activity,

and healthy control subjects showed increased left frontal lateralization. They also saw right

frontal lateralization in anxious arousal participants, and left frontal and right parietotemporal

lateralization in anxious apprehension. Finally, they found a bilateral increase in frontal and

increased right parietotemporal activity in depressed or comorbid participants. These findings

supported the valence-arousal predictions for frontal but not posterior regions.

An fMRI study of depressed patients by Herwig et al. in 2010 [87] studied the effect of

pessimism-related emotion processing in major depression. Herwig et al. cued depressed

patients and healthy control subjects to expect and then perceive pictures of known emotional

valences as well as stimuli of unknown valence in order to compare the brain activation in the

unknown expectations with the known expectations. They found that the brain activation in

depressed patients with unknown expectation was comparable to known negative expectation,

28

but not to known positive expectation, in contrast with healthy control subjects. This finding

was in line with the assumption that a cognitive feature of depression is the expectation of a

negative outcome. There was also increased activity in the dorsolateral prefrontal cortex and

medial prefrontal cortex correlated with the grade of depression, and also differed significantly

from the activity in healthy control subjects.

Kemp et al’s study in 2010 [88] compared resting EEG data in patients with MDD and PTSD

relative to healthy control subjects. The purpose of the study was to determine the specificity of

brain laterality in both disorders. Kemp et al. found reduced left-frontal activity and an overall

increase in alpha power in MDD. They also saw positive correlation between the severity of

PTSD and right-frontal lateralization and greater activity in the right-parietotemporal region in

PTSD relative to MDD. The increased alpha power in MDD was unexpected, as was the right-

frontal lateralization in PTSD. Their findings suggested that activation in the right-

parietotemporal region in particular may distinguish between the disorders in resting EEG.

From a treatment perspective, Rosenblau et al.’s 2012 study [89] investigated the effects of

successful antidepressant therapy on major depressive disorder. Rosenblau et al. used fMRI to

study activation during the presentation and anticipation of negative stimuli on a group of MDD

patients and healthy control subjects before and after an eight-week antidepressant treatment.

The patient group had greater amygdala activation during negative anticipation and greater

prefrontal activation without anticipation. Post-treatment, the amygdala and prefrontal activation

was significantly decreased in the patient population relative to the controls. The results indicate

that dysfunctions in emotional regulation mechanisms are present in depressed patients, but that

these dysfunctions may be at least partially reversible.

In 2013, Groenewold et al. reviewed fMRI studies of depression to determine whether emotional

valence modulates any of the abnormalities in brain function [90]. In a systematic literature

review, they found that opposing effects were seen in the amygdala, striatum, parahippocampal,

cerebellar, fusiform and anterior cingulate cortex. In particular, depressed subjects showed

hyperactivation to negative stimuli and hypoactivation for positive stimuli. Anterior cingulate

activity varied with facial vs. non-facial stimuli as well as to emotional valence. There was

reduced left dorsolateral prefrontal activity with negative stimuli and increased activity in the

29

orbitofrontal cortex with positive stimuli. Groenewold et al. determined that emotional valence

does moderate neural abnormalities in depression.

Focusing on post-traumatic stress disorder, Shankman et al. studied resting EEG asymmetry in

2008 by comparing a PTSD group and a control group [91]. Against their expectations,

Shankman et al. did not find significant differences in resting EEG asymmetry between the two

groups, nor did they relate specific aspects of PTSD to hemispheric differences. They concluded

that PTSD may be associated with different processes than conditions normally studied with

relation to brain asymmetry. Their study may have been limited by taking only one reading per

subject, and possibly by some of the PTSD group taking psychiatric medications.

In a 1995 study, Grosh et al. studied abnormal laterality in schizophrenia [92] by looking at both

schizophrenic patients and their parents. The study paired neutral words with words of positive

emotional valence in one test and negative emotional valence in another test. The results of the

study suggested that schizophrenics and their parents had similar abnormalities in hemispheric

activation only at baseline, but negative emotional stimuli caused a greater decrease in left

hemisphere activation only in the patient group. Grosh et al. suggest that dysfunction of the left

hemisphere may be a marker of vulnerability to schizophrenia, and the severity of the

dysfunction may distinguish those who do develop schizophrenia.

Burbridge and Barch studied the impact of emotional valence on reference disturbance inpatients

schizophrenia in a 2002 report [93]. The study found that schizophrenic patients had more

reference errors in their language for affectively negative topics vs. neutral topics. There is a

possibility that negative valence increases arousal levels, which can negatively impact the clarity

of language production, which is consistent with prior research on increased arousal negatively

influencing cognitive function. Burbridge and Barch suggest that further studies should include

more measures of affective arousal including skin conductance and heart rate. In 2007, Phillips

et al. [94] produced a similar study that also showed impairment in speech referencing of

schizophrenic patients during high arousal conditions. Phillips et al. found that patients with

depressive symptoms showed an even higher reactivity to stimuli with negative valence and high

arousal. Their findings demonstrated the importance of considering emotional context and

content in these patients.

30

In 2011, Lepage et al. studied the idea that schizophrenia can cause difficulties in facial

emotional processing [95]. Lepage et al. performed an fMRI study on a group of schizophrenia

patients and a group of healthy controls presented with unhappy, happy and neutral faces. Both

groups were able to rate the emotional valence of the faces similarly, and exhibited increased

brain activity when presented with emotional faces compared to neutral ones in multiple brain

regions. There were differences in specific areas, as the schizophrenia group showed a

correlation between flat affect and activity in the amygdala and bilateral parahippocampal

regions. The healthy group showed more activity in brain regions involved in early visual

processing compared to the patient group.

Pavuluri et al. performed an fMRI study on pediatric bipolar patients in 2008 [96] to determine

how attentional control and affect processing are integrated. A patient group and a healthy

control group were given an emotional valence task matching the colour of affective words to

coloured circles. The patient group showed functional alteration compared to the healthy control

group in affective and cognitive brain circuitry which may contribute to difficulty with affect

regulation and behavioural self-control in patients with pediatric bipolar disorder.

In 2010, Drago et al. [97] studied the intensity of emotional processing in patients with

Alzheimer’s disease. Drago et al. presented a group of patients with Alzheimer’s disease and a

healthy control group a series of emotionally affective pictures and asked them to rate these

pictures on a linear scale from happy to sad, based on how pleasant or unpleasant they found the

image. The patient group scored lower intensities of emotional valence than the control subjects

and had more inconsistency in the valence ratings.

Schiffer et al. studied the hemispheric emotional valence response to auditory evoked potentials

in a 2007 study [98] by comparing a group of healthy control subjects to a group who were

victims of childhood maltreatment. The results showed that 62% of controls and 67% of

maltreated subjects had right negative hemispheric emotional valence with a strong relationship

to the gender of the subject. Schiffer et al. suggest that the laterality of emotional valence may

be an important factor for guiding lateralized treatments such as transcranial magnetic

stimulation.

31

In 2011, Flo et al. studied emotional and physical distress during sleep [99] by examining frontal

EEG alpha asymmetry. Flo et al. found that even during sleep, measurable changes in alpha

symmetry were seen to aversive stimulation, as well as galvanic skin response, and REM sleep.

No frontal beta asymmetry was seen during sleep conditions.

32

Chapter 3. Technical Background

3.1 EEG Background and Specifications

3.1.1 Digital EEG Requirements

While EEG was originally recorded on paper in analog form, the advancements in computer

technology made digital EEG the new standard. Advantages of digital EEG include efficiency,

nondestructive processing, precision, archiving, transmission and comparison, increased

frequency range, reliability, portability, and the use of digital signal processing [100].

Recording digital EEG has a number of requirements for clinical use, including amplifier

specifications, analog-to-digital conversion specifications, and filtering. The OSET [25]

recommends a minimum of 25 electrode inputs including 21 on the scalp, the system reference,

ground, and two extra electrodes, which will be described more fully in the next section. The

input impedance of the amplifier is recommended to be greater than 10 MΩ with a common

mode rejection ratio (CMRR) of greater than 100 dB for each input. Common-mode-rejection

ratio refers to how well the differential amplifier can reflect the difference between inputs [100].

The OSET recommends a minimum sampling rate of 200 Hz, with 256 or more being preferable,

but over 500 Hz is not required for cortical EEG. In terms of vertical resolution, 12 bits or

higher is preferred. In the area of filtering, a wide bandpass of 0.1-100 Hz is suggested with a

notch filter at 50 or 60 Hz, depending on the line power used.

The ACNS guidelines from 2006 [101] modify these recommendations slightly. In particular,

they suggest that the sampling rate should be at least three times higher than the high-frequency

filter setting, for example, 100 Hz for 35-Hz high filter, or 200 Hz for 70 Hz high filter, though

higher rates are preferable. The ACNS recommends a minimum of 11 bits per sample though

prefer 12 bits or more to resolve EEG down to 0.5 µV and up to plus or minus several mV

without clipping. The minimum CMRR is set at 80 dB, though again with a preference for

higher, and noise in the recording of less than 2 µV peak-to-peak from 0.5-100 Hz.

33

3.1.2 Standard Electrode Placement and Electrode Montages

The standard EEG electrode positioning is referred to as the International 10-20 System which

was first published in 1958 [102]. In this system, the subject’s head is measured from nasion to

inion (top of nose to base of skull) and from middle of ear to middle of ear. The main electrode

lines laterally are Fp (frontal pole), F (frontal), C (central), P (parietal), and O (occipital). The

central line is exactly half the distance from nasion to inion, while the frontal pole and occipital

lines are 10% of the distance from the nasion and inion, respectively, and twice this distance, or

20% of the total, separates each of the other lines. This positioning allows the system to be used

on any subject universally.

Since the original definitions, more lines have been added between the original five. The ACNS

Guidelines for Standard Electrode Position Nomenclature [103] suggest a slight modification of

the original 10-20 system as there was an inconsistency in T3/T4 and T5/T6 electrodes. The

standard definitions used now are shown in the figure below.

Figure 13 - Standard 10-20 Electrode Positioning with ACNS Modification [103]

34

The additional lines are AF (anterior frontal), FT and FC (frontotemporal and frontocentral), TP

and CP (temporal-posterior temporal and centroparietal), and PO (parieto-occipital). The

electrodes are evenly spaced still as percentages of the subject’s head size.

The voltages at each of these electrode sites are measured as a voltage difference. Where the

differences are set is referred to as an electrode montage. In their guideline to standard montages

[104], the ACNS discusses three main classes of montage: longitudinal bipolar, transverse

bipolar, and referential. While there are several different types of longitudinal and transverse

bipolar montages, the type used in this thesis and in EEG studies at CAMH is the referential

montage. In this system, a single reference electrode is chosen, and the voltage at each

measurement electrode is recorded as VElectrode - VReference.

In Pivik et al.’s guidelines for EEG [105], the placement of the reference should be as

electrophysiologically silent as possible. The convention is to use a contralateral or unilateral

earlobe or mastoid reference, designated A1/A2 or M1/M2. In the United States NIH’s EEG and

ERP guidelines [6], the commonly suggested options are the tip of the nose, single or linked

mastoid, or single or linked earlobes. In NeuroScan’s 64-channel cap, the reference electrode is

located between the CZ and CPZ electrodes.

In order to minimize electrical noise, a ground or bias electrode is used. The NIH suggests that

this electrode can be placed anywhere, but a forehead or ear location is often used. Teplan’s

Fundamentals of EEG Measurement [106] suggests that a forehead or ear location can be used,

but sometimes a wrist or leg (similar to the right leg drive in ECG) can be used. The NeuroScan

cap places the ground electrode at the AFZ position.

35

3.1.3 EEG Signal Processing

An EEG is originally recorded as a signal of voltage (or power) vs. time. In order to analyze the

signal using the clinical bands described in section 1.1.2 and further in the next section, the

signal must be converted to the frequency domain. This section will discuss the techniques used

to convert a time-domain EEG signal to a frequency domain signal, though the same technique

could be used for any time-domain signal.

In particular, signals can be examined in the frequency domain using Fourier analysis. A

detailed description of Fourier series is not given here, as it is not required for basic signal

analysis, however, it can be found in Signal Processing for Neuroscientists [107] or Discrete-

Time Signal Processing [108], among other resources. The majority of the signal processing is

carried out using MATLAB software, though some of the techniques are later converted to C++

for the ambulatory system.

The primary technique used is the discrete Fourier transform (DFT) [107], which is defined as:

Equation 2 - Discrete Fourier Transform

In this equation, x(n) is the time signal represented by a discrete (or sampled) series, and X(k) is

the discrete Fourier series. is a notational simplification of the exponential value shown in

the first equation, which comes from the continuous Fourier transform. This value is also called

the twiddle factor. The discrete inverse Fourier transform, which returns the Fourier series to a

discrete time series, is defined as:

Equation 3 - Discrete Inverse Fourier Transform

In MATLAB and in many other programming languages, the DFT is optimized using a periodic

twiddle factor and called an FFT and the inverse DFT is called an IFFT. This periodicity is

limited by sampling frequency, FS, where the largest frequency that can be represented is one

half of the sampling frequency, defined as the Nyquist limit [107].

36

The initial output of the FFT is a series of N complex values where the frequency spacing is

given by

. In order to obtain a real value in frequency analysis, this output can be interpreted

as an amplitude spectrum or a power spectrum. The power spectrum is the power at each

frequency, which is obtained by multiplying the FFT output X with its complex conjugate X*.

Equation 4 - Power Spectrum of Signal

This power spectrum is normalized by dividing by the number of data points, N, which ensures

that the energy of the time series equals the sum of elements in the power spectrum, which

comes from Parseval’s theorem [107]. The DFT consists of even (real) and odd (imaginary)

parts. The power spectrum is even, so the part of the spectrum relating to negative frequencies is

identical to the part with positive frequencies. It is therefore common to depict only the first half

of the spectrum, which is up to the Nyquist limit.

The other approach to spectral analysis is the amplitude spectrum, which is used commonly in

this project. The amplitude spectrum corresponds with the amplitude of sinusoidal signals in the

time domain, or the square root of the power.

Equation 5 - Amplitude Spectrum of Signal

In practical terms, the following scripts with example numbers are used to calculate these

elements in MATLAB. The time signal will be represented by x.

Fs = 250; % Sampling rate

L = 1000; % Length of FFT

x % time signal

y = fft(x,L)/L; % create frequency signal

S = y(1:L/2).*conj(y(1:L/2)); % Power spectrum of signal

AS = abs(y(1:L/2)); % Amplitude spectrum

Furthermore, in order to use the clinical EEG bands as defined in section 1.1.2, there are several

different approaches that can be taken [105]. Most commonly, the bands are quantified by

37

summing up the amplitudes within the band or the power components within each band. This

summation is referred to as absolute band power or total amplitude. However, relative amplitude

or power can also be used, where the sum of amplitudes or power in each band is divided by the

total amplitude or power across all bands. This type of summation gives a value in percentage,

which can then be easily compared between different EEG systems.

Finally, when performing spectral analysis, it is common to use windows in the time domain in

order to gain a sense of the change in the amplitude or power spectrum with time. For example,

a single channel of five minutes of EEG data sampled at 250 Hz would contain 75000 data points

in the time domain. One could perform an FFT on the entire signal and have an amplitude

spectrum over five minutes of time. If, however, the user performed different tasks over these

five minutes, it might be useful to view the amplitude spectrum in different segments, or

windows, of time. This view might involve taking an FFT of every 10 seconds of data, or every

2500 points. In some applications, it would be ideal to overlap these windows by some portion

of the window length. In this example, a five second, or 1250 point overlap would provide

spectral analysis of time windows from 0-10s, 5-15s, 10-20s, and so on. The ideal length for

windows will be discussed further in section 3.1.4.

There are a number of different windows that can be used in signal processing, including

Bartlett, Hamming, and Hann windows, but for the purposes of this research, the simple

rectangular window is used, where:

Equation 6 - Definition of Rectangular Data Window

While the rectangular window can come with a cost of ripple effects in the spectrum [107], the

limits of the clinical EEG bands still make it a useful tool, particularly if the signal is not being

converted back to the time domain.

38

3.1.4 Expanding on Clinical EEG Bands

The standard EEG bands are explained briefly in section 1.1.2, but there are more details with

regards to amplitudes, areas of occurrence, subject condition, and reliability of quantitative

features. Detailed explanations of different EEG waveforms are provided in several reference

texts, including Standard Electroencephalography in Clinical Psychiatry [109] and

Niedermayer’s Electroencephalography [110]. An expansion of the clinical bands taken and

slightly modified from Standard Electroencephalography is shown in the table below. Note that

neither source mentions gamma rhythms beyond a cursory explanation.

Table 3 - Information on EEG Bands [109]

Band

Name

Frequency

Range

Amplitude

Range

Main area Condition

Delta 1-3.5 Hz 50-350 µV Variable Drowsiness, deep sleep, hyperventilation,

infancy & childhood

Theta 4-7 Hz 10-150 µV Variable Drowsiness, deep sleep, hyperventilation,

infancy & childhood

Alpha 8-12 Hz 20-100 µV Posterior Relaxed wakefulness, eyes closed

Beta 12.5-28 Hz 10-30 µV Frontal or

Diffuse

Increase during cognitive efforts,

drowsiness and light sleep

Gamma 30-50 Hz Low No discussion

As mentioned in the previous section, the power in each band can be measured both absolutely

and relatively, though measuring relative power may reduce the ability to interpret variations in

bands, so absolute power measures are recommended for clinical use and research [105].

In 2007, Gudmundsson et al. produced a report on the reliability of different quantitative EEG

features [111]. Among their findings, they determined that the highest reliability was obtained

with an average montage, where the “reference” electrode is an average of all electrodes, and

that the EEG signals became increasingly reliable with epochs or windows up to 40 seconds in

length, with longer epochs not providing significant improvement. Gudmundsson et al. found

that quantitative EEG features were most reliable in power spectral parameters. Among the EEG

bands, they found that the theta, alpha and beta bands were most reliable in repeated readings,

while delta and gamma bands were somewhat less reliable.

39

3.2 Statistical Analysis and Correlation Methods

In Chapter 4 of this thesis, as well as sections 6.2 and 7.1, ambulatory EEG acquisition systems

are quantitatively compared to a gold standard system. A common tool for comparing numerical

measurements, which is used particularly in Chapter 4, is Pearson’s correlation [112] [113]. In

particular, Pearson’s correlation provides a correlation coefficient (R or ρ) and p-value, which is

an indicator of the significance of the results. The following table explains the significance of

some correlation coefficients.

Table 4 - Significance of Different Correlation Coefficients [114]

Correlation Coefficient (R or ρ)

Value Meaning

1.0 Complete positive linear relationship

0.7 Strong positive linear relationship

0.5 Average positive linear relationship

0.0 No relationship between data

-0.7 Strong negative linear relationship

-1.0 Complete negative linear relationship

The p-value is the probability of getting a result more extreme than the one that was observed

(the correlation coefficient in this case) if we assume that there is no relationship between the

data (null hypothesis). A significance level can be chosen, usually 0.01 or 0.05, which indicates

that the result would be very unlikely if the data was unrelated [114]. Pearson’s correlation

coefficient is explained in many statistical textbooks including Probability, Statistics and

Random Processes for Electrical Engineering [115] and can be calculated in MATLAB using

the [R,P] = corr(X,Y) command. Other statistical software programs also commonly use

Pearson’s correlation.

It has been suggested in literature that evaluating different observation methods with Pearson’s

correlation coefficient is inappropriate because the data may be linearly similar but have little or

no agreement [116]. Suggested alternative approaches include Lin’s concordance correlation

coefficient and intraclass correlation coefficients (ICC). Pearson’s correlation coefficient

describes the relationship of two variables to a line of best fit. Lin’s coefficient modifies this

approach by also assessing how close the line is to a 45-degree line drawn through the origin if

the two variables are plotted on a scatter diagram. Lin’s coefficient is calculated as:

40

Equation 7 - Lin's Concordance Correlation Coefficient

where r is the estimated Pearson coefficient between n pairs of results (xi, yi), and are the

sample means of x and y, and and

are

times the estimated variance of x and y,

respectively.

An ICC is an index of reliability that is used to measure reproducibility and repeatability which

is very similar to Lin’s coefficient. It is similar to calculating Pearson’s correlation between the

data sets, however instead of centering and scaling each group by its own mean and standard

deviation, a combined mean and standard deviation is used. In the simplest terms, the ICC is a

measurement of the proportion of variance that is due to the object of measurement, or target

[117]. An ICC is calculated as the part of the total variance that is due to the differences in

paired measurements obtained by two or more methods [118]. It can be calculated as:

Equation 8 - Intraclass correlation

where is the variance between the methods being compared and

is the error variance.

This variance can be calculated using analysis of variance (ANOVA) tables. McGraw and Wong

[117] discuss different versions of ICC, two of which may be appropriate in comparing two

measurement systems. To compare two sets of scores, a two-way ICC model can be used. One

two-way ICC measures the degree of consistency among measurements, which excludes column

variance, or variance between the particular measurement systems. This variance can be the case

if one of the two systems may consistently measure or rate higher or lower than the other. The

other ICC measures the absolute agreement between measurements, where both measurements

are assumed to come on the same scale with no significant difference in anchor point.

Calculations for ICC are based on this paper, and use a MATLAB function available on the

Mathworks File Exchange [119].

41

Chapter 4. Comparitive Evaluation of an MBAN EEG Platform vs. Clinical Gold Standard [23]

4.1 Introduction

Monitoring EEG in ambulatory environment is becoming more important not only in clinical

domains but also as an extra parameter for various life-style, brain computer interface (BCI), and

entertainment applications. In order to address a wide variety of clinical applications, it is

important to have a system that is miniaturized, wearable, wireless and capable of providing

flexibility and comfort to the user.

The imec group [22] [27] [120] [26] has created an eight-channel ultra-low-power wireless EEG

system that acquires EEG data and wirelessly transmits to a USB-connected receiver. In order to

analyze data quality, it was determined that the ideal method would be to connect a single EEG

cap to both the wireless system and to the gold standard NeuroScan SynAmps system, which is

used extensively in clinical research applications. NeuroScan lists many articles in applied

neuroscience, in research involving MRI/EEG recordings, and in sensory neuroscience in where

NeuroScan equipment is used [121].

There is a lack of data available on the quality of ambulatory MBAN EEG systems compared to

clinical standard systems. NeuroSky, a manufacturer of single-lead dry EEG systems, published

their own white paper [112] in which they gave correlation coefficients for Fourier-transformed

EEG signals from their dry sensor EEG system to the wet electrode Biopac system, which is

used in medical and research applications. Signals were simultaneously recorded from side-by-

side electrodes, and they provided results for a single subject tested with approximately 30

seconds of data, with no correlation coefficient above 0.858 recorded in the frequency domain.

Matthews et al. [113] of QUASAR produced a study comparing novel hybrid EEG electrodes to

conventional wet electrodes in side-by-side testing which produced high levels of correlation

(>99% for seated subjects in the frequency domain).

Neither of these studies, however, tested two systems using the same headgear, which eliminates

any differences resulting from discrepancies in electrode positioning, material, and stimulus

42

effects. In this study, all testing was done simultaneously with the wireless MBAN EEG system

and a gold standard NeuroScan SynAmps system using a standard 64-channel EEG cap.

4.2 Participants

Nine healthy control subjects were tested in the EEG laboratory at the Centre for Addiction and

Mental Health (CAMH) in Toronto, Canada. All subjects passed a screening process that

included collecting their basic medical history to ensure their eligibility as healthy controls. We

excluded subjects with a psychiatric history, as well as any mental health history of first-degree

relatives. All subjects gave their written informed consent and the protocol was approved by

CAMH in accordance with the Declaration of Helsinki. The consent and screening form is

including in Appendix 3.

4.3 Equipment

The eight-channel wireless EEG system developed by imec is shown in figure 14a below, at far

left. The system builds on an EEG Application-Specific Integrated Circuit (ASIC) that achieves

high-performance at low power consumption [27]. The system has a low-noise (62 nV/Hz),

high common mode rejection ration (120dB) and has been optimized for low power

consumption, consuming between 3.3mW and 14mW depending on the mode of operation [120].

The system’s packaging included connectors for EEG DIN cables. In order to evaluate the signal

quality, it was compared to NeuroScan’s SynAmps amplifier system, which can be connected to

a 64-channel EEG cap. The SynAmps connector is shown in the figure 14b below, at middle

left. A 64-channel Quik-Cap from NeuroScan was used, with only eight channels plus reference

and ground being prepared with EEG gel to minimize their impedances. The specific channels

used are shown in figure 2. The Quik-Cap is shown in the figure 14c below, at middle right. A

pin-out board was created so that the 80-pin connector on the Quik-Cap could be connected both

to the SynAmps system and to the imec EEG ASIC. The connector board is shown in the figure

14d below, at far right.

43

Figure 14 - Equipment setup (left to right) a) Imec 8-channel EEG ASIC; b) NeuroScan

SynAmps connection; c) NeuroScan QuikCap 64-channel EEG cap; d) Custom-made 80-pin

connection board

4.4 Testing Method

The subjects completed baseline EEG readings, including 10 minutes of resting EEG (eyes

closed), and 5 minutes of watching an emotionally neutral video clip (eyes open) from Disney’s

“Silly Symphonies” without audio. These steps were completed in a counterbalanced order.

After the baseline readings, the subjects completed N-back working memory tests (N=0, 1, 2) in

random order and counterbalanced. Each of these tests was 13-15 minutes long.

In order to obtain meaningful correlation, simultaneous testing was required. Since only eight

channels could be used by both systems, four pairs of parallel electrodes were chosen for testing;

they are shown in the figure below. Frontal polar (FP1, FP2), anterior frontal (AF3, AF4),

frontal (F5, F6) and central (C3, C4) electrodes were used. Each electrode was prepared with

electrode gel as electrode impedances were lowered to < 5kΏ. Channels were referenced to an

electrode placed posterior to the CZ electrode.

Figure 15 - Electrodes from 10-20 system used for testing

44

4.5 Analysis Method

Acquired data was post-processed using MATLAB. The NeuroScan acquired signals were

sampled at 1000 Hz, and the imec acquired signals were sampled at 1024 Hz. To correct this

discrepancy, a native resampling function in MATLAB was used to down-sample the Imec data

to 1000 Hz to match the NeuroScan sampling rate. Next, the data was aligned using an original

function that checked the correlation between the data sets by moving the first 5 seconds of the

imec data by milliseconds against the NeuroScan data and finding the correct offset. After

matching the offset, the beginning and end of each set was trimmed to make them each even

multiples of five seconds and to remove data that may have been affected by the down-sampling.

After aligning the data and dropping the front and back, there was a total of over 7.5 hours of

data used.

The data was then transformed from time domain to the frequency domain using overlapping

two-second windows using MATLAB’s FFT function at 0.25 Hz resolution. The overlap was

one second, so the time domain data was converted for segments from 0-2 s, 1-3 s, 2-4 s, etc. for

each test. To match clinical EEG use, the frequency domain data from 1-50 Hz was used for

correlation, with 201 total points from the 0.25Hz resolution (i.e. 1.00 Hz, 1.25 Hz, 1.50 Hz, etc).

This frequency domain data was also split into δ (1-3.5Hz), θ (4-7Hz), α (8-12Hz), β (12.5-

28Hz) and γ (30-50Hz) bands. The same analysis was also done for 10-second time windows

with 5-second overlaps (0-10 s, 5-15 s, etc.). A correlation analysis returned the Pearson’s

correlation coefficient (R) and confidence value (P) for each set, and the means and medians of

different subsets were isolated. The results are presented below.

45

4.6 Results

Table 5 - Correlation for 2-second windows: Pearson’s Correlation Coefficient and

Confidence Valuesa

Band

(# of Points)

Pearson’s Coefficient (R) P-value

Mean Median Variance R2>0.5 Mean Median Variance

All (201) 0.9303 0.9757 0.00001 94.68% 0.0018 <1E-50 3.8E-7

Delta (11) 0.8110 0.9528 0.00016 80.62% 0.0148 1.6E-6 3.1E-6

Theta (13) 0.8383 0.9552 0.00011 83.76% 0.0197 1.7E-9 4.7E-6

Alpha (17) 0.8357 0.9612 0.00021 82.87% 0.0205 8.2E-12 1.3E-5

Beta (63) 0.8325 0.9414 0.00024 82.30% 0.0110 7.5E-36 7.3E-6

Gamma (83) 0.7839 0.8768 0.00011 78.07% 0.0121 3.7E-34 4.7E-6

Time (2001) 0.5520 0.6688 0.00015 41.73% 0.0084 <1E-50 3.0E-6

a. 215,880 total sets for correlation from 7.5 hrs. of testing

Table 6 - Correlation for 10-second windows: Pearson’s Correlation Coefficient and

Confidence Values b

Band

(# of Points)

Pearson’s Coefficient (R) P-value

Mean Median Variance R2>0.5 Mean Median Variance

All (201) 0.9570 0.9848 0.00001 97.83% 0.0018 <1E-50 3.8E-7

Delta (11) 0.9070 0.9647 0.00009 93.95% 0.0148 1.6E-6 3.1E-6

Theta (13) 0.9124 0.9834 0.00009 92.84% 0.0197 1.7E-9 4.7E-6

Alpha (17) 0.9064 0.9797 0.00018 91.15% 0.0205 8.2E-12 1.3E-5

Beta (63) 0.8922 0.9637 0.00014 90.10% 0.0110 7.5E-36 7.3E-6

Gamma (83) 0.8467 0.9215 0.00006 86.96% 0.0121 3.7E-34 4.7E-6

Time (2001) 0.5804 0.7063 0.00008 45.27% 0.0084 <1E-50 3.0E-6

b. 42,888 total sets for correlation from 7.5 hrs. of testing

Over the range of clinical EEG bands, very high correlation was seen between the two systems.

The correlation for each of the Delta, Theta, Alpha, Beta and Gamma bands was very high as

well, with significant p-values. The correlation values improved with larger windows,

suggesting that small errors were not as significant depending on the size of window used. It

was noted that the results of all bands together are not an average of the individual bands. This

46

discrepancy is likely due to the algorithm used by the correlation function in MATLAB, which

would be more forgiving to a 201-point data set.

The time domain signal was not as well correlated. This problem may be due in part to electrical

noise, as the NeuroScan system used AC power while the Imec system ran on a DC battery. The

NeuroScan system also excelled at eliminating offset or drift, but these issues were largely

removed by conversion to the frequency domain.

4.7 Conclusion

While the imec system was susceptible to some noise in the time domain, its frequency domain

information compared favourably to the gold standard NeuroScan system. In particular, for the

1-50 Hz range, if nearly 95% of values had a coefficient of determination (R2) above 0.5, then in

a 60-second sample where moving 2-second windows were compared, approximately 57 seconds

of the data are well correlated. For clinical EEG that is used for evaluation of emotional state,

this result would provide more than sufficient information. By reanalyzing the data with 10-

second windows, the results were improved across all bands. It is possible that changing the

time window further would improve results, as small errors and noise would be reduced further

with longer windows. Depending on the application of the system, a large enough window

would provide near-perfect results. In the future, this testing will be used to validate a full

wireless system for ambulatory monitoring of subjects with mental illness. Results indicate that

a fully ambulatory EEG system has comparable fidelity to clinical gold standard system.

47

Chapter 5. System Development

5.1 Hardware Development

5.1.1 Acquisition System

In the early stages of this project, several commercial and research systems were tested,

including the Emotiv Epoc, imec’s eight-channel wireless EEG ASIC and the Enobio EEG

system. The main problem with these systems was a lack of direct compatibility with

smartphones. In particular, none of them used Bluetooth or Bluetooth Low Energy, though the

newest iteration of the Enobio system does use Bluetooth communication. Due to this limitation,

it was decided that a new custom-made system should be built with chosen specifications. The

design and build of the system was led by technologist Kevin Tallevi. I provided requirements

for the system and helped test at each step of development. A 3D representation of the wireless

acquisition system is shown below, in figure 16. The major components are discussed below.

Figure 16 - 3D Representation of Wireless EEG Acquisition System

In order to perform straightforward EEG signal acquisition, the system was based around Texas

Instruments’ ADS1299 analog front-end chip [122]. The ADS1299 is a low-noise, 8-channel,

24-bit analog front-end chip designed for biopotential measurements. The chip is designed for

EEG measurements with very low input-referred noise of 1.0 µVPP and low 5 mW/channel

48

power requirements. It uses an input bias current of 300 pA, and has a common-mode rejection

ratio of -110dB, can provide a programmable gain between 1 and 24 and digitizes the analog

data at rates between 250 Hz and 16 kHz. The maximum input voltage is +4.5V (VREF)/gain.

The ADS1299 also has a built-in bias drive amplifier, lead-off detection, and test signals, as well

as internal or external reference signals. A functional block diagram of the ADS1299 is shown

in figure 17, below. The channel inputs can be bipolar or single-ended with a common

reference. For this system, a common reference is used, which is the format used by many other

EEG systems including the NeuroScan EEG system and the imec wireless system.

Figure 17 - Functional Block Diagram of ADS1299 [122]

In Appendix 1, the register settings used for the final system is provided. In particular, the

programmable gain is set to 12 for each channel, the reference and bias buffers are enabled, the

data rate is set to 250 Hz, and the single reference option is chosen.

Next, the wireless communication portion of the acquisition system is provided by the Texas

Instruments’ CC2541 Bluetooth Low Energy chip [123]. This chip is Bluetooth Low Energy

compliant and supports 250 kbps-2Mbps data rates with programmable output power of up to 0

dBm.

49

The power is provided by a 3.7V 500 mAh lithium battery, which is isolated with the Analog

Devices ADuM6201 5 kV isolator [124] to protect the circuit from ground when the charging

adapter is plugged in and isolates the patient from the power of the circuit. A 3V regulator is

used to provide power to the Bluetooth module. The 3.7V from the battery is also stepped up to

5V, and then split into +2.5V bipolar supply to power the ADS1299.

5.1.2 Capacitive Electrodes

After researching dry and capacitive electrodes as documented in section 2.1.2, an active

capacitive electrode was designed for EEG measurements. Kevin Tallevi designed and built the

electrode. I provided requirements and assisted with testing. The design is shown in figure 18,

below. The electrode was composed of four layers. The top layer contained the electronic

components, particularly the instrumentation amplifier as well as the ribbon connector; the

second layer contained a ground plane; the third layer was the bias layer of the electrode; and the

bottom layer was an insulated metal plane that acted as the electrode surface.

Figure 18 - Design of Active Capacitive Electrode

Unfortunately, when the actual electrode was built, it was unable to acquire small enough signals

to be used for EEG purposes. When connected to a signal generator, it was able to resolve

signals down at the 1 mV range, but EEG signals are typically in the 10-100µV range. When it

was connected to a 10-100 µV sine wave, there was no response, and there was also no response

when it was connected to a live test subject. As noted by Chi et al. [62], there is significant

attention required to the circuit design using custom components in order to acquire an

acceptable EEG signal, and there was not sufficient time or resources to fulfill this particular

50

portion of this project. Due to these limitations, the final proof-of-concept system uses

conventional wet electrodes.

5.1.3 Wireless Cap and Electrode Placement

In Brown et al.’s study [20], they began with eight electrodes at Fp1, Fp2 F3, F4, F7, F8, C3 and

C4. However, when data was analyzed, Fp1 and Fp2 were removed due to significant EOG

artifacts, and C3 and C4 were removed due to lack of emotional processing in this part of the

brain. As other studies including Ramirez and Vamvakousis [79] used F3 and F4 in particular,

and general consensus focuses on frontal asymmetry, the F3, F4, F7 and F8 positions were

chosen. Keeping the headset to four electrodes also helps to minimize the data rate and improve

the performance of the wireless transmission. The electrode placement is highlighted below in

figure 19. The signal electrodes are in green, and reference and ground electrodes are in blue.

As discussed in section 3.1.2, standard placement for ground electrodes varies, but forehead is

often used, and the NeuroScan cap uses the AFz position. To simplify the design, an Fz position

was chosen for the ground electrode so that a single line between the four signal electrodes and

the ground could be created. The common single earlobe/mastoid position was chosen for the

reference electrode (both sides are highlighted).

Figure 19 - Electrode Placement for EEG Cap

51

Images of the headset prototype are shown in figure 20, below. Kevin Armour assisted with

designing and building this headset prototype.

Figure 20 - Images of Headset Prototype

5.2 Software and Signal Processing Development

To create an ambulatory system, the BlackBerry Z10 smartphone was used for signal acquisition

and basic analysis. The Z10 uses the new BlackBerry 10 operating system, and has 2 GB of

RAM, 16 GB of internal storage, expandable MicroSD storage, 4.2 inch touchscreen, and a

Qualcomm Snapdragon S4 processor with 1.5 GHz dual-core CPUs which allows for multi-

threaded processes. The Z10 has USB 2.0 for charging and data synchronization, and is

Bluetooth 4.0 Low Energy (BTLE) compatible [125].

BlackBerry 10 applications can be written in C++, so the initial signal processing code was

written in Visual Studio and then migrated to BlackBerry Native SDK with Cascades for User

Interface components. Initially, I wrote and tested the signal processing code with CSV files of

data acquired during lab testing sessions. The data was windowed and transformed to the

frequency domain using the open-source Kiss FFT source code [126]. The size of the window

was easily adjustable using minimal variable changes, and was set generally at 5000 samples, or

20 seconds. These windows were used for calculating emotional valence using the simple

formula Valence = αF4 – αF3. The valence value was scaled by a multiplication factor so that the

graphed value would be on a similar scale to the self-reported emotion ratings.

The BlackBerry application was fully written by programmer John Li. John took the signal

processing code and translated it to the Cascades framework to be able to visually represent the

data and to add the user annotation portion of the software. The application was set up so that

52

the user could start an acquisition session when the headgear was applied, and that emotional

events could be annotated with a score ranging from -4 to + 4, using the same Self-Assessment

Manikin used in section 6.4 with a normalized scoring scale. These emotional events can be

roughly time-stamped from a menu that allows people to choose “When did this happen?”.

The data provided by the ADS1299 came in a 3-byte signed two’s complement format that had

to be translated to voltage. The least significant bit (LSB) is weighted as (VREF/Gain) / (2(Nbits-1)

)

= (4.5/12) / (223

) = 4.47035 x 10-8

. The data output from the ADS 1299 is converted to voltage in

the following form:

Equation 9 - Voltage Conversion Formula (24-bit)

For the purposes of Bluetooth data streaming, 16 bits were used. The calculation above remains

similar except that the last (least significant) byte is dropped, and then the multiplications are

based on 16 rather than 24 bits.

In the figure below, screenshots of the three main parts of the application are shown. On the left

is the device pairing screen, where the EEG device is selected and data streaming can begin. In

the middle is a graph of the 4 channels in near real-time and where the option to log the data is

selected. On the right is the emotional self-assessment screen, where the user grades his or her

emotion, adds a comment if required, and also adds a time-stamp.

53

Figure 21 - Screenshots of EEG App on BlackBerry 10 - Bluetooth Device Selection (left);

Graph of time signals (centre); Emotional Self-Assessment (right)

54

Chapter 6. Testing and Validation

6.1 Participants

The testing of the ADS1299 demonstration kit was completed with two healthy control subjects

(one male, one female). The testing of emotional valence calculations using the ADS1299

demonstration kit was completed with four healthy control subjects (two males, two females).

The full mobile system was tested with five healthy control subjects (two males, three females).

All of these tests were carried out at CAMH TMS-EEG labs. All subjects were between the ages

of 20 and30

The subjects were required to fill out a screening form in order as part of the REB approval

obtained by CAMH. The screening form is the same form that subjects in the comparative

evaluation of imec’s wireless EEG system in Chapter 4. This screening included collecting their

basic medical history. Subjects with a psychiatric history or a significant brain injury were

excluded, as well as those with any mental health history of first-degree relatives. All subjects

gave their written informed consent and the protocol was approved by CAMH in accordance

with the Declaration of Helsinki. The consent and screening form is included in Appendix 3.

6.2 Testing ADS1299 Demonstration Kit

In order to verify the performance of the Texas Instruments’ (TI) ADS1299, a simultaneous

testing protocol, similar to the one completed in Chapter 4, was performed with the NeuroScan

SynAmps2 system. As in Chapter 4, the subjects completed baseline EEG readings. In

particular, these readings included 10 minutes of resting EEG (eyes closed), and 5 minutes of

watching an emotionally neutral video clip (eyes open) from Disney’s “Silly Symphonies”

without audio. After the baseline readings, the subjects completed N-back working memory

tests (N=0, 1) in random order. Each of these tests was 13-15 minutes long. For one of the two

subjects, the 0-back condition data was lost due to technical difficulties.

In order to calculate the correlation, simultaneous signal acquisition was performed. For the first

subject, eight EEG channels were used, as in Chapter 4, specifically frontal polar (FP1, FP2),

anterior frontal (AF3, AF4), frontal (F5, F6) and central (C3, C4) positions. For the second

subject, new electrode positions had been chosen for the proof-of-concept system, as outlined in

55

section 5.1.3. These electrodes were all in frontal positions (F3, F4, F7 and F8). Each electrode

was prepared with electrode gel as electrode impedances were lowered to < 5kΏ. Channels were

referenced to an electrode placed posterior to the CZ electrode. The NeuroScan Quik-Cap was

connected to a pin-out board that split the connection to the NeuroScan SynAmps amplifier and

to the ADS1299 Performance Demonstration Kit.

The NeuroScan data was sampled at 1000 Hz, and the TI data was sampled at 250 Hz. In order

to align the data laterally, two tests were performed, with the NeuroScan data downsampled in

MATLAB to 250 Hz and the TI data upsampled to 1000 Hz. To align the data vertically, the TI

data was centred at 0 by subtracting all data points in a single channel from the mean of that

channel, and multiplying them by a scaling factor of 1,000,000 to qualitatively match the

amplitude of the NeuroScan data. After the data was aligned and scaled, the following steps

were performed to analyze the fidelity of the ADS1299 compared to the NeuroScan system:

1. Both data sets were separated into 4-second, non-overlapping windows (1000 samples for

TI data, 4000 samples for NeuroScan) and 20-second, non-overlapping windows (5000

samples for TI, 20000 samples for NeuroScan). The number of data sets is summarized

at the end of this section in table 7. The subsequent steps were done for both window

lengths and on individual channels.

2. The windows were converted to the frequency spectrum with a 1000-point FFT for TI

data and a 5000-point FFT for NeuroScan data, which corresponded to a 0.25 Hz

resolution for both systems.

3. The amplitude spectrum and power spectrum of each window was calculated for the

range of 1-50Hz.

4. The absolute and relative powers of each EEG band were calculated from the power

spectrum data.

5. For both the full amplitude spectrum (1-50Hz) and full power spectrum (1-50Hz),

Pearson’s correlation coefficient and confidence value, Lin’s correlation coefficient, and

ICC’s for consistency and absolute agreement were calculated. Each of these values was

correlated for the spectrums from each time window. Each correlation was done on 197

points (0.25 Hz resolution).

56

6. These correlations were repeated for the alpha and beta range combined (8-28.5 Hz), as

these ranges are of primary interest for calculating emotional valence. Each correlation

was done on 81 points.

Table 7 - Number of Segments Used for Correlation Analysis

Subject Test Time 4-second segments 20-second segments

Subject 1

(8 channels)

Eyes Open 5:10 (310 s) 616 128

Eyes Closed 10:10 (610 s) 1216 248

0-Back 14:00 (840 s) 1680 336

1-Back 15:00 (900 s) 1800 360

Subject 2

(4 channels)

Eyes Open 5:10 (310 s) 308 64

Eyes Closed 10:10 (610 s) 608 124

1-Back 15:00 (900 s) 900 180

Total 74:40 (4480 s) 7128 1440

The results are presented in section 7.1. Where means, standard deviations, minima, maxima

and percentages are used, they refer to the totals from the table above. For the sake of

comparison, all correlations were also repeated using the unprocessed data from the ADS1299.

6.3 Testing Electrodes

As outlined in section 5.1.2, a prototype of an active capacitive electrode design was ordered and

built. It was initially tested by hooking it up to a signal generator that was able to create sine

waves with amplitudes as low as 10 µV and in the 1-50Hz frequency range. Unfortunately, the

prototype was not able to read signals below 1 mV, which suggested that it would be unable to

acquire EEG signals. To ensure that the result was correct, it was placed on the forehead of test

subject and an exaggerated blink test was attempted, but no result was seen.

Next, conventional gel electrodes were tested. First, basic blink tests were attempted which

produced a clean result. Next, a prototype headset was made with gel electrodes, and they were

attached the NeuroScan SynAmps amplifier, and the impedance was successfully lowered to

below 5 kΩ, which is ideal for clinical EEG. This test determined that the electrodes were

usable.

57

6.4 Emotional Valence Testing Protocol

The subjects completed baseline EEG readings, including 5 minutes of watching an emotionally

neutral video clip (eyes open) from Disney’s “Silly Symphonies” without audio and 5 minutes of

resting EEG (eyes closed), at the end of which a chime sounded to alert the subject to open his or

her eyes for the next section.

Next, a series of 50 emotionally stimulating images was presented from the IAPS [21]. For each

image, the following timing sequence occurred: first, 4 seconds of blank screen, followed by

countdown slides from 4 to 1 lasting 1 second each. Then the emotional image was presented for

4 seconds, and then the subject was given 8 seconds to mark down his or her emotional rating of

the image on the Pleasure portion of the SAM by Bradley and Lang [69], a version of which is

shown in figure 22, below. This SAM is a modified version of the protocol used by

Petrantonakis and Hadjileontiadis [83]. Images were projected in groups in Brown et al. [20],

but time was not provided for the subjects to self-assess the valence after each image or video.

The full SAM also contains figures for arousal and dominance, but these were not deemed

necessary for this exercise. Each picture segment lasted for 20 seconds. A block figure of the

experimental protocol is shown in figure 23, below.

Figure 22 - Self-Assessment Manikin (Pleasure) [70]

Blank

Screen

Emotionally

Neutral Video

Instructions to

close eyes

Eyes Closed Instructions to

open eyes

Emotional

Pictures

Blank

Screen

0:08 5:00 0:04 5:00 0:04 16:40 0:04

Blank Screen 4 3 2 1 Picture Presentation Instruction to rate image

0:04 0:01 0:01 0:01 0:01 0:04 0:08

Figure 23 - Emotional Valence Experimental Protocol:

a) Overall protocol (top); b) Breakdown for each image (bottom)

58

Pictures from the IAPS database were chosen and grouped into five classes based on scores

reported by IAPS testers. This approach is similar to the protocol used by Brown et al. in the

image section [20], Petrantonakis and Hadjileontiadis [83], and others. Valence and arousal

were each rated on a scale of 1-9, with 5 considered average. These groups were, in order:

low valence (all below 4.0, average 3.38), low arousal (all below 4.0, average 3.81)

low valence (all below 4.0, average 2.57), high arousal (all above 6.0, average 6.40)

average valence (all between 4.5-5.5, average 5.00), average arousal (all between 4.5-5.5,

average 4.93)

high valence (all above 6.0, average 7.21), low arousal (all below 4.0, average 3.37)

high valence (all above 6.0, average 7.50), high arousal (all above 6.0, average 6.29)

A scatter plot of the emotional valence vs. emotional arousal of the emotional pictures is shown

in figure 24, below. A detailed breakdown of the images used is included in Appendix 2.

Figure 24 - Emotional Valence vs. Emotional Arousal Scatterplot of IAPS Images

For the first four subjects, data was acquired using the ADS1299 demonstration kit. Further

testing of emotional valence was done during the full mobile system test in section 6.5. Signal

electrodes were placed at F3, F4, F7 and F8. The bias electrode was placed at approximately

position Fz, and the reference electrode was placed at the mastoid. The simple prototype headset

pictured in section 5.1.3 was used for this testing. Data was sampled at 250 Hz.

1

3

5

7

9

1 2 3 4 5 6 7 8 9

Emo

tio

nal

Aro

usa

l

Emotional Valence

Emotional Arousal vs Emotional Valence (IAPS Averages)

59

6.5 Emotional Valence Data Analysis

For data analysis, the time data was converted to the frequency domain in 20-second (5000

sample) windows, which matches the length of time given for each emotional stimulus picture.

Different equations for calculating emotional valence were tested, and these are shown in table 8,

below. The variables represent the absolute power in the band at each electrode location. The

basis for these equations was taken from Ramirez and Vamvakousis [79] who calculate

emotional valence using equation 1 in the table below, and Brown et al. [20] who calculate

emotional valence based on equation 4 in the table below. The left hemisphere of the brain, with

electrodes F3 and F7, and the right hemisphere of the brain, with electrodes F4 and F8, are used in

different combinations.

Table 8 - Emotional Valence Equations Tested

Type (Right – Left) Type (Right / Left) - 1

1

2 (

) - 1

3 - 4 / - 1

5 - 6 / - 1

7

8

– 1

9

10 (

) - 1

11 - 12 / - 1

In order to obtain a more complete picture of these emotional valence calculations, this

processing was repeated on the Database for Emotion Analysis using Physiological Signals

(DEAP) [127] which is a set of physiological signals (EEG, skin conductance, EOG, EMG,

temperature, respiration) that were collected while subjects watched emotionally affective video

clips and then provided self-assessment ratings. There were 32 channels of EEG collected and

processed to a sampling rate of 128 Hz with EOG artifacts removed and bandpass filtered from

4.0-45.0 Hz. The data was split into 40 segments, each 63 seconds long, to match the videos

presented to the subjects, and a 512-point FFT was taken for 0.25 Hz resolution. The data had

already been pre-processed when it was acquired.

To test the accuracy of each emotional valence equation, the subjects’ self-assessment ratings of

the emotional stimuli were compared to the calculated emotional valence value after the mean of

the emotional valence ratings from the baseline period was subtracted from each value. In the

case of the DEAP datasets, the mean of the readings in the subject set was used. The self-

60

assessments were split into two categories, neutral-positive and negative, as in one version of

Brown et al.’s protocol [20]. For a simple binary outcome, if the self-assessment rated as 4.5 or

higher and the calculated value was positive or within one variance of 0 (as calculated through

MATLAB or Excel), this was considered a correct classification. Similarly, if the self-

assessment rated as 4.5 or lower, and the calculated value was negative or within one variance of

0 this was also considered a correct calculation. In summary:

IF SA > 4.5 AND EV > [mean(baseline) - variance(baseline)] Correct classification

IF SA < 4.5 AND EV < [mean(baseline) + variance(baseline)] Correct classification

6.6 Testing Full Mobile System

In order to minimize sources of error for the mobile acquisition system and software, the

NeuroScan Quik-cap EEG cap was used for mobile system testing at CAMH. The same

electrode locations were used as in section 6.4, though the Quik-cap reference at the top of the

head was used. The cap was connected to a breakout board so that signals could be collected

both by the wireless acquisition system and the NeuroScan SynAmps system.

The same evaluation testing protocol was used as in section 6.2, however, only 20 second

windows were calculated. The NeuroScan data was downsampled in the NeuroScan software to

250 Hz and FFT lengths were the same for both systems. The same testing protocol was used as

in section 6.4, as the subjects were presented with an emotionally neutral video clip for eyes

open baseline readings, followed by an eyes closed baseline reading, then the emotional stimuli.

The subjects rated each picture in the BlackBerry application with the SAM on a scale of -4 to 4.

Emotional valence was calculated by the BlackBerry application in real-time. Afterward, the

frequency components of the signal were correlated to the NeuroScan data as in section 6.2, and

the emotional valence measurements were analyzed as in section 6.5. In order to verify the real-

time calculations, the data was processed offline and emotional valence measurements were

compared to the participants’ ratings. Due to the packet streaming protocol used with Bluetooth

Low Energy, the transmitted sampling frequency did not exactly match the 250 Hz used by the

ADS1299 chip. The effective sampling frequency of the BlackBerry data was computed for

each subject in MATLAB, and data was resampled in MATLAB to match the NeuroScan data.

61

Chapter 7. Results

7.1 Validation of Performance Demonstration Kit

Table 9 - Pearson's Correlation (r, p) for Amplitude and Power Spectra of 4-second and 20-

second windows with Averaged and Scaled ADS1299 Data

Set Pearson’s Coefficient (r) P-value

Mean Median Variance r2>0.5 Mean Median Variance p<0.01

Amplitude

(4-second) 0.9550 0.9800 0.0039 98.91% 2.4E-14 9.3E-139 4.1E-24 100%

Power

(4-second) 0.9354 0.9801 0.0112 94.98% 5.7E-7 7.3E-139 6.4E-10 100%

Amp: α-β

(4-second) 0.9292 0.9633 0.0097 96.11% 4.4E-4 7.1E-47 1.3E-4 99.76%

Pow: α-β

(4-second) 0.9428 0.9733 0.0085 96.86% 4.2E-4 2.9E-52 1.1E-4 99.68%

Amplitude

(20-second) 0.9533 0.9792 0.0042 98.89% 6.0E-17 4.4E-137 5.2E-30 100%

Power

(20-second) 0.9336 0.9802 0.0116 94.65% 1.8E-6 3.8E-139 2.3E-9 100%

Amp: α-β

(20-second) 0.9298 0.9635 0.0084 96.46% 4.3E-6 5.9E-47 9.4E-9 100%

Pow: α-β

(20-second) 0.9423 0.9724 0.0073 96.88% 2.0E-6 1.0E-51 1.6E-9 100%

Table 10 - Pearson's Correlation (r, p) Repeated with Raw ADS1299 Data

Set Pearson’s Coefficient (r) P-value

Mean Median Variance r2>0.5 Mean Median Variance p<0.01

Amplitude

(4-second) 0.9548 0.9798 0.0039 98.92% 2.4E-14 2.9E-138 4.1E-24 100%

Power

(4-second) 0.9348 0.9799 0.0112 94.95% 5.7E-7 1.7E-138 6.4E-10 100%

Amp: α-β

(4-second) 0.9287 0.9632 0.0098 96.06% 4.4E-4 8.0E-47 1.3E-4 99.76%

Pow: α-β

(4-second) 0.9425 0.9734 0.0086 96.80% 4.2E-4 2.7E-52 1.1E-4 99.68%

Amplitude

(20-second) 0.9526 0.9789 0.0043 98.89% 6.0E-17 1.6E-136 5.2E-30 100%

Power

(20-second) 0.9319 0.9798 0.0119 94.51% 1.8E-6 2.7E-138 2.3E-9 100%

Amp: α-β

(20-second) 0.9288 0.9634 0.0088 96.25% 5.7E-6 6.3E-47 1.2E-8 100%

Pow: α-β

(20-second) 0.9415 0.9723 0.0076 96.67% 2.0E-6 1.2E-51 1.6E-9 100%

62

Table 11 - Lin's Covariance Correlation Coefficient (Rc) for Amplitude and Power Spectra

of 4-second and 20-second windows with Averaged and Scaled ADS1299 Data

Set Mean Median Variance Rc2 > 0.5

Amplitude

(4-second) 0.9456 0.9766 0.0062 97.69%

Power

(4-second) 0.9014 0.9659 0.0215 90.01%

Amp: α-β

(4-second) 0.9186 0.9611 0.0146 94.56%

Pow: α-β

(4-second) 0.9260 0.9688 0.0152 94.88%

Amplitude

(20-second) 0.9438 0.9767 0.0065 97.78%

Power

(20-second) 0.8988 0.9649 0.0218 89.65%

Amp: α-β

(20-second) 0.9197 0.9608 0.0132 94.65%

Pow: α-β

(20-second) 0.9258 0.9679 0.0139 94.65%

Table 12 - Lin's Covariance Correlation Coefficient Repeated with Raw ADS1299 Data

Set Mean Median Variance Rc2 > 0.5

Amplitude

(4-second) 1.2E-6 1.3E-6 1.5E-13 0%

Power

(4-second) 1.9E-12 1.8E-12 1.0E-24 0%

Amp: α-β

(4-second) 5.7E-7 5.6E-7 2.2E-14 0%

Pow: α-β

(4-second) 1.3E-12 1.3E-12 4.6E-25 0%

Amplitude

(20-second) 1.2E-6 1.2E-6 1.8E-13 0%

Power

(20-second) 2.0E-12 1.8E-12 3.1E-24 0%

Amp: α-β

(20-second) 5.7E-7 5.6E-7 2.1E-14 0%

Pow: α-β

(20-second) 1.3E-12 1.3E-12 9.7E-25 0%

63

Table 13 - Intraclass Correlation Coefficients (ICC) for Amplitude and Power Spectra of 4-

second and 20-second windows with Averaged and Scaled ADS1299 Data

Set ICC (C-1) ICC (A-1)

Mean Median Variance R2>0.5 Mean Median Variance R

2<0.5

Amplitude

(4-second) 0.9473 0.9772 0.0057 97.91% 0.9458 0.9767 0.0062 97.71%

Power

(4-second) 0.9024 0.9663 0.0211 90.17% 0.9018 0.9660 0.0214 90.08%

Amp: α-β

(4-second) 0.9261 0.9625 0.0109 95.64% 0.9193 0.9615 0.0145 94.65%

Pow: α-β

(4-second) 0.9312 0.9701 0.0127 95.62% 0.9267 0.9692 0.0150 94.99%

Amplitude

(20-second) 0.9456 0.9771 0.0060 97.92% 0.9441 0.9768 0.0064 97.78%

Power

(20-second) 0.8998 0.9653 0.0214 89.93% 0.8992 0.9650 0.0217 89.79%

Amp: α-β

(20-second) 0.9267 0.9628 0.0097 96.04% 0.9204 0.9613 0.0131 94.79%

Pow: α-β

(20-second) 0.9305 0.9688 0.0118 95.35% 0.9265 0.9682 0.0137 94.86%

Table 14 - Intraclass Correlation Coefficients repeated with Raw ADS1299 Data

Set ICC (C-1) ICC (A-1)

Mean Median Variance R2>0.5 Mean Median Variance R

2<0.5

Amplitude

(4-second) 2.0E-6 2.0E-6 7.0E-14 0% 1.3E-6 1.3E-6 1.5E-13 0%

Power

(4-second) 2.2E-12 2.0E-12 1.2E-24 0% 2.0E-12 1.9E-12 1.1E-24 0%

Amp: α-β

(4-second) 1.9E-6 1.9E-6 4.9E-14 0% 5.8E-7 5.7E-7 2.3E-14 0%

Pow: α-β

(4-second) 2.0E-12 1.9E-12 1.1E-24 0% 1.3E-12 1.3E-12 1.1E-24 0%

Amplitude

(20-second) 2.0E-6 2.0E-6 1.1E-13 0% 1.3E-6 1.3E-6 1.8E-13 0%

Power

(20-second) 2.2E-12 2.0E-12 3.3E-24 0% 2.0E-12 1.8E-12 3.2E-24 0%

Amp: α-β

(20-second) 1.9E-6 1.9E-6 6.8E-14 0% 5.7E-7 5.7E-7 2.2E-14 0%

Pow: α-β

(20-second) 2.0E-12 1.9E-12 2.5E-24 0% 1.3E-12 1.3E-12 9.9E-25 0%

64

A thorough set of correlations were done to compare the ADS1299 data to the NeuroScan data.

The data was separated into 4-second windows and 20-second windows and converted to the

frequency domain and the amplitude spectra and power spectra were calculated. Correlations

were performed for the full 1-50 Hz range and alpha-beta only (8-28.5 Hz) in both amplitude and

power spectra. The correlations were also done for both the raw ADS1299 data and with the

data that was averaged and scaled to match the NeuroScan data.

First, Pearson’s correlation coefficients were calculated, as in Chapter 4. Next, Lin’s covariance

correlation coefficients were calculated, which are a measure of both linear correlation and

agreement. Finally, intraclass correlation coefficients were calculated for both consistency (C-1)

and agreement (A-1).

The Pearson’s correlation coefficients were very high (mean above 0.928 for all sets) for both

averaged and scaled data and raw data, as seen in tables 9 (averaged and scaled) and 10 (raw),

which was expected. For 1-50 Hz frequencies, the amplitude spectrum showed slightly higher

correlation, but for the alpha-beta range, the power spectrum showed slightly higher correlation.

The Lin’s coefficients were very high for averaged and scaled data, shown in table 11, (mean

above 0.898 for all sets) with slightly higher correlation for amplitude spectra in the 1-50 Hz

range and slightly higher correlation for power spectra for alpha-beta frequencies. For the raw

data, shown in table 12, the correlation was extremely poor (mean on the order of 10-6

or less).

The intraclass correlation coefficients were very similar to Lin’s coefficients. The values were

very high for averaged and scaled data, shown in table 13, similar to the Pearson’s coefficients.

For raw data, shown in table 14, the correlation was extremely low.

These differences come primarily from the fact that the NeuroScan system automatically

averages the data so that each channel is centred on zero, and writes the values in microvolts,

while the ADS1299 writes the values in volts and has an offset for each channel. The NeuroScan

system also uses 32-bit data resolution, while the ADS1299 uses 24-bit data resolution, which

may introduce a level of difference as well. When the data is compared on equal footing, the

results are excellent, and are similar to those seen with the comparative evaluation of the imec

system performed in Chapter 4. Of particular note, the power spectra for the alpha-beta

frequency ranges were very well correlated.

65

7.2 Emotional Valence Testing

The results of the emotional valence testing on healthy control subjects at CAMH on the

ADS1299 demonstration kit are presented in the table below. Four subjects were tested on 50

images each. Based on the self-assessments completed by the four subjects, 129 of 200 images

were rated as neutral-positive, with the remaining 71 rating as negative.

Table 15 - Emotional Valence Testing on ADS1299 Demonstration Kit

Equation Positive

(of 129)

Negative

(of 71)

Total

(of 200)

Average

(of 50)

Maximum

(of 50)

1 -

91

(70.54%)

54

(76.06%)

145

(72.5%)

36.25 50

2 - (

) - 1 89

(68.99%)

58

(81.69%)

147

(73.5%)

36.75 50

3 - - 129

(100%)

69

(97.18%)

198

(99.0%)

49.50 50

4 - / - 1 129

(100%)

68

(95.77%)

197

(98.5%)

49.25 50

5 - - 120

(93.02%)

70

(98.59%)

190

(95.0%)

47.50 50

6 - / - 1 109

(93.02%)

71

(100%)

180

(90.0%)

45.00 50

7 -

81

(62.79%)

44

(61.97%)

125

(62.5%)

31.25 40

8 -

– 1 73

(56.59%)

50

(70.42%)

123

(61.5%)

30.75 40

9 -

100

(77.52%)

31

(43.66%)

131

(65.5%)

32.75 39

10 - (

) - 1 94

(72.87%)

36

(50.70%)

130

(65.0%)

32.50 40

11 - - 115

(89.15%)

66

(92.96%)

181

(90.5%)

45.25 50

12 - / - 1 99

(76.74%)

52

(73.24%)

151

(75.5%)

37.75 49

The results of testing the same emotional valence calculations on the DEAP dataset are presented

in the table below. A total of 32 subjects were tested on 40 images each. Based on the self-

66

assessments completed by the 32 subjects, 808 out of 1280 video clips were rated as neutral-

positive, with the remaining 472 rating as negative.

Table 16 - Testing Emotional Valence Equations on DEAP Dataset

Equation Positive

(of 808)

Negative

(of 472)

Total

(of 1280)

Average

(of 40)

Maximum

(of 40)

1 -

608

(75.25%)

366

(77.54%)

974

(76.09%)

30.4 40

2 - (

) - 1 572

(70.79%)

367

(77.75%)

939

(73.36%)

29.3 40

3 - - 808

(100%)

472

(100%)

1280

(100%)

40.0 40

4 - / - 1 558

(69.06%)

353

(74.79%)

911

(71.17%)

28.5 40

5 - - 808

(100%)

472

(100%)

1280

(100%)

40.0 40

6 - / - 1 527

(65.22%)

337

(71.40%)

864

(67.50%)

27.0 40

7 -

608

(75.25%)

346

(73.31%)

954

(74.53%)

29.8 37

8 -

– 1 529

(65.47%)

335

(70.97%)

864

(67.50%)

27.0 40

9 -

642

(79.46%)

373

(79.03%)

1015

(79.30%)

31.7 40

10 - (

) - 1 771

(95.42%)

180

(38.14%)

951

(74.30%)

29.7 39

11 - - 808

(100%)

472

(100%)

1280

(100%)

40.0 40

12 - / - 1 564

(69.80%)

350

(74.15%)

914

(71.41%)

28.6 40

The emotional valence testing showed very good results. By adding in an averaging mechanism

for each individual subject, some equations were able to correctly match the self-assessment on a

positive-neutral vs. negative basis nearly 100% of the time.

67

In particular, the simple - equation correctly identified the valence range of subjects

tested on the ADS1299 performance demonstration kit 99% of the time (198 of 200 ratings), and

100% of the time for DEAP subjects.

Using - , the valence range of subjects tested on the ADS1299 performance

demonstration kit was correctly identified 90.5% of the time, and 100% of the time for DEAP

subjects, which provides a useful secondary measure given the four electrodes being used by the

proof-of-concept system. This secondary measure may be particularly useful if one electrode

malfunctions.

Another alternative is the - equation, which correctly identified

valence in 95.0% of ratings on the ADS1299, and for 100% of DEAP subjects. This equation

makes use of the alpha-band power in all four electrodes.

In the simplest terms, these results show that positive and negative emotional valence can be

effectively classified for different subjects using a baseline average. While the equation

proposed by Ramirez and Vamvakousis [79] did not perform as well as those above, the

equations that did perform best were variations of the approach taken by Brown et al. [20],

focusing on power in the alpha band only. The difference between the two hemispheres

produced better results than the ratio between the two hemispheres.

68

7.3 Proof-of-Concept Data Validation Results

Table 17 - Pearson's Correlation for Subject 1 on Proof of Concept System (Averaged and

Scaled Data) – Effective Sampling Rate Calculated as 234 Hz

Set F3 F4 F7 F8

Mean Median Mean Median Mean Median Mean Median

Amplitude

(20-second) 0.6640 0.6694 0.6158 0.6541 0.7140 0.6987 0.7567 0.7757

Power

(20-second) 0.5789 0.5954 0.5140 0.5076 0.6735 0.6996 0.7307 0.7513

Amp: α-β

(20-second) 0.1474 0.1123 0.2419 0.2525 0.1960 0.1438 0.2240 0.1849

Pow: α-β

(20-second) 0.0585 0.0001 0.2109 0.2219 0.1018 0.0424 0.1619 0.1020


Scaled Data) - Effective Sampling Rate Calculated as 234 Hz

Set F3 F4 F7 F8


Amplitude

(20-second) 0.5071 0.4720 0.4798 0.4493 0.5399 0.5242 0.2878 0.2728

Power

(20-second) 0.3941 0.3309 0.3404 0.2946 0.4301 0.3649 0.1197 0.0822

Amp: α-β

(20-second) 0.0466 0.0472 0.0631 0.0617 0.0357 0.0200 0.0194 0.0252

Pow: α-β

(20-second) 0.0034 -0.0133 0.0050 -0.0106 -0.0047 -0.0301 0.0065 -0.0114



Set F3 F4 F7 F8


Amplitude

(20-second) 0.7121 0.7114 0.6129 0.6663 0.7463 0.7392 0.6704 0.6689

Power

(20-second) 0.6243 0.6202 0.5368 0.5686 0.6778 0.7399 0.5771 0.5799

Amp: α-β

(20-second) 0.3772 0.4061 0.2979 0.2667 0.4262 0.4236 0.4346 0.4248

Pow: α-β

(20-second) 0.2544 0.2624 0.1961 0.1667 0.3199 0.3164 0.3763 0.3453

69



Set F3 F4 F7 F8


Amplitude

(20-second) 0.7089 0.7272 0.7642 0.7749 0.7483 0.7519 0.6431 0.6957

Power

(20-second) 0.6839 0.7157 0.7513 0.7865 0.7329 0.7523 0.5754 0.6553

Amp: α-β

(20-second) 0.5689 0.5925 0.6380 0.6646 0.6043 0.6250 0.5076 0.5409

Pow: α-β

(20-second) 0.5304 0.5406 0.5968 0.6327 0.5873 0.6093 0.5091 0.5224



Set F3 F4 F7 F8


Amplitude

(20-second) 0.7378 0.7327 0.6997 0.7154 0.7840 0.7843 0.7446 0.7679

Power

(20-second) 0.6881 0.7145 0.6355 0.6472 0.7217 0.7730 0.6891 0.7195

Amp: α-β

(20-second) 0.4784 0.4802 0.4988 0.5097 0.4092 0.3773 0.4530 0.4492

Pow: α-β

(20-second) 0.3699 0.3288 0.3842 0.3726 0.3744 0.3072 0.3534 0.3180

Table 22 - Lin's Correlation for Subject 1 on Proof of Concept System (Averaged and


Set F3 F4 F7 F8


Amplitude

(20-second) 0.5951 0.6073 0.0628 0.0484 0.6371 0.6395 0.7169 0.7290

Power

(20-second) 0.4452 0.4251 0.0085 0.0020 0.5159 0.5434 0.6156 0.6530

Amp: α-β

(20-second) 0.1143 0.0733 0.0177 0.0118 0.1571 0.1133 0.2045 0.1634

Pow: α-β

(20-second) 0.0451 0.0000 0.0045 0.0014 0.0654 0.0098 0.1278 0.0658

70



Set F3 F4 F7 F8


Amplitude

(20-second) 0.4781 0.4534 0.4611 0.4297 0.5066 0.4766 0.0496 0.0443

Power

(20-second) 0.3122 0.2589 0.2873 0.2223 0.3443 0.2574 0.0040 0.0009

Amp: α-β

(20-second) 0.0391 0.0.0392 0.0565 0.0494 0.0293 0.0167 0.0029 0.0042

Pow: α-β

(20-second) 0.0032 -0.0042 0.0052 -0.0041 -0.0005 -0.0054 0.0004 -0.0004



Set F3 F4 F7 F8


Amplitude

(20-second) 0.6708 0.6809 0.4567 0.5737 0.7049 0.6963 0.4986 0.5061

Power

(20-second) 0.5368 0.4998 0.3623 0.3197 0.5809 0.5664 0.3702 0.3269

Amp: α-β

(20-second) 0.3176 0.3004 0.2026 0.1449 0.3853 0.3734 0.3004 0.2349

Pow: α-β

(20-second) 0.1607 0.0981 0.1031 0.0255 0.2463 0.2014 0.2032 0.0956



Set F3 F4 F7 F8


Amplitude

(20-second) 0.6540 0.7128 0.7566 0.7641 0.7255 0.7350 0.5699 0.6445

Power

(20-second) 0.6042 0.6627 0.7146 0.7578 0.6593 0.6700 0.4628 0.5025

Amp: α-β

(20-second) 0.5288 0.5710 0.6260 0.6574 0.5911 0.6245 0.4271 0.4631

Pow: α-β

(20-second) 0.4698 0.4823 0.5577 0.5928 0.5546 0.5553 0.3713 0.3583

71



Set F3 F4 F7 F8


Amplitude

(20-second) 0.7239 0.7225 0.6874 0.7002 0.7626 0.7645 0.7300 0.7446

Power

(20-second) 0.6397 0.6630 0.6019 0.6006 0.6542 0.6733 0.6435 0.6552

Amp: α-β

(20-second) 0.4518 0.4606 0.4713 0.4679 0.3816 0.3539 0.4306 0.4246

Pow: α-β

(20-second) 0.3108 0.2563 0.3233 0.3072 0.3274 0.2972 0.3073 0.2509

The results of comparing the data from the wireless system to the NeuroScan SynAmps2 system

are shown in the four previous tables. The results were separated by test subject due to different

recurring errors during each test; namely, an unexpected pattern occurring in a single channel

(channel 2 for subject 1, and channel 4 for subject 2). The errors are more noticeable in Lin’s

correlation (tables 19 and 20) compared to Pearson’s correlation (tables 17 and 18). Since Lin’s

correlation and intraclass correlation were so similar in section 7.1, only Lin’s was reported here

to minimize data overload.

The results for subjects 3 to 5 were significantly improved compared to the first two subjects.

After analyzing the data from the first two subjects, changes were made to the BTLE code to

produce a higher and more consistent sampling rate.

The results overall are significantly worse than when testing the ADS1299 performance

demonstration kit. While this situation will be discussed more in section 8.1.3, two potential

culprits of the problem are the variation in sampling rate due to Bluetooth streaming, and the

dropping of 1 byte (8 bits) from the transmitted data. The sampling rate for subjects 1 and 2 was

calculated at approximately 234 Hz based on calculation and inspection of the data in MATLAB,

but there was some variation during the testing itself. The sampling rate for subjects 3 to 5 was

calculated at approximately 240 Hz after some changes to the BTLE code were completed

between testing sessions. Note that the results in the alpha-beta range were also significantly

worse than in the full clinical range (1-50 Hz).

72

7.4 Proof-of-Concept Emotional Valence Results

Due to the discrepancies in data between the two systems outlined in the previous section,

emotional valence was calculated separately using both the wireless system and the NeuroScan

system. Self-assessment values were taken from the BlackBerry self-assessment window. In

this case, positive-neutral was counted as any rating from 0 to 4 and negative was any rating

from -4 to -1. This data was post-processed in MATLAB. While the BlackBerry application

contained a section to calculate emotional valence, the discrepancy in the sampling rate would

produce significant errors at this time. Since equations 3, 5, and 11 were shown to be the best in

section 7.2, only those are compared here.

Table 27 - Emotional Valence Classification for Proof-of-Concept System Test

Equation Wireless System NeuroScan

Positive

(154)

Negative

(96)

Total

(250)

Positive

(154)

Negative

(96)

Total

(250)

3 - - 153 96 249 154 95 249

5 - - 154 96 250 154 96 250

11 - - 153 88 231 152 96 248

It is useful to note that despite the problems with the data correlation and the different errors seen

with both subjects, especially in the alpha-beta frequency range, the emotional valence response

was correctly classified in almost every case. Using the baseline readings for an average as

described in section 6.5 probably allowed for the discrepancies to be worked around.

73

Chapter 8. Discussion and Conclusions

8.1 Discussion of Results

8.1.1 Validation of ADS1299 Performance Demonstration Kit

All three types of correlation (Pearson, Lin, ICC) showed that the ADS1299 could produce

frequency domain results very similar to NeuroScan’s SynAmps2 EEG amplifiers. Power

spectra and amplitude spectra for both 4-second and 20-second windows had very high

correlations when the ADS1299 data was averaged and scaled to make the signal amplitudes

roughly the same as the NeuroScan data.

Both 4-second and 20-second windows produced very similar results, with generally the same

mean to at least 2 decimal points. This result shows that both window lengths can produce valid

results at 250 Hz sampling frequency on the ADS1299. While there were some differences in

mean correlation values between amplitude spectra and power spectra, the median values were

very close across both window lengths and over all clinical frequency bands as well as the alpha-

beta range only. The lowest median value across all different correlation coefficients was 0.9608

and the highest was 0.9801. This result suggests that certain windows may have seen errors that

affected the mean much more than the median value.

The ADS1299 appears to acquire and digitize EEG signals with an acceptable fidelity. The

ADS1299 did prove to be somewhat difficult to work with in the proof-of-concept system

development, although this challenge was related to the microcontroller programming involved.

As it is a relatively new chip from Texas Instruments, more development on the documentation

and software side would make it easier to use for EEG applications.

As discussed in section 3.2, the value of using Lin’s correlation and intraclass correlation was

demonstrated by the differences in the values for the raw ADS1299 data compared to the

averaged and scaled data. The Pearson’s correlation coefficients were nearly identical for both

types of data, but the raw ADS1299 data had extremely low Lin’s correlation coefficients and

ICC’s due to an amplitude difference on the order of 1,000,000 (V vs. µV).

The results overall compare very well to the results presented in Chapter 4 of this thesis.

74

8.1.2 Comparison of Emotional Valence Calculation Methods

While many papers regarding emotional valence measurement use frontal alpha-band

asymmetry, they take a wide range of approaches, including support vector machines,

asymmetry indices, and other machine-learning classifications. Some publications also choose

to keep their approaches proprietary as they may be used for marketing or advertising research.

The approach taken here was to compare a number of different combinations of power in the

alpha and beta frequency ranges over the four frontal electrodes being used in the proof-of-

concept system. Equation 1 was taken directly from Ramirez and Vamvakousis [79], and

equations 3 and 4 were versions of the approach used by Brown et al. [20], but the rest were

developed as modifications of those using the F3 / F4 and F7 / F8 electrode pairings alone or in

combination.

The three most accurate equations for classification were based only on alpha-band power, which

does hold with common theories of alpha asymmetry being an appropriate measure of emotional

valence. That any combination of F4 – F3, F8 – F7, or (F4+F8) – (F3+F7) seemed acceptable is

a good result for simple ambulatory monitoring.

The use of a baseline average came after evaluating the calculated emotional valences and

noticing that some subjects had consistently positive or consistently negative measurements,

regardless of their self-assessment scores. Since (in the case of subjects tested at CAMH), there

were baseline EEG readings available, those were used to calculate an average. In the subjects in

the DEAP dataset, an average of all readings for the individual subject was used as there were no

baseline readings. The variance was added to account for slight differences around the average

value. This variance also allowed the approach to be used for all test subjects with a simple

individualized approach. Anecdotally, one subject said that most of the images did not cause any

strong emotion, which was reflected in relatively small changes in measured emotional valence.

Nevertheless, the emotions were still correctly classified on a binary level.

It is a standard EEG procedure to acquire baseline readings, including in Brown et al.’s study on

wireless emotional valence detection [20]. In this way, the emotional valence fluctuations can be

seen more clearly in each individual subject. A simplified binary emotion approach (positive-

neutral and negative) was used due to the limited electrodes and the noted limitations of emotion

elicitation using images only, which was also one of the approaches used in Brown et al. As the

75

results in section 7.2 showed, it was possible to correctly classify the subjects’ classified

emotions using this binary approach in nearly 100% of cases. The use of the variance may

account in some part for the particularly high classification percentage, as a very slightly positive

value for a reported negative emotion could still count as a correct classification, and vice versa.

While this approach may not be perfect because of this slightly soft definition, it seems to

demonstrate that strongly felt emotions would be classified correctly, as a slight difference in

positive or negative in relatively neutral emotions might not be especially noteworthy, especially

in a clinical application.

These results also suggest that using a straightforward emotional valence calculation with the

benefit of a baseline reading might be very useful as a monitoring tool, since strong emotions

could be measured reliably. The results also compare well to the results in Brown et al., which

was the basis of this study. Brown et al. were able to achieve 82% accuracy for classification of

positive, negative and neutral valence while viewing film clips. They achieved up to 85%

accuracy using the same binary emotion approach used here (positive-neutral vs. negative) with

KNN using multiple classifiers using the maximum and kurtosis of maximum of the alpha power

ratio between electrodes F3 and F4. The equations used here are simpler to implement on a

smartphone compared to support vector machines or KNN classifiers. The results also compare

well to the results in Ramirez and Vamvakousis [79], who were able to achieve 80.11%

classification accuracy for positive vs. negative emotional valence in response to sound stimuli.

8.1.3 Proof-of-Concept System Testing

The creation of the wireless EEG acquisition system was a difficult process for the team working

on it (Kevin Tallevi, John Li, Nathaniel Hamming, and myself). The Bluetooth Low Energy

transmission protocol used required interrupt requests to send signal compared to previous

versions of Bluetooth that could be switched on for as long as required. This requirement meant

that the proper interrupt interval had to be found to prevent the Bluetooth transmission from

crashing or shutting down, which created a sampling frequency different than the one being used

by the ADS1299 chip. The data was also transmitted only using its two most significant bytes

(16 bits) instead of the full three bytes created by the chip so that all information for four

channels could be transmitted in the Bluetooth packets.

76

The system was tested first using a low-frequency, low-voltage signal generator to mimic EEG

signals (1-100 Hz at 10-100 µV), which was created by Kevin Tallevi. Once a reasonable signal

could consistently be seen from the signal generator, the system was tested using a standard EEG

cap to look for eye blink peaks and other typical EEG signal qualities. Due to time constraints,

the final system was tested simultaneously with the NeuroScan SynAmps2 system at the same

time as the emotional valence classification was being tested. Anecdotally, certain channels

exhibited unexpected behavior during some of the tests, which is seen most in the Lin’s

correlation coefficients (tables 19-20).

As noted in section 7.3, the correlation between the two systems was not ideal. The most likely

reasons for this are the difference in sampling frequency and the dropping of the third byte of

data. The effective sampling frequency of the wireless system was not absolutely consistent, and

had slight fluctuations. In order to match the data from the NeuroScan system, the data from the

wireless system was processed in MATLAB and an effective average sampling frequency was

calculated based on comparing the length of the time signals and locating the 60 Hz noise signal

with a spectral analysis. Some differences in the sampling frequencies over different windows

may have adversely affected the frequency elements that were being correlated. The data was

resampled in MATLAB, and to avoid some of the signal changes that occur with the resampling

process, the beginning and end of each test (2-4 seconds total) of data was removed on both

systems after the data was aligned properly. A simple analysis on the same test signal with two

bytes and three bytes showed that minimal information loss should have occurred due to this

choice, but it is not impossible that this had an impact.

It should also be emphasized that, while the prototype headset was used for the emotional

valence testing in section 6.4 (shown in figure 20), the standard NeuroScan 64-channel Quik-Cap

was used for the wireless system testing to try to minimize any sources of error. Future testing

should use a separate four-channel headset, though there was not enough time to focus on the

final design and implementation of this headset.

It was worth noting that emotional valence classification showed strong results despite the

differences in data fidelity. This result probably owes a great deal to the use of the baseline

readings in particular, as recurring errors in any channel would be consistent through both the

baseline readings and the subsequent emotional readings. The classification was higher even

77

than that seen in Brown et al’s study, despite the use of images only. This result is probably due

to two-class emotional valence used as well as the individual mean and variance, as discussed

previously.

It should also be noted that the emotional valence calculations and classifications were carried

out in MATLAB during post-processing rather than in real-time on the BlackBerry device. This

approach was due in part to time constraints and in part because of the inconsistencies with the

sampling rate from the Bluetooth device. Most of the time had to be spent producing a reliable

signal on the phone, and the real-time signal processing was kept to a minimum. The application

does calculate the frequency response every 5000 points (20 seconds with a consistent 250 Hz

sampling rate), and can use this data to calculate the emotional valence using all three equations

shown in section 7.4. These results are saved in a database file at this time to be viewed offline,

and are not shown immediately on the BlackBerry.

The emotional self-assessment screen proved to be very easy for the subjects to use during

testing. John Li created it so that it was as simple as moving a slider from -4 to +4

(corresponding to the 1 to 9 scale on the self-assessment manikin), with an image of the SAM

shown above the slider, as in figure 21. At this time, separate user profiles have not been

created, so all self-assessments were saved together, and were compared after testing to the

emotional valence data.

8.2 Limitations and Difficulties Encountered

There were a number of limitations in this study, though there were still quality results in the

end. The primary limitations included:

The lack of time to focus on headset design including the difficulty in designing or

obtaining dry electrodes;

The problems with Bluetooth Low Energy data transmission and the problems with

programming the ADS1299 and using the evaluation software;

Emotional valence testing being limited to a lab setting using image stimuli only.

78

First of all, though a prototype headset was created for some of the tests, it had to be made with

conventional wet electrodes. Since the signal quality of the NeuroScan cap was known, this cap

was used in the analysis of the final wireless system. Some time was spent on the design of

capacitive electrodes, but they were unable to acquire signal of small enough amplitude for EEG.

We also attempted to acquire dry or capacitive electrodes, but they are still a very new

technology. The dry electrodes that were available were priced too high for this project, so a

more standard approach had to be used. As mentioned, Chi et al. noted [62] that significant

focus needs to be given to the electrode design alone to obtain a useable signal. In the future,

this design may be its own project to add on to the wireless EEG acquisition system.

As discussed in the previous section, there were many difficulties encountered while building the

wireless acquisition system. The BTLE data transmission protocol forced a reduction in

sampling rate and bit-rate, and the time it took to resolve these issues to an acceptable level

prevented a more significant testing protocol as well as ways to fix those specific problems. The

ADS1299 also proved difficult to work with on the programming and hardware design side.

While it does incorporate useful EEG-specific features, the ADS1299 also had limited

documentation and support. As the chip was only released in 2012, it is likely for such a young

technology that further development is needed for ideal use. In the future, Kevin Tallevi has

suggested that it might be simpler to create a custom front end with an analog-to-digital

converter and microcontroller that may have fewer inherent difficulties involved.

Finally, the testing protocol was limited to a lab setting with image stimuli. The main reason for

this protocol was the time constraints due to technical difficulties. While the testing did help to

demonstrate the efficacy of the system as a proof-of-concept, it would be very helpful for the

system to be tested outside of a lab setting. This additional testing would evaluate the system

with increased movement as well as real-life emotional stimuli. These stimuli would be harder

to measure for comparison, but the goal of this research is to provide a stepping-stone to a real-

life clinical monitoring application.

79

8.3 Future Directions

This project has a number of future directions that would greatly add to its field of research. In

particular, a dry electrode headset should be developed so that the system can be easily and

comfortably worn on a daily basis without assistance. While wet electrodes provide the best

EEG signal, the system degrades over time and requires gel preparation every time it is worn,

which is messy and somewhat uncomfortable. Dry electrodes with good signal quality would

maintain a consistent signal and would not be difficult to apply properly. Capacitive electrodes

in particular are such a new technology that there is room to focus on the electronics and

shielding design for a wireless system. Also, some research suggests that to read more depth in

emotions rather than just a binary positive-negative scale, a larger electrode montage may be

useful. This requires further testing and research to determine which areas of the brain and

which electrodes can provide meaningful information on specific emotions.

The wireless transmission system may also require refinement. For example, it is possible that

designing an EEG front end from known components rather than using a single chip like the

ADS1299 would provide more flexibility in signal acquisition and analysis. It is also worth

exploring whether another wireless transmission protocol that can handle constant transmission

more simply than BTLE might be useful. A change like this would require some way of

transmitting it to the smartphone, but it is possible that some onboard memory on the acquisition

system could be used for data buffering.

On the software side, future refinements would increase the functionality of the system as a

whole. For example, creating a unique user profile would be helpful, though presumably only

one user would use one system in a real-life setting. It would also be useful to have a setting for

acquiring a baseline reading at the beginning of each wearing period that could be used to create

an average for the emotional valence calculation and classification. A screen for displaying the

user’s emotional valence could provide him or her with useful feedback during the day as well.

On a system diagnostic side, depending on the chips used, it would be a useful feature to

incorporate either an impedance measurement or a lead-off detection setting to ensure that all

electrodes are making sufficient contact to provide good EEG readings.

80

On the testing side, as mentioned in the previous section, it would be helpful to add more testing

modalities. These modalities could include adding sound stimuli, or video stimuli, as well as

scenarios where a user could wear the system at home or at work for an extended period of time.

This testing could be part of a clinical trial when the wireless transmission and headset have been

sufficiently developed. If the system is able to accurately monitor EEG and emotional state, it

may provide a useful tool for ambulatory monitoring of people living with mental illnesses. A

comfortable and unobtrusive headset connected to a smartphone that can be carried in a user’s

pocket would allow them to integrate this monitoring into their daily activities. They could be

provided feedback by the smartphone to promote self-care and self-regulatory behaviour.

Furthermore, if their clinician can be notified of any potential adverse events, more serious

complications and hospitalizations may be avoidable.

Finally, to further improve the emotional recognition, this system could be grown to incorporate

sensors on the autonomic nervous system, such as heart rate, blood pressure, or skin

conductance, which are used in different emotional response studies. This development would

create a more complete MBAN-type system. Alternatively, in an MBAN system, the EEG

sensors could be used alongside other monitoring tools for ECG, blood sugar, and others

depending on the requirements of each individual user. This more complete approach would

provide better non-clinical monitoring for patients who may not be able to readily access a

hospital and to improve patient-care outcomes by detecting problems before they become serious

enough to require hospitalization.

81

8.4 Conclusions

The purpose of this thesis, as stated in section 1.3, was to create and test a proof-of-concept

novel ambulatory EEG system to monitor emotional valence in real-time. This purpose was

mostly achieved, as an ambulatory EEG acquisition and transmission system was created that

sends data to a BlackBerry Z10 smartphone for storage and basic analysis. During testing, users

were able to perform emotional valence self-assessments on the smartphone. Using the acquired

data, the emotional valence of subjects was correctly classified on a positive-neutral vs. negative

basis in post-processing.

In pursuit of the main objective of the thesis, a comparative evaluation of a wireless EEG system

from the imec group to a gold-standard laboratory EEG system was performed prior to the

development of a novel ambulatory system. The novel system was developed using the Texas

Instruments’ ADS1299 EEG front-end chip. This chip was evaluated using the performance

demonstration kit in the same way as the imec system. A number of emotional valence

calculation methods were also compared using the performance demonstration kit, the final

wireless system, and data from the DEAP. Three of these equations using alpha asymmetry in

frontal EEG electrodes (F3 to F4 and F7 to F8) were able to correctly classify the user’s self-

reported emotional valence nearly 100% of the time using a baseline reading as an average.

The wireless acquisition and transmission system was tested using a standard laboratory EEG

cap rather than with a simple ambulatory headset, as dry electrodes proved too costly to acquire

and too difficult to build in the required timeframe. The data also had to be post-processed using

MATLAB as Bluetooth transmission requirements affected the sampling rate of the data to the

point that the emotional valence calculations on the BlackBerry would have been affected. The

quality of the data was not excellent, but the emotional valence classification was still very

successful. Furthermore, while emotional valence was only tested using image stimuli in a lab

setting, the classification was very accurate across all participants.

The work completed is a step towards an ambulatory monitoring system for people living with

mental illnesses. Further refinements to this work may provide a useful tool to monitor and

prevent the escalation of adverse events in mental illnesses to lessen the impact of mental

illnesses to individuals, to the community, and to the economy.

82

8.5 Summary of Contributions

This study was designed to replicate research by Brown et al. [20] by measuring emotional

valence with EEG signals on a mobile system. Several contributions to the research field were

accomplished during this project.

1. This study was one of the first to perform a direct quantitative comparison of a wireless

EEG system to a gold-standard EEG system used in mental health applications. A direct

comparison of two systems using the same cap had not been done in this way before.

Some studies had placed electrodes very close together for two different systems but this

study provided the most direct comparison possible for what would be the same signal

source. This comparison method was repeated for both the ADS1299 performance

demonstration kit and the wireless acquisition and transmission system.

2. This study was also one of the first to compare a wide range of simple emotional valence

equations using a combination of asymmetrical alpha and beta power in frontal

electrodes, particularly F3, F4, F7, and F8. By averaging the emotional valence during

baseline (neutral eyes open and eyes closed) EEG readings, several equations, in

particular [ - ], [ - ], and [ - ] were able to

correctly classify positive-neutral vs negative emotional valence nearly 100% of the time

compared with the subjects’ self-assessments. Other studies used machine learning

algorithms with a number of different signal characteristics, particularly in the alpha

band, but these approaches used simple ratios and differences of absolute power over the

alpha and beta frequency ranges.

3. The study produced an EEG acquisition and transmission system using the ADS1299

EEG front-end for analog-to-digital conversion and multiplexing, and Bluetooth Low

Energy for transmission to a BlackBerry smartphone.

4. This study also produced a BlackBerry application that could acquire and analyze EEG

data to calculate emotional valence given a consistent sampling rate. The application also

allows users to perform self-assessment of emotional valence that can be compared to the

emotional valence measured at different times.

83

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Appendices

Appendix 1 - Register Settings for ADS1299

Register Address Value D7 D6 D5 D4 D3 D2 D1 D0

ID 0x00 0x3E 0 0 1 1 1 1 1 0

CONFIG1 0x01 0x96 1 0 0 1 0 1 1 0

CONFIG2 0x02 0xC0 1 1 0 0 0 0 0 0

CONFIG3 0x03 0xFC 1 1 1 1 1 1 0 0

LOFF 0x04 0x00 0 0 0 0 0 0 0 0

CH1SET 0x05 0x50 0 1 0 1 0 0 0 0

CH2SET 0x06 0x50 0 1 0 1 0 0 0 0

CH3SET 0x07 0x50 0 1 0 1 0 0 0 0

CH4SET 0x08 0x50 0 1 0 1 0 0 0 0

CH5SET 0x09 0xD0 1 1 0 1 0 0 0 0

CH6SET 0x0A 0xD0 1 1 0 1 0 0 0 0

CH7SET 0x0B 0xD0 1 1 0 1 0 0 0 0

CH8SET 0x0C 0xD0 1 1 0 1 0 0 0 0

BIASSENSP 0x0D 0x06 0 0 0 0 0 1 1 0

BIASSENSN 0x0E 0x02 0 0 0 0 0 0 1 0

LOFF_SENSP 0x0F 0x00 0 0 0 0 0 0 0 0

LOFF_SENSN 0x10 0x00 0 0 0 0 0 0 0 0

LOFF_FLIP 0x11 0x00 0 0 0 0 0 0 0 0

LOFF_STATP 0x12 0x00 0 0 0 0 0 0 0 0

LOFF_STATN 0x13 0x00 0 0 0 0 0 0 0 0

GPIO 0x14 0x00 0 0 0 0 0 0 0 0

MISC1 0x15 0x20 0 0 1 0 0 0 0 0

MISC2 0x16 0x00 0 0 0 0 0 0 0 0

CONFIG4 0x17 0x00 0 0 0 0 0 0 0 0

98

Appendix 2 - IAPS Images Used in Emotional Valence Study

Included below are the IAPS image identifications of the images used in the emotional valence

portion of this thesis. Valmn is the mean valence score reported by the IAPS, Valsd is standard

deviation of the valence score. Aromn is the mean arousal score, and arosd is the standard

deviation of arousal. Though it was not used in this study, dom1mn and dom1sd refer to the

dominance score of the images.

The images are grouped as follows: High valence, high arousal; high valence, low arousal; low

valence, high arousal, low valence, low arousal, medium valence, medium arousal.

desc IAPS valmn Valsd aromn arosd dom1mn dom1sd

Jaguar 1650 6.65 2.25 6.23 1.99 4.29 1.99

Astronaut 5470 7.35 1.62 6.02 2.26 4.96 2.47

Cupcakes 7405 7.38 1.73 6.28 2.16 5.67 2.4

Sailboat 8170 7.63 1.34 6.12 2.3 5.72 2.15

Skier 8190 8.1 1.39 6.28 2.57 6.14 2.74

WaterSkier 8200 7.54 1.37 6.35 1.98 6.17 1.61

Rafting 8370 7.77 1.29 6.73 2.24 5.37 2.02

Gymnast 8470 7.74 1.53 6.14 2.19 6.17 2.09

RollerCoaster 8490 7.2 2.35 6.68 1.97 5.37 2.46

Rollercoaster 8499 7.63 1.41 6.07 2.31 . .

PolarBears 1441 7.97 1.28 3.94 2.38 . .

Gannet 1450 6.37 1.62 2.83 1.87 6.75 1.87

Rabbit 1610 7.82 1.34 3.08 2.19 6.77 2.19

Antelope 1620 7.37 1.56 3.54 2.34 6.82 2.34

Binoculars 2314 7.55 1.24 4 2.01 6.17 1.78

Flower 5010 7.14 1.5 3 2.25 7.4 2.25

Nature 5780 7.52 1.45 3.75 2.54 6.05 2.3

Clouds 5891 7.22 1.46 3.29 2.57 5.2 2.57

IceCream 7340 6.68 1.63 3.69 2.58 6.32 2.33

Violin 7900 6.5 1.72 2.6 2.08 6.48 2.22

Snake 1052 3.5 1.87 6.52 2.23 3.36 2.26

Gun 2811 2.17 1.38 6.9 2.22 . .

AimedGun 6200 2.71 1.58 6.21 2.28 3.35 2.28

Attack 6370 2.7 1.52 6.44 2.19 3 1.87

Attack 6510 2.46 1.58 6.96 2.09 2.81 2.12

Shipwreck 9620 2.7 1.64 6.11 2.1 3.29 1.95

Bomb 9630 2.96 1.72 6.06 2.22 2.98 2.13

CarAccident 9904 2.39 1.36 6.08 2.06 3.4 2.21

CarAccident 9910 2.06 1.26 6.2 2.16 3.02 1.89

99

Fire 9921 2.04 1.47 6.52 1.94 3.57 2.41

Woman 2039 3.65 1.44 3.46 1.94 5.06 1.85

Woman 2399 3.69 1.4 3.93 2.01 . .

Man 2490 3.32 1.82 3.95 2 4.72 2.03

ElderlyWoman 2590 3.26 1.92 3.93 1.94 4.31 2.14

Jail 2722 3.47 1.65 3.52 2.05 5.34 2.34

Jail 6010 3.73 1.98 3.95 1.87 5.08 2.53

Bucket 7078 3.79 1.45 3.69 1.86 5.41 1.73

Cemetery 9001 3.1 2.02 3.67 2.3 3.47 1.9

Puddle 9110 3.76 1.41 3.98 2.23 4.88 1.68

Cemetery 9220 2.06 1.54 4 2.09 3.13 1.97

Wolf 1645 4.99 1.64 5.14 1.99 4.74 1.91

TongueOut 2122 5.15 1.82 4.59 1.91 5.49 1.81

MaleFace 2220 5.03 1.39 4.93 1.65 5.32 1.77

Soldiers 2704 4.85 1.89 5.3 2.16 . .

Actor 2780 4.77 1.76 4.86 2.05 . .

Coach 3550.2 4.92 1.62 5.13 2.24 5.38 2.02

Stove 7077 5.12 1.46 4.61 2.06 5.6 1.85

Ramen 7476 4.99 2.24 4.63 2.02 5.45 1.99

Crowd 7497 5.19 1.55 4.97 2.16 4.26 2.1

Battleship 9422 4.95 1.72 5.09 1.92 4.89 2.25

100

Appendix 3 - Screening Form for EEG Study Participants at CAMH

TMS - Screening and Demographic Information Form

Completed by: Date:

Subject Name: Subject #:

Gender: Male Female DOB: _____/_____/_____ Age: ________

d m y

Status: Control Patient Diagnosis: _____________________

Address: ____________________________________________________________

____________________________________________________________

____________________________________________________________

Telephone # - Home: ______________________ Work: ___________________

Primary Language: Other Languages:

DRUG USAGE? SMOKER?

ETHNICITY:

Type of Education: 1 – grade 6 or less

2 – grade 7 to 12 (w/o completing high school), 3 – graduated high school

4 – part college

5 – graduated 2 year college

6 – graduated 4 year college

7 – part graduate / professional school

8 – completed graduate / professional school

Years of Education:

Current Main Occupation and for How Long:

Father’s Highest Education (see prev. scale): Occupation:

Mother’s Highest Education (see prev. scale): Occupation:

101

Handedness:

L R

1. Writing

2. Drawing

2. Throwing

3. Scissors

4. Toothbrush

5. Knife (without fork)

6. Spoon

7. Broom (upper hand)

8. Striking a match

Kick a ball

Eye when using only one

+, ++ when preference is so strong that you would never try to use the other hand unless

absolutely forced to

MEDICAL HISTORY

I would like to ask you some questions about your health.

1. Weight: __________ Height: __________

2. Have you ever been hit on the head and lost consciousness for more than one hour?

(probe for when head injury occurred and if hospitalization was required, how long PTA

lasted, etc.) Y N

If yes, describe: ________________________________________________________________

______________________________________________________________________________

______________________________________________________________________________

______________________________________________________________________________

102

3. Do you have a history of (check ALL that apply):

Seizures ______ Stroke ______

Hypertension ______ Diabetes ______

Heart Attack ______ Thyroid Disease ______

Pulmonary ______ Allergies ______

Other (specify): ________________________________________________________________

______________________________________________________________________________

Details (explain): _______________________________________________________________

Are you pregnant?

Are you currently on birth control?

4. Have you ever seen a psychologist, psychiatrist, clinical social worker, or other mental

health professional? Y N

Under what circumstances? _______________________________________________________

______________________________________________________________________________

Is there a history of mental illness in your family? Y N

Which illness and what family member?_____________________________________________

______________________________________________________________________________

Have you been hospitalized for psychiatric reasons? Y N

If yes, roughly how many times? 1 2 3 4 5 6 ___

Where and when were you admitted?

Date Reason/Dx In/Out Pt. Length Tx

Emerg?

___/___/___ _____________________ _________ _________________ _________

d m y

103

___/___/___ _____________________ _________ _________________ _________

d m y

___/___/___ _____________________ _________ _________________ _________

d m y

___/___/___ _____________________ _________ _________________ _________

d m y

___/___/___ _____________________ _________ _________________ _________

d m y

___/___/___ _____________________ _________ _________________ _________

d m y

5. Do you need/use glasses for reading? Y N

6. How many times have you seen a doctor in the past year (excluding times/ hospitalized)?

___________

Dr. Reason Tx

_______________________ _____________________________ _______________________

_______________________ _____________________________ _______________________

_______________________ _____________________________ _______________________

7. What medicines are you currently taking (prescription and non-prescription)?

Time on Change

Name Reason Dose How Often Medication in Dose

_________________ _________________ _____ ___________ _______________________

_________________ _________________ _____ ___________ _______________________

_________________ _________________ _____ ___________ _______________________

_________________ _________________ _____ ___________ _______________________

_________________ _________________ _____ ___________ _______________________

_________________ _________________ _____ ___________ ___________ ___________

104

Contraindications to Magnetic Exposure

No If Yes, Explain

Surgical clips in the brain

Cardiac pacemaker OR valves

Cochlear implant

Metal rods, plates, screws, or nails

Shrapnel/metal fragments in head/eyes/body

Dentures

Have you ever had an adverse reaction to TMS?

Have you ever had an EEG?

Do you suffer from frequency or severe headaches?

Have you ever had any other brain-related condition?

Have you ever had any illness that caused brain injury?

Does anyone in your family have epilepsy?

Do you need further explanation of TMS and its associated risks?

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