phase analysis in steady-state visual evoked potential ... · figure 3.9.the definition of each...

Technical Note PR-N 2010/00081

Issued: 03/2010

Phase Analysis in Steady-State Visual Evoked Potential (SSVEP)

based BCIs

Shirin Abtahi, Gary Garcia Molina

Philips Research Europe

Unclassified Koninklijke Philips Electronics N.V. 2010

PR-TN 2010/00081 Unclassified

ii Koninklijke Philips Electronics N.V. 2010

Authors’ address

Shirin Abtahi [email protected]

G. Garcia Molina HTC34-41 [email protected]

© KONINKLIJKE PHILIPS ELECTRONICS NV 2010 All rights reserved. Reproduction or dissemination in whole or in part is prohibited without the prior written consent of the copyright holder .

mailto:[email protected]

mailto:[email protected]

Unclassified PR-TN 2010/00081

Koninklijke Philips Electronics N.V. 2010

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Title: Phase Analysis in Steady-State Visual Evoked Potential (SSVEP) based BCIs

Author(s): Shirin Abtahi

Reviewer(s): IPS Facilities

Technical Note: PR-TN 2010/00081

Additional Numbers:

Subcategory:

Project:

Customer:

Keywords: BCI, SSVEP, Phase Analysis, Spatial Filter, Neural Network classifier, Information Transfer Rate, Confusion Matrix, ROC curve, Area Under Curve

Abstract: Brain-computer interfaces (BCI) based on Steady State Visual Evoked

Potential (SSVEP) can provide higher information transfer rate than other BCI modalities. For the sake of safety and comfort, the frequency of the repetitive visual stimulus (RVS) necessary to elicit an SSVEP, should be higher than 30 Hz.

However, in the frequency range above 30 Hz, only a limited number of frequencies can elicit sufficiently strong SSVEPs for BCI purposes. Consequently, the conventional approach, consisting in presenting various repetitive visual stimuli having different frequency each, is not practical for SSVEP based BCI functioning. Indeed this would bring low communication bitrates.

In order to increase the number of possible repetitive visual stimuli, we consider modulating the phase of the stimulus instead of the frequency. Thus, several stimuli, sharing the same frequency, but with different phase can be presented to the user.

The approach presented in this document, to detect the phase of the stimulus is termed phase synchrony. It consists in using as feature, to identify a subject's focus of attention, the phase difference between the SSVEP and the stimulus. The phase is extracted through the Hilbert transform applied on an univariate signal resulting from spatially filtering the electroencephalogram.

We have conducted experiments with seven subjects to estimate the information transfer rate that can be achieved using the phase synchrony analysis method.

Conclusions: The phase in high frequency band (32Hz-40Hz) can be use to increase the number of possible commands in BCI operation.

The Hilbert transform (phase synchrony) effectively extracts the phase


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difference

Spatial filter can improve the classification accuracy

It is represent that the coefficient of spatial filter (w) is different for each subject, so the spatial filter is subject dependent.

Information transfer rate calculated by Nykopp equation is more than Walpaw equation.

Information transfer rate (bit rate) by calculating spatial filter is more than Oz-Cz derivation

The result of information transfer rate achieving with spatial filter in both Synchronous and Asynchronous compare to Oz-Cz combination, prove the advantage of spatial filter enhancement.



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Figures Index Figure 2.1.Brain main parts ........................................................................................................... 11 Figure 2.2.The four lobes of cerebral cortex ................................................................................. 11 Figure 2.3.Comparison methods of brain activity measurement [8].............................................. 12 Figure 2.4.Synaptic transmission-communication between neurons ............................................ 13 Figure 2.5.Left picture shows cortical field potentials[9]- the right picture shows the structure of pyramidal cells as a dipole ............................................................................................................ 13 Figure 2.6.The surface electrode position [9] ................................................................................ 14 Figure 2.7.Standard 10-20 system [9] ........................................................................................... 14 Figure 2.8.Schematic diagram of typical BCI system [8] .............................................................. 15 Figure 3.1.Calculate Energy in stimuli frequency as feature ......................................................... 17 Figure 3.2.Extract feature from signal by sliding window-the statistical properties like mean or mode is used to extract feature from each window ....................................................................... 18 Figure 3.3.Extract phase as a feature. .......................................................................................... 18 Figure 3.4.Extract feature from signal by sliding window-the statistical properties like mean or mode is used to extract feature from each window ....................................................................... 19 Figure 3.5.Example of two extracted features of signal versus each other for one subject with 1-second window without overlap..................................................................................................... 20 Figure 3.6.Example of two extracted features of signal versus each other for one subject with spatial filter (1-second window without overlap) ............................................................................ 23 Figure 3.7.Classification system.................................................................................................... 24 Figure 3.8.Single layer Neural Network with two features and three classes ............................... 24 Figure 3.9.The definition of each cell of confusion Matrix Probability-The diagonal elements indicate TP and the off-diagonal elements indicate FP (classes have the same probability)

................................................................... 26 Figure 3.10.Indicate the number of TP, FP, TN and FN ............................................................... 27 Figure 3.11.Confusion Matrix probability ....................................................................................... 27 Figure 3.12.Calculate the information transfer rate and accuracy with the specific shift and window size, the blue arrows show the shift. ................................................................................ 29 Figure 4.1.Distribution of LEDs ..................................................................................................... 31 Figure 4.2.The sounds hear from speaker two seconds before starting the stimuli to tell the subject to be ready for looking at the new direction ...................................................................... 32 Figure 4.3.EEG electrode placement according to the 10-20 system ........................................... 33 Figure 5.1.Feature energy versus feature phase with Oz-Cz ....................................................... 35 Figure 5.2.Feature energy versus feature phase with Spatial filter ............................................... 36 Figure 5.3.Compare the energy of Oz-Cz and the spatial filter SSVEP signal for S1 .................. 36 Figure 5.4.Information transfer rate result with different window size (0.1-2.7sec) with different overlap for subject 1-red indicates spatial filter and blue indicates Oz-Cz combination ............... 37 Figure 5.5.The best result for Information transfer rate and accuracy .......................................... 38 Figure 5.6.Display energy versus phase feature with Oz-Cz combination and after spatial filtered signal for best window size and overlap-Left figures show, the Train and test with Oz-Cz and Right figures show the train and Test trials after spatial filtering for S1 ........................................ 38 Figure 5.7.Calculated bit/trial for spatial filtered signal (red) and Oz-Cz signal (blue) for subject 1 with different window size (0.1 to 2 sec) ....................................................................................... 40 Figure 5.8.The best Information Transfer rate and test accuracy for subject one ........................ 41 Figure 5.9.Comparison of Instantaneous phase difference between SSVEP and stimuli signal for 1-second EEG record .................................................................................................................... 43 Figure 5.10.Compare spatial filtered signal for subjects ............................................................... 44 Figure 5.11.The best ROC curve for each subject ........................................................................ 44


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Tables Index Table 1.Data classification ............................................................................................................ 22 Table 3.The optimal frequency selects for each subject ............................................................... 31 Table 4.The stimuli place that have the most bit/trial for subject 1-40Hz with different window size and best shift ................................................................................................................................. 41 Table 5.The AUC for the best ROC curve for each subject .......................................................... 45 Table 6.The best information transfer rate in Synchronous and Asynchronous methods for each subject ........................................................................................................................................... 45 Table 7.The location of best information transfer rate with Asynchronous and Synchronous method for each subject ................................................................................................................ 46



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Contents

Figures Index ............................................................................................................................... v

Tables Index ................................................................................................................................ vi

1. Introduction .......................................................................................................................... 9

1.1. Objective ...................................................................................................................... 9

1.2. Problem description ..................................................................................................... 9

1.3. Solution approach........................................................................................................ 9

1.4. Outline ......................................................................................................................... 9

2. Background knowledge .................................................................................................... 11

2.1. Physiology of human brain ........................................................................................ 11

2.2. Brain activity .............................................................................................................. 12

2.3. Brain Computer Interface (BCI) ................................................................................. 15

2.4. Evoked Potential ........................................................................................................ 16

2.5. Visual Evoked Potential (VEP) .................................................................................. 16

2.5.1. Transient and steady state Evoked Potential ............................................... 16

2.5.2. Steady-State visual Evoked Potential (SSVEP) ........................................... 16

3. Methodes ............................................................................................................................ 17

3.1. Preprocessing ............................................................................................................ 17

3.2. Features extraction .................................................................................................... 17

3.2.1. Features ........................................................................................................ 17

3.2.1.1. Energy .......................................................................................................... 17

3.2.1.2. Phase ............................................................................................................ 18

3.2.2. Feature enhancement .................................................................................. 21

3.2.2.1. Spatial filtering .............................................................................................. 21

3.3. Classification ............................................................................................................. 24

3.3.1. Classifier ....................................................................................................... 24

3.4. Information Transfer Rate ......................................................................................... 26

3.4.1. The Confusion Matrix ................................................................................... 26

3.4.2. Information Transfer Rate Calculation .......................................................... 28

4. Experiment Procedure ...................................................................................................... 30

4.1. Recording Equipment ................................................................................................ 30

4.2. Experiment Setup ...................................................................................................... 31

5. Result and discussion....................................................................................................... 34

5.1. Compare result of single channel detection with multiple channel detection ............ 35

5.2. Phase classification results ....................................................................................... 37

5.2.1. Asynchronous method .................................................................................. 37

5.2.2. Synchronous method .................................................................................... 40


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5.3. Discuss result of all subjects ..................................................................................... 43

6. Conclusion ......................................................................................................................... 48

7. Future work ........................................................................................................................ 49

References ................................................................................................................................. 50



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1. Introduction

In fact, many people all around the world have “locked-in” due to certain reasons every year. “Locked-in,” means that someone lost the voluntary movement of the Body. However, they stay conscious after they got locked-in. For instance, brainstem strokes, spinal cord injuries, Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, multiple sclerosis are some of the diseases that damages neurons which controlling muscles. Using Brain Computer Interface (BCI) as a helper for some alternative abilities such as speaking, communication, drive wheel chair and so on is a good solution for these patients. Moreover, other Research fields in BCI have done about providing ability of communication with digital world without the use of muscles. Most BCI systems use three groups of EEG Signals: rhythmic activity Signals, evoked potential and the modulation response. This work focus on specific evokes potential called

SSVEP Signals. The steady-state visual evoked potential (SSVEP) ‐based BCI’s were first intro-

duced by J. Vidal in 1973, it is a visual cortical response evoked by a repetitive stimulus oscillat-ing at a constant frequency.[1] As a nearly sinusoidal oscillatory waveform, the SSVEP usually contains the same fundamental frequency as the stimulus and some harmonics of the fundamen-tal frequency [2].

1.1. Objective

The aim of this thesis work is to assess the accuracy of phase detection in Steady-State Visual Evoked Potential (SSVEP) based BCI.

1.2. Problem description

Usage of flickering frequency in the low bands (5–12Hz) and medium bands (12–25 Hz) for producing SSVEP have some disadvantages. The first, in the lower frequencies band, visual fatigue easily happens. The Second, flashing LED can provoke seizures especially between 15 to 25Hz. The third, low frequency band covers alpha band (8-13Hz) which cause amount of false positive. [3] All these disadvantages can mitigate by using the high frequency band (25-50Hz). However, the high frequency band (25-50Hz) also has deficiency such: not all these frequencies can elicit SSVEP enough strong for BCI purposes. In addition, the usage of this band for com-mands is not enough to prepare a full keyboard due to this fact that a subject has not good re-sponse at some of them.

1.3. Solution approach

Different approaches can introduce to overcome the problem, above that two of them men-tion here. One is combining two frequencies to obtain third stimuli, but this destroys the periodic-ity of the stimuli. The other one is using stimulus with different phases with same frequency. It has reported that SSVEP is phase-locking i.e. successive cycles at identical start and ends should have identical phase (each period have the same phase at start point). [4] In addition, the SSVEP phase has successfully applied in BCIs [5]. The aim of this project is developing a method to determine to which stimulus that the subject pay attention from a set of stimuli oscillating at the same frequency but different phase.

1.4. Outline

This thesis report consists of seven chapters. The first introduces the objective, problems and solution approaches. Second chapter contain short description of background knowledge that needs in this work like physiology of human brain, the principal of EEG, the meaning of Brain Computer Interface, the concept of Visual Evoked Potential (VEP) and finally a clear definition about Transient and steady-state visual evoked potentials.


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In chapter three, the step of feature extraction and methods of classification define. The concept of spatial filter, Principal component analysis and neural network classifier is considered. In this section, also some consistent evaluation criteria such as Confusion Matrix, ROC curve and Information transfer rate introduce for comparing the classification results. Chapter 4 discuss about the advantage of using special filter. The result of using PCA on feature extraction has shown. The results of the applying of the neural Network classifier that explained in the previous chapter have done. The conclusion and future research give in chapter six and seven.



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2. Background knowledge

2.1. Physiology of human brain

The Central Nervous System (CNS) consists of brain and spinal cord. The brain is the important part of the CNS and it is responsible for the control of body activities. In addition, it is the centre of interpretation of information from the senses like sight, hearing, smell, etc. The brain has three main parts: the cerebrum, the cerebellum and the brainstem (Figure 2.1).

Figure 2.1.Brain main parts

Cerebrum is the largest portion of the brain and is about the two-third of the total weight of the brain. The thin layer of the cerebrum calls cerebral cortex and it divides into two hemispheres. The right hemisphere controls movements of the left side of the body; the left hemisphere con-nects to the right side of the body. The cerebral cortex can divide into four sections: The frontal lobe, parietal lobe, occipital lobe and temporal lobe (Figure 2.2).

Figure 2.2.The four lobes of cerebral cortex

The frontal lobe is located at the front of the brain and is related with reasoning, motor skills, higher level recognition, and expressive language. The motor cortex lies at the back of the frontal lobe, near the central sulcus.

The parietal lobe situates in the middle section of the brain and relates with processing sensory information such as pressure, touch, and pain. Somatosensory cortex is located in this lobe and is necessary to the processing of the body's senses.

The temporal lobe is located on the bottom section of the brain. This lobe is also the loca-



tion of the primary auditory cortex, which is important for interpreting sounds and the lan-guage we hear. The hippocampus is also located in the temporal lobe, which is why this lobe of the brain is associated with the formation of memories.

The occipital lobe is located at the back portion of the brain and is associated with inter-preting visual stimuli and information. The primary visual cortex, which receives and in-terprets information from the retinas of the eyes, is located in the occipital lobe.[6]

Cerebellum is located just above the brain stem and toward the back of the brain. It is responsible for voluntary body movement, balance and equilibrium and muscle tone. Brainstem plays a vital role in consciousness, attention and arousal. All information from the brain to the body or from the body to brain must passes through the brainstem.

2.2. Brain activity

Brain sends and receives signals every second during the day in the form of hormones, nerve impulses and chemical messengers. The exchange of this information makes human move, eat, sleep and think. Obstructions such as tumours can interrupt normal brain activity, leading to mitigates of motor control, or consciousness. Since the actual cause of these problems is internal and the structure of the brain is fragile, it is difficult to access to it without risking damage. Therefore, the imaging technique as a non-invasive method is used to visual the brain activity. Technology provides many useful tools for studying the brain activity that some of them use for medical diagnosis and some of them for research. There are two methods of brain analysis. The structural analysis studies about the anat-omy of the brain to find the structural deviations such as tumours, blood clots, lesions and some-thing like this. MRI and CAT/CT are the example of this kind of analysis. The functional analysis measures the location of brain activity. This is useful to diagnose something like epileptic sei-zures or disease affecting brain activity. Some typical functional analysis is PET/SPET, EEG and MEG and fMRI. Many times a structural and functional method use in joint to better evaluation how the activity and region are related. [7] On the other hand, the imaging methods that measure the brain activity by changes in blood flow, oxygen consumption and glucose utilization call indirect method such as MRI/fMRI, PET/SPET and CAT/CT. MEG and EEG are direct methods to measure the brain activity by using magnetic and electrical fields. In these methods, EEG is more usable because there is no need for expensive equip-ments and devices. Additionally, it can measure with suitable time resolution around 1ms. How-ever, spatial resolution is not good enough (about three cm3). Figure 2.3 shows the difference between these methods.

Figure 2.3.Comparison methods of brain activity measurement [8]

The EEG is a recording of the brain’s electrical activity, in most cases made from elec-



13

trodes over the surface of the scalp. It may also be make from electrodes placed directly over the surface of the brain or from needle electrodes inserted into the brain. The recordings are the summation of volume conductor fields produced by millions of interconnecting neurons. The neuron components producing the currents are the dendrites, axons and cell bodies. The cerebral cortex contains two types of nerve cells – excitatory or inhibitory. Each neu-ron – a nerve cell in the brain – communicates with other neurons through chemical connections that fire off a tiny bit of chemical that either inhibits or excites the next neuron. These connections between neurons call synapses. (Figure 2.4)

Figure 2.4.Synaptic transmission-communication between neurons1

Pyramidal cells are the efferent (long-axon) cells of the cerebral cortex. The name refers to the shape of the cell body. The apex of the pyramid points toward the cortical surface. A large apical dendrite extends further upward toward that surface, while other dendrites arise from the corners and sides of the pyramid. The axon extends down into white matter (the internal capsule) from the base of the pyramid. Most pyramidal cells project association fibres to other cortical regions and/or to deeper nuclei of the brain. (Figure 2.5)

Figure 2.5.Left picture shows cortical field potentials[9]- the right picture shows the structure of pyramidal cells as a dipole

1 http://cwx.prenhall.com/bookbind/pubbooks/morris5/chapter2/custom1/deluxe-content.html



Variations in time in the potentials of the cortical dipole layer leads to EEG-waves. Only synchronous variation in groups of neurons contributes to the EEG signal and asynchronous activity cancels out. The electrode placement on the surface of the scalp is shown in Figure 2.6.

Figure 2.6.The surface electrode position [9]

In the 10-20 standard electrode position, the electrodes place at 10 or 20% of the total left to right and back to front distances. The nasion (between nose and forehead) and onion (the lowest part of the back of the skull) use as primary landmarks. (Figure 2.7)

Figure 2.7.Standard 10-20 system [9]



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2.3. Brain Computer Interface (BCI)

Actually, the Brain Computer Interface (BCI) or brain machine interface communicates with the environment by use of brain activities. In addition, the goal of BCI is to allow the user to communicate with the computer by thought. Since it does not sensor exist for thought, the one way for inference thoughts is using electrical activities of the brain. As depicted in the schematic diagram of BCI system (Figure 2.8), the first function is related to biosensors which collecting EEG Electrical Activity of the brain. After that, it removes the interface of eye movement, affect of heart rate, Power Line and other common physiological and physical noises. The result of this is the improved SQNR and digitalized signal before send-ing them to computer.

Figure 2.8.Schematic diagram of typical BCI system [8]

Like any communication or control system, a BCI has input (i.e. electrophysiological activ-ity from the user), output (i.e. device commands), components that translate input into output, and a protocol that determines the onset, offset, and timing of operation. Moreover, Computer software executes several steps due to extract features, classified signal and recognizes specific commands from the brain. After that, the commands send to the application for specific functions. For example, spelling alphabet or moving the cursor is some of the functions that can get from these steps. BCI can divide into three types due to placing of the electrodes:

Invasive Brain Computer Interface: the devices implanted directly to the brain and have the highest signal quality. These devices can provide functionality for paralyzed people. The problem of this type is that the scar tissue forms over the device as e reaction to the ex-ternal matter and this reduce the efficiency and increase the risk to the patient.

Partially Invasive BCIs also implement inside the skull but outside the brain. The signal strength of this type is a bit weaker but has a less risk of scar tissue formation.

Non Invasive Brain Computer Interface has the least signal precision and skull distorts the signals but it is the safest. In this type patient has the ability of moving muscle and re-store partial movement. One of the most important devices in this group is the electroen-cephalogram (EEG) that provides a good temporal resolution. This is cheap, portable and easy to use.[10]

As described above, most BCI systems use EEG Signals in order to simple process of taking EEG signals. The sources of EEG signals that we use in BCI have typically divided into three groups: The first group is rhythmic activity Signals from the brain, the second is evoke potential and the third is modulation response. In this project work a specific evoke potential call SSVEP use.



2.4. Evoked Potential

Evoked potential (EP) measure the electrical activity of the brain in response to stimulation of specific sensory nerve pathways. Three type of the evoked potential have used:

Visual Evoked Potentials (VEP): The patient sits before a screen on which a flashing checkerboard pattern or LEDs has display.

Brainstem Auditory Evoked Potentials (BAEP): The patient hears a series of clicks in each ear.

Sensory Evoked Potentials (SEP): Short electrical impulses administer to an arm or leg. In order to measure evoked potentials. Electrodes place on the scalp overlying the areas of the brain being motivated. The exam-iner then provides specific types of sensory input (e.g., sound, light, or sensation), and records the responses of the person’s brain. Each type of evoked potentials can measure from the spe-cial part of the brain. [11]

2.5. Visual Evoked Potential (VEP)

A visual evoked potential is an evoked potential caused by a visual stimulus, such as an alternating checkerboard pattern on a computer screen or flickering LEDs. Responses can record from electrodes that place on the back of your head (occipital region) and observe as a reading on an electroencephalogram (EEG). These responses usually originate from the occipital cortex, the area of the brain involved in receiving and interpreting visual signals. There are two major classes of VEP stimulation, luminance and pattern. Luminance stimu-lation is usually delivered as a uniform flash of light and pattern stimulation may be either acces-sible in a reversal or onset-offset fashion. [12]

2.5.1. Transient and steady state Evoked Potential

Transient EPs produce by giving the system a “kick” and then measuring voltage versus time with an average. The kick can be a flash, an abrupt presentation of a pattern, a sudden movement in depth and etc. if the kick is flash it called Transient visual evoked potential (TVEP). In steady-state responses, you should stimulate repetitively. You “shake the system gently” and then wait until the transient response to the onset of shaking has finished. At this time, there may be a steady state response. On the other hand, maybe after waiting there is no response left. [13]

2.5.2. Steady-State visual Evoked Potential (SSVEP)

The steady-state visual evoked potential (SSVEP) is a visual cortical response evoked by a repetitive stimulus oscillating at a constant frequency. As a nearly sinusoidal oscillatory wave-form, the SSVEP usually contains the same fundamental frequency as the stimulus and some harmonics of the fundamental frequency. [2] The amplitude of the response is not the same for all stimulation frequencies. Flickering light at frequencies between 1 to 100 Hz in 1Hz step elicited steady-state oscillations with the largest amplitude at ~ 15 Hz (Herrmann, 2001). However, the SSVEP amplitude decreases as the stimulation frequency increases. [14] Compare to other types of BCIs, SSVEP based BCIs require fewer EEG channel and minimal user training. In addition, it has the higher information transfer rate (bit rate). [15]



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3. Methodes

For the experiments reported in this document, the EEG signals record from all the elec-trodes with standard 10-20 system. The recorded signals analyzed with two approaches: the first one is using the differential signal of two channels (Oz - Cz). The second one is using spatial filter to combine the signal of occipital channels to increase signal to noise ratio. In this project, the spatial filtered signal is a combination signal of O1, O2 and Oz.

3.1. Preprocessing

The EEG was recording with BIOSEMI system at a sampling frequency of 2048Hz. After importing BDF (Biosemi Data File) to Matlab, pre-processing has done by removing mean and by using notch filter to remove the effect of power line noise (50 Hz). The EEG was then resampled to 256 Hz.

3.2. Feature extraction

Feature extraction is an essential step in pattern recognition and machine learning prob-lems. Selection of proper features is necessary for successful classification.

3.2.1. Features

3.2.1.1. Energy

Energy represents the amount of amplitude of the signal in a specific frequency and the SSVEP energy defines the energy of the steady state visual evoked potential in stimuli frequency.

The steps to obtain energy of the SSVEP signal are:

SSVEP response are Whitened by autoregressive (AR) model of order four to increase signal to noise ratio.

Signal is Filtered using the band-pass filter, which is a FIR filter to have linear phase with 1Hz width, i.e. the cut off frequency were at stimuli frequency±0.5Hz.

By using peak-filter after band-pass filter, the energy feature calculates to compare the power of each class after classification.

The diagram shows the step of extracting energy as one feature (Figure 3.1)

Figure 3.1.Calculate Energy in stimuli frequency as feature

For extracting energy features, the sliding windows on SSVEP signal uses that it can depict in Figure 3.2.

SSVEP

Signal Pre-processing Whitened Energy

Signal Band pass Peak filter



Figure 3.2.Extract feature from signal by sliding window-the statistical properties like mean or mode is used to extract feature from each window

3.2.1.2. Phase

SSVEP like any sinusoidal waveform can be measured in terms of its amplitude and phase. The phase is a joint function of the stimulus frequency and the time delay between stimulus and brain response.

The steps to obtain the phase as a feature are:

Stimuli signal and the SSVEP response are Whitened by autoregressive (AR) model of order four to increase signal to noise ratio.

Both Signals Filtered by using the band-pass filter, which is a FIR filter to have linear phase with 1Hz width, i.e. the cut off frequency were at stimuli frequency±0.5Hz.

The Hilbert transform has used to obtain instantaneous phase difference between stimuli and SSVEP signals as one of the features. The Hilbert transform is a linear operator like FFT and it is useful for non stationary signals by expressing the frequency as a rate of change in phase, so the frequency can change with time.[16]

The above procedure to extract phase as a feature can see in below diagram (Figure 3.3):

Figure 3.3.Extract phase as a feature.

In this study, the phase difference between SSVEP response which is taken from bipolar combination(Oz-Cz) or signal after using spatial filter, and the stimuli signal which is recorded by photodiode is considered as a feature.

SSVEP Signal

Light

Signal

Pre-Processing (Remove mean, Resample, notch

filter)

Resample

Whitened Band pass Hilbert

Transform

Light Phase

SSVEP Phase

Delta-Phase Signal



19

Delta phase uses as feature due to these facts that:

The subtracting of the two phases between these signals abandons the time variable, so the synchronization is not necessary between training and operation sessions.

By using the phase difference, the change in the SSVEP phase distribution that produced by the possible phase deviation of stimuli is avoided.

For more details, assume the SSVEP response is and the Stimuli signal recorded by

photodiode is .

The Hilbert transform is obtained from these two signals which is the convolution of the signal

with , to get the instantaneous phase:

In the stimuli frequency and can be defined as followed equations,

The phase difference between SSVEP and stimuli signal at frequency and time is:

For extracting phase features, the sliding window on phase signal uses that it can depict in Figure 3.4.

Figure 3.4.Extract feature from signal by sliding window-the statistical properties like mean or mode is used to extract feature from each window

In addition, another example of two extracted features of signal versus each other for one subject depict in Figure 3.5.



Figure 3.5.Example of two extracted features of signal versus each other for one subject with 1-second window without overlap

Features extracted by sliding window place in a row of feature matrix and its label place in target matrix. The feature matrix contain feature of the signals in each column and consequently row in target matrix specified the label of the class with integer numbers, Here is Left (1), Top (2), Right (3), Bottom(4) and Center(5).



21

3.2.2. Feature enhancement

3.2.2.1. Spatial filtering

The detection of SSVEP response using multiple electrode signals present in [23]. In this way, each electrode signal have a special weights that multiply by it before combining the electrode signals to remove those electrodes which have less effect on SSVEP response. Therefore, the SSVEP response can magnify and the nuisance signal and noise cancel.

For the optimization SSVEP detection and extract only the SSVEP, those part of the SSVEP response signal that is not the part of SSVEP must remove like nuisance signal and the noise.

To become more obvious, (where indexes the electrode location) defines as a visual stimulation with flickering frequency:

The electrode signal is decomposed into three parts:

The evoked SSVEP response consists of a number of sinusoids with stimuli

frequencies and a number of harmonic frequencies.

Where t is a vector of sample indices, H is the number of harmonics that are considered in a model and are real numbers.

The set of the nuisance signal such as motion artefacts, power line interference

and concurrent brain processes. Weight factor , which is, added the nuisance signal to

each electrode signal.

Noise component is specific for electrode .

Equation 6 can generalize to the whole set of electrodes ( is the number of

electrodes)

Where is x matrix with sampled signals from all electrodes in a columns, is a noise

matrix constructs in same way, and contain the amplitudes and the weight factors for all sinusoid and nuisance signals, and for all electrode signals.

By combining the electrode signals linearly, the channel signal creates, where is x matrix containing the weights for each combination in its columns.

There is six method for combining electrode signals to channel signals and calculating that

are mentioned here, for more information refer to [17].

Average Combination

Native Combination

Bipolar Combination

Laplacian combination

Minimum Energy Combination

Maximum Contrast Combination



Due to that paper result, the classification accuracy of Maximum Contrast Combination method is better. In this method for increasing signal to noise ratio, the energy of the signal, , must be

increased and the energy of the nuisance signal and noise, , must be decreased. To achieve this, following equation must be solve:

The eigenvectors that belong to the smallest eigenvalues correspond to the smaller signal to noise ratio and the eigenvectors that belong to the largest eigenvalues correspond to the largest signal to noise ratio. For maximizing the measured signal and minimizing the nuisance signals and noise, the eigenvector that has the largest eigenvalues must select.

In order to select the training region, the segments where the SSVEP energy is larger and the interference of the other signals minimizes at the same frequency is considered. It is necessary to choose a window on energy distribution of visual area to calculate the spatial filter. The windows with specific length that have a largest energy at the SSVEP fundamental frequency in each stimulated region select as training regions.

For calculating the spatial filter, three electrodes (O1, Oz and O2) at the occipital region select to obtain training regions. These training regions use on eight electrodes on occipital region (P3, Pz, PO3, O1, Oz, O2, PO4, and P4) to calculate spatial filter coefficients. Therefore, eight possible spatial filter signals are exist. To obtain the one that perform better according to the maximum contrast combination method, the one that has the largest eigenvalues select.

The idea for testing the spatial filter is to calculate the probability of the detection (T+) of the SSVEP in the stimulated regions (S+) and the probability of the non detection (T-) in the non stimulated one (S-). All the possible classification of the data can see in Table 1.

Table 1.Data classification

Stimulated Region (S+) Non Stimulated Region (S-)

SSVEP Detected (T+) True Positive (TP) False Positive (FP)

SSVEP non Detected (T-) False Negative (FN) True Negative (TN)

The True positive rate means that the SSVEP detect in the stimulated region. The False Negative Rate means that the SSVEP not detect in the stimulated region. The False positive rate means that the SSVEP detect in the non-stimulated region. The True Negative rate means that the SSVEP not detect in the non-stimulated region.

The definition of sensitivity and specificity are as below:

Sensitivity: measures the quality of detecting the SSVEP component in the stimulated region and calculate as the ratio of SSVEP detected in the stimulated region to total SSVEP components exist in that region.

Specificity: measures the quality of detecting the non-SSVEP component in the non-stimulated region and calculate as the ratio of SSVEP non-detected in the non-stimulated region to sum of false detected SSVEP components and true detected non-SSVEP components in that region.



23

The Receiver Operation Characteristic (ROC) curve is used to study how good the spatial filter is. It plots the sensitivity versus the false positive rate (1-specificity). By varying the threshold and measuring the error in the stimulated and non-stimulated region, the curve is plotted.

Receiver Operation Characteristics (ROC) graphs indicates the performance of the classifiers clearly. It shows true positive rate as a function of false positive rate of a classifier system. ROC space is a square [0 1] * [0 1] that the x axis denotes False positive (1-specificity) and the y axis denotes True Positive (sensitivity). Suppose these two points in the square region: [FP1, TP1] and [FP2, TP2]. If FP1 < FP2 and at the same time TP1 > TP2, this relation is called domination ([FP2, TP2] dominated by [FP1, TP1]). It is obvious that the suitable ROC curve can be described by FP and by TP. FP must be as small as possible and TP should be as big as possible (almost one).the point [0 1] represents the best classification.

To compare different classifiers, using the Area under ROC curve (AUC) is a common method. AUC is a measure of test accuracy. The value of AUC is always satisfies the following inequalities:

If the AUC is close to one, it indicates a very good test. [20] In the other words, the ROC curve is near to the diagonal if the classes mix and difficult to classify, when the classes can separate from each other completely this curve is higher.

In this project, AUC of ROC curve used for selecting best stimuli high frequency for each subject and during selecting best spatial filter (See sections 4).

Example of two extracted features of signal versus each other for one subject with spatial filter depict in Figure 3.6.

Figure 3.6.Example of two extracted features of signal versus each other for one subject with spatial filter (1-second window without overlap)

Features extracted by sliding window place in a row of feature matrix and its label place in target matrix. The feature matrix contain feature of the signals in each column and consequently row in target matrix specified the label of the class with integer numbers, Here is Left (1), Top (2), Right (3), Bottom(4) and Center(5). As clearly visible in comparing Figure 3.5 and Figure 3.6, extracted features from enhancement signal can be separated completely.



3.3. Classification

The aim of the classification system is to define the object (i.e. the test data) in a correct target class, i.e. Classified the object into correct category. The block diagram of typical classifi-cation system shows below (Figure 3.7).

Figure 3.7.Classification system

3.3.1. Classifier

Different classifiers exist and can use for the classification like: Artificial Neural Network, Linear discriminant Analysis, Bayesian classifiers, Kernel machines, Support Vector Machines, k-nearest neighbour classifiers, Fuzzy classifiers, Rule-based classifiers. In this project, Neural Network classifier chooses. Neural networks are non-linear statistical data modelling tools to model complex relation-ships between inputs and outputs or to find patterns in data. Netlab toolbox and Matlab toolbox use to implement AAN single and multi layer classifiers in this project. The statistical techniques that considered in single layer network are linear regression and generalised linear models. In Netlab, Generalised Linear Model (GLMs) has shown with the abbreviation of glm. This model consists of a linear combination of input variables, in which the coefficients are the parameters of the model, followed by the activation function appropriate to the type of the data which being model. The architecture of single layer neural network without hidden layers consists of:

Number of Input Neurons i.e. the number of Features that are used

Number of Outputs i.e. the number of labels used as a targets

The network form c linear combination of the inputs, , to give a set of intermediate vari-ables ( is the number of output)

The model of the single layer Neural Network is in Figure 3.8.

Figure 3.8.Single layer Neural Network with two features and three classes

Input

Signal Pre-processing Classification Feature

Optimization

Feature

extraction Result

Signal Space Feature Space



25

Netlab implement three activation functions:

Linear Function for regression problem

Logistic Sigmoid Activation Function

Softmax Activation Function

o y is the value of an output node.

o is the net input to an output node.

For each of this choice of activation function there is a corresponding choice of error function.[19] In this project, the neural network with two phase and energy features as an input and five-class label as an output with Softmax activation function use to classify the data. (See section 5)



3.4. Information Transfer Rate

BCI operation is depending on some factors: experimental setup, the type and number of EEG features, the type of classifies and the target application. Any arbitrary combination of these factors can be a BCI system. For comparing different BCI systems, consistent evaluation criteria are necessary. If each component of BCI system can fail due a problem like bad EEG features, bad classifier and something like this, the whole BCI system may not work. It is very difficult to understand which component cause problem. To overcome this problem, the online and offline analysis must perform. [31]

In this section, the confusion matrix is introduced to calculate the information transfer rate and three methods of estimating information transfer are presented.

3.4.1. The Confusion Matrix

The result of -class classification problems describe best with the confusion matrix. The confusion matrix defines the relation between the actual output of the classifier (targets) and the output classes that user intended (true class).

The elements in confusion matrix show how many samples of class have predicted as class and the diagonal elements indicate the correct classified samples as class (True

Positive). The off diagonal, , show the number of incorrect classifier that classify as class

instead of class (False Positive). [31]

The definition of each cell of the confusion matrix probability shows in the Figure 3.9.

Predicted value

Output value (Y)

Actu

al valu

e

Ta

rge

t v

alu

e (

X)

Total

probability

Figure 3.9.The definition of each cell of confusion Matrix Probability-The diagonal elements indicate TP and the off-diagonal elements indicate FP (classes have the

same probability)

Let explain with a simple example to become more obvious. Suppose we have these two arrays as target and output values:

Target-Value = [1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4];

Output-Value = [2 2 1 3 4 3 3 4 1 2 3 4 2 2 3 4 1 2 3 4];



27

The confusion matrix and confusion matrix probability of the above example shows in Figure 3.10 and Figure 3.11.

2 2 0 1 5

1 4 1 0 5

1 0 4 0 5

0 0 1 4 5

3 6 6 5 20

Figure 3.10.Indicate the number of TP, FP, TN and FN

0.4 0.4 0 0.2 1.0

1 0.8 0.2 0 1.0

0.2 0 0.8 0 1.0

0 0 0.2 0.8 1.0

0.6 1.2 1.2 1.0 4.0

Figure 3.11.Confusion Matrix probability

In current work, confusion matrix elements use to calculate information transfer rate (See section 3.4.2).



3.4.2. Information Transfer Rate Calculation

The communication capacity (transformation rate) of current BCI systems is not sufficient to enable many applications. Investigation on new approaches in signal processing and classification is necessary to increase the information rate. Three different equations exist for calculating the information transfer rate [21]. The first, information transfer for M classes calculated by Farwell and Donchin (1988) as:

This is an upper limit for a discrete M-class system.

The second, according to Pierce (1980), Walpaw et al. (2000a) is defined as:

This equation requires the following to be true: M classes are possible, classes have the same probability, each class has the same accuracy and each undesired selection should have the same probability of selection.

The third, the information transfer rate can be estimated from the confusion matrix, which is a communication between input and output . The entropy of a discrete random variable is

defined as:

Nykopp (2001) calculated the information transfer rate for a general confusion matrix:

The mutual information calculates as:

o is the prior probability for class

o is the probability to classify as

The information for the previous two arrays example by Nykopp equation calculates and it is equal to 1.0308 (See 3.4.1).

In this thesis work, the stimuli section considers in order to calculate the information transfer rate. The start point of window is important and it calls shift. In this experiment, the shift is changing from zero to 3 seconds and the window size changes from 0.1 to 2 seconds. Different shift can be use by constant window size in each stimulus. It means that, the window is move during the stimuli part and remain part outside of the window is discarded.



29

Comparing between information transfer rate calculated by Walpaw equation (18) and Nykopp equation (23) shows that the information transfer rate based on Nykopp equation is more accurate. Since Nykopp uses the confusion matrix for calculating information transfer rate. In this study, the information transfer rate is calculated by Nykopp.

To explain this better, suppose the signal with stimuli and non-stimuli part. In each stimuli part, there is a window with the same shift and window size as it has shown in Figure 3.12 .From each window, feature extracted. These features use for classifier and after that information transfer rate (bit/trial) is calculated.

Figure 3.12.Calculate the information transfer rate and accuracy with the specific shift and window size, the blue arrows show the shift.



4. Experiment Procedure

This section introduces the equipment that use in this experiment for recording. At last, the way

of recording the EEG and light stimulus explain.

4.1. Recording Equipment

The system, which use in this study for recording is BioSemi active two. The BioSemi

Active Two measurement system is designed to measure differences in electrical potential from

the human or animal body surface. It has three components:

Active electrodes: The active electrodes have Ag-AgCl sintered tip, and a buffer amplifier with

an input protection circuit.

A/D box: The electrode signals amplified and converted from analogue to digital format in the AD

‑box.

Battery box: contains a sealed lead acid battery (6 Volt) and a shut-down circuit. When the

battery voltage is low, there is a red LED which illuminated on the AD-box front panel and there is

a pop of window on the computer screen. At this time there is 30-60 minutes operational time left.

The electrodes are placed on the surface of scalp according to the 10/20 standard by

using a cap to have the electrode in the right position. A conductive gel is used to have a good

connection between skin and electrodes. These electrodes are connected to the top of AD-box

from Input for channels 1 to 32 plus CMS/DRL with a cable.

The A/D box is connected to the computer via optic fibre cable that decuples the subject

from power line and a USB converter. The computer is responsible of the processing of the signal

from the amplifier.

In the system, there are two reference electrodes, The CMS (Common Mode Sense) and

DRL (Driven Right Leg) electrodes that when they are connected properly there is a blue LED on

the AD-box being illuminated. The CMS is located at the centre of the other electrodes (in case of

a typical EEG measurement it places on the top of the head). The DRL electrode can be located

anywhere on the body. The DRL and CMS electrodes are used to drive the subject to a potential

close to the AD-box reference.

It is possible to connect external sensors such as photodiode to the amplifier, in order to collect

data from environment synchronized to the EEG data.



31

4.2. Experiment Setup

In this experiment, four green LEDs (they put in the 10 cm x 10 cm box and the box was

covered with a panel), which were driven by square-wave currents that rendered by four Agilent

Functional Generators separately (Model: 33220A), are arranged in the same distance together

as shown in Figure 4.1. They all oscillate at the same frequency but different phase

( ).

Figure 4.1.Distribution of LEDs

The best frequency which each subject has response to it has selected by a procedure in

which the subject had to look at a flickering LED with four stimuli for each integer frequency from

40Hz to 32Hz by 1Hz step. The frequency changes from up to down to avoid the subjects fatigue.

Between each testing frequency, there is 30 seconds rest period. At the same time the EEG

signals was recorded to calculate the energy at the frequency after using spatial filter and ROC

curve the best frequency selects. The Area Under Curve is calculated from ROC curve for each

frequency. The largest AUC (Area Under Curve) has the better accuracy in classification so it

selects. The idea has used in this work is like this:

Assume two frequency and that , if the ROC curve of the frequency is more

than 90 percent, so is selected. If the ROC curve of the frequency is less than 90 percent,

between two frequency which have this condition, , the is selected due to

this fact that it is higher. The optimal frequency selects for each subject shows in Table 2.

Table 2.The optimal frequency selects for each subject

Subject Frequency

S1 40 Hz

S2 32 Hz

S3 39 Hz

S4 36 Hz

S5 39 Hz

S6 32 Hz

S7 39 Hz



Before experiment, luminance meter has used to measure the maximum luminance and

the background luminance Value. The maximum luminance was 982.5 nits and the background

Luminance was 75.59 nits by luminance meter Model LS-100 (Minolta camera Co. LTD). There-

fore, the modulation depth of these four LEDs calculated by (L max – L min) / (L max + L min)

was (982.5−75.59)/ (982.5+75.59) ×100% ≈ 85.71%. This correlated with the strength of SSVEP

response. [2] During experiment, photodiode has used in order to mitigate a phase shift, which

probably appears in the case of a long-term presentation of stimuli over time. The signal recorded

by photodiode use to calculate different phase between stimuli and SSVEP signals. A photodiode

can put near each of the LED. In this experiment, the photodiode places near the bottom LED

( ).

Eight subjects (two female and six male) aged 20 to 35 who had no nervous system dis-

ease (like epilepsy) participated in this experiment (named as S1, S2 … S8). Subjects had nor-

mal vision or adjusted-normal vision. All the subjects offered their informed consent and can quit

at any time. The subject seated on a comfortable chair about 50 cm away from the LEDs when

curtain has closed and lights were on. There is a speaker to tell the subject two seconds before

starting stimuli to look at which of directions. The commands tell to look at one of the five direc-

tions: Left, Top, Right, Bottom and Centre (No attention).

After each three seconds of the stimuli, which shows in Figure 4.2, there was a random-

ized rest time between 4-6 seconds as no light. When the stimuli start, the subject must focus

attention to one of the LED without a large movement as commands that speaker told (Only eyes

move to that direction, subjects advised to blink or move in the No Light Period).

Figure 4.2.The sounds hear from speaker two seconds before starting the stimuli to

tell the subject to be ready for looking at the new direction

Subjects participated in four sessions of recording. Each session is consisting of ten

groups that one group is include of random sequence of five directions. (Each direction can

appear several times with same number as stimuli in one session). The first session is used for

training and remain three sessions are used for testing. In total, the number of stimuli for training

is 50 and the number of stimuli for testing is 150.

The EEG data collects by a BIOSEMI active- two system at a sampling frequency of

2048Hz in a normal office environment. Thirty-two Electrodes used according to international 10-



33

20 system (Figure 4.3).

Figure 4.3.EEG electrode placement according to the 10-20 system



5. Result and discussion

Since SSVEP response, is subject dependent, result of phase classification for each

subject discussed separately in this report. The result of one of the subject that has best SSVEP

response shows here and discussion of the other subjects explain in appendices. Results for

each subject organized as below:

1. In section 0, SSVEP Energy signals versus phase of each subject shows and discuss if it

is visually good or not (Using Oz-Cz signal) for train and test trial.

2. In section 5.2, SSVEP Energy signals versus phase of each subject shows and discuss if it

is visually good or not (Using spatial filter signal) for train and test trial and SSVEP En-

ergy signals of each subject shows and discuss if it is visually good or not (Using Oz-Cz

signal and spatial filter) for train trial.

3. In section 5.3.1, Classification results of Oz-Cz or spatial filter discuss by using sliding win-

dow (Asynchronous method). For window size between 0.1 to 2.7 seconds, accuracy and

information transfer rate calculate with different overlap (0, 0.25, 0.5 and 0.75)

4. In section 5.3.2, to discover best part of stimuli, which has the best SSVEP response, the

delay between stimuli and SSVEP response calls shift defines (Synchronous method).

For the shift between zeros to 3 second, accuracy and information transfer rate discuss

with different window sizes (0.1-2 second).

5. The results for all subject discussed to get a conclusion for using asynchronous and syn-

chronous method.



35

5.1. Phase and energy of one subject (Oz-Cz)

Features versus each other’s depicts in Figure 5.1 for subject 1 for train and test trials with Oz-Cz

derivation. As it shows the features can not separate from each other’s.

Figure 5.1.Feature energy versus feature phase with Oz-Cz

5.2. Feature enhancement (Spatial filter)

Features versus each other’s depicts in Figure 5.2 for subject 1 for train and test trials with spatial

filter. As it is considerable, with spatial filter the features can separate from each other’s

completely. This subject has the best SSVEP response in 40Hz frequency.



Figure 5.2.Feature energy versus feature phase with Spatial filter

As it is significant in Figure 5.3, the energy of Oz-Cz is less than the energy of spatial

filtered of SSVEP signal. As it is obvious, the spatial filter decreases the SSVEP energy of center

and no light sections. The algorithm, which use for spatial filter discussed in section 3.2.2.1. The

energy obtained from peak-filter after band-pass signal as explained in section 3.2.1.1.

Figure 5.3.Compare the energy of Oz-Cz and the spatial filter SSVEP signal for S1



37

5.3. Classification and bitrates

5.3.1. Asynchronous method

In Figure 5.4, information transfer rate has shown by changing window size from 0.1 to 2.7

second with step of 0.1 second by four different overlaps. The y-axis indicates the information

transfer rate. As it can show the bit/trial results for this subject with spatial filter is more than the

Oz-Cz combination. It is obvious that the information transfer rate for window size of 0.9 second

with overlap of 75 percent in spatial filter is more than other overlaps results for subject 1.

Figure 5.4.Information transfer rate result with different window size (0.1-2.7sec)

with different overlap for subject 1-red indicates spatial filter and blue indicates Oz-

Cz combination

The best result for Information transfer rate and accuracy with window size of the 0.9 second and

overlap of 75 percent is indicated in Figure 5.5. The bit/trial for spatial filter is more than the Oz-

Cz combination. As it is considerable, the maximum location of bit/trial is not the same as the

maximum location that has the most accuracy.



Figure 5.5.The best result for Information transfer rate and accuracy

Energy feature versus phase feature for train and test trials with Oz-Cz combination and spatial

filter are depicted in Figure 5.6. As it is considerable, the phase difference after spatial filtered

signal is better than Oz-Cz.

Figure 5.6.Display energy versus phase feature with Oz-Cz combination and after

spatial filtered signal for best window size and overlap-Left figures show, the Train



39

and test with Oz-Cz and Right figures show the train and Test trials after spatial

filtering for S1

This figure depicts that spatial filtering signal improves the possibility of making phase separate

from each other’s.



5.3.2. Synchronous method

The result of comparing bit/trial for Oz-Cz and spatial filtered signal with all window size and

different shift for subject 1 shows in Figure 5.7.

Figure 5.7.Calculated bit/trial for spatial filtered signal (red) and Oz-Cz signal (blue)

for subject 1 with different window size (0.1 to 2 sec)

To find out the best shift for different window size, the information transfer rate calculates

as explains in section 3.4.2. By comparing different shift for one window size, the one that has

the best information transfer rate select and summarize in Table 3. The information transfer rate

(Bit/trial), compare between Oz-Cz combination and after spatial filtered signal.



41

Table 3.The stimuli place that have the most bit/trial for subject 1-40Hz with different

window size and best shift

Window size

(second)

Shift

(second)

Bit/Trial

Oz-Cz

Bit/Trial

Spatial filter

Window size

(second)

Shift

(second)

Bit/Trial

Oz-Cz

Bit/Trial

Spatial filter

0.1 1.8 0.1 2.07 1.1 1.5 0.09 2.14

0.2 1.7 0.07 2.11 1.2 1.4 0.08 2.14

0.3 1.6 0.11 2.11 1.3 1.7 0.1 2.14

0.4 2.1 0.06 2.09 1.4 1.6 0.07 2.14

0.5 1.8 0.1 2.18 1.5 1.4 0.07 2.2

0.6 1.8 0.09 2.18 1.6 0.7 0.08 2.16

0.7 1.7 0.09 2.16 1.7 0.7 0.09 2.16

0.8 1.7 0.14 2.16 1.8 1.1 0.13 2.2

0.9 1.5 0.09 2.12 1.9 0.7 0.14 2.18

1 1.4 0.06 2.12 2 1 0.21 2.16

As it considers in Table 3 and Figure 5.8, the 1.4-second shift with 1.5-second window

has most bit/trial for spatial filter. The information transfer rate calculated with spatial filter is more

than Oz-Cz combination. Therefore, after spatial filter the information transfer rate is improved.

For the same window size and the same shift, the result of spatial filter is much better than Oz-Cz

derivation. Between these two window-sizes, 1.5-second and 1.8-second the first one is selected

in order to get better bit/second.

Figure 5.8.The best Information Transfer rate and test accuracy for subject one



The maximum amount that could be discarding or shifting from the stimulus part is the difference

between stimulus duration and window size. Therefore, in above figure the maximum shift for 1.5-

second window can be 1.5- second.



43

5.4. Discuss result of all subjects

The phase difference between SSVEP and light signal extracted from 1-second EEG recorded is

depicted in Figure 5.9 for one subject when focused on each of the four LED by using Hilbert

transform that mentioned in section 3.2.1.2. Therefore, the phase difference value can use to

know which LED the subject focuses on.

The instantaneous phase difference between SSVEP and stimuli signal for one second EEG

recorded for one subject depicts in Figure 5.9. The subject has good SSVEP response by

focused attention on one of the four LEDs in this one second. As it is visible, Using Delta Phi can

cancel the phase shift of the SSVEP Response.

Figure 5.9.Comparison of Instantaneous phase difference between SSVEP and

stimuli signal for 1-second EEG record

As you can see the Figure 5.10, the topographical pictures are not the same for all

subjects. It is represent that the coefficient of spatial filter (w) is different for each subject, so the

spatial filter is subject dependent. Red and blue part of the topography indicates channel signal

with more coefficient weight in spatial filtered signal, green parts has not any effect.



Figure 5.10.Compare spatial filtered signal for subjects

As it is considerable in section, 3.2.2.1 the ROC curve is used to select the best coefficient of the

spatial filter. The best ROC curve that selected for each subject shows in Figure 5.11.

Figure 5.11.The best ROC curve for each subject

The Area Under Curve (AUC) for best ROC curve of each subject depicts in Table 4.



45

Table 4.The AUC for the best ROC curve for each subject

Subject AUC (percent)

S1 94.96

S2 86.17

S3 92.20

S4 85.72

S5 71.05

S6 81.47

S7 93.49

The best information transfer rate for each subject with Synchronous and Asynchronous methods

for Oz-Cz combination and spatial filter summarize in Table 5.

Table 5.The best information transfer rate in Synchronous and Asynchronous

methods for each subject

Subject Oz-Cz Spatial filter

Bit/trial Async Bit/trial Sync Bit/trial Async Bit/trial Sync

S1 0.26 0.94 1.68 2.2

S2 1.2 1.47 1.01 1.34

S3 1.27 1.57 1.17 1.47

S4 0.54 0.84 1.18 1.42

S5 0.61 0.92 0.58 0.79

S6 0.45 0.63 1.04 1.14

S7 0.38 0.48 1.46 1.72

S8 0.51 0.93 0.84 1.04

Mean ± std 0.65±0.37 0.97±0.37 1.12±0.34 1.39±0.43

As it is considerable in Table 5 the information transfer rate in spatial filter is better than Oz-Cz

for all subject except subject 5 that has not good response. Also in subject 3 and 5, because in

these subjects the small occipital region has response, the spatial filter has not improvement

more than Oz-Cz. The achieved bit/trial with Synchronous method is more than Asynchronous.

The details of obtaining this result discuss in section 5.3.

The place of SSVEP response that has the best bit/trial for Asynchronous and

Synchronous method for each subject is summarized in Table 6.



Table 6.The location of best information transfer rate with Asynchronous and

Synchronous method for each subject

Subject

Oz-Cz Spatial filter

Asynchronous Synchronous Asynchronous Synchronous

Window size

Overlap Window

size shift

Window size

Overlap Window

size Shift

S1 1.6 0 0.2 0.3 0.9 0.75 1.5 1.4

S2 1.7 0.75 0.9 1.4 1.1 0.75 1 1

S3 2 0.75 0.1 0.8 0.9 0.75 2 0.4

S4 2.7 0 0.1 2.9 2.7 0 1.8 0.9

S5 1.1 0.75 0.1 0.7 2 0 0.8 0.3

S6 1.6 0 0.7 0.3 1.5 0 1 0.4

S7 2.7 0 1.2 0 2.7 0 0.4 0.4

S8 2 0 0.4 0.5 1.7 0.5 0.9 0.6

Mean ± std 1.92 ± 0.55 --- 0.46± 0.42 0.86±0.92 1.68±0.73 --- 1.17±0.54 0.67±0.38

As it is visible the location that have the best SSVEP response with the most bit/trial result is

subject dependent in both Asynchronous and Synchronous methods. This shows clearly that the

SSVEP response is subjected dependent. However, the following results can obtain from the

table.

In Asynchronous method, the average best window size is more than 1.6 second for both

Oz-Cz and spatial filter.

In Synchronous method, the average window size is about 0.4 second for Oz-Cz and

about 1 second for spatial filter.

In Synchronous method, the average shift is 0.8 second for Oz-Cz and 0.6 second for

spatial filter.



47

Table 7.Average bit/trial for each subject

Window Size

Information Transfer Rate

Oz-Cz Spatial Filter

Synchronous Asynchronous Synchronous Asynchronous

S1 0.22±0.21 0.15±0.05 1.86±0.27 1.50±0.08

S2 1.06±0.25 0.99±0.15 0.98±0.20 0.92±0.06

S3 1.31±0.17 1.12±0.07 1.06±0.22 0.98±0.10

S4 0.52±0.15 0.41±0.06 0.96±0.32 0.93±0.13

S5 0.55±0.17 0.46±0.06 0.45±0.15 0.41±0.07

S6 0.42±0.10 0.33±0.05 0.72±0.23 0.75±0.18

S7 0.20±0.07 0.20±0.10 1.28±0.14 1.23±0.12

S8 0.39±0.19 0.32±0.08 0.65±0.19 0.67±0.10

As it shows in Table 7, the averaged information transfer rate for asynchronous and synchronous

methods is almost the same for each subject. Also, it is visible that the averaged information

transfer rate with synchronous method by spatial filter is more than the others.

Analyzing with synchronous method, show the best window sizes that could be used.

Analyzing with synchronous method, shows in which part of the stimuli the subject has

good response and if the user doesn’t focus in some stimulus part.

Use the Best window size to decrease the time of stimuli.

By averaging Information transfer rate of synchronous (equal window size and different

shift) and asynchronous (equal window size and different overlap) methods, results are

almost the same and they somehow prove each other.



6. Conclusion

The phase in high frequency band (32Hz-40Hz) can be use to increase the number of

possible commands in BCI operation. (only one of the subject has not response)

The Hilbert transform (phase synchrony) effectively extracts the phase difference(see

section 5.4)

Spatial filter can improve the classification accuracy

It is represent that the coefficient of spatial filter (w) is different for each subject, so the

spatial filter is subject dependent.

Information transfer rate calculated by Nykopp equation is more precise than Walpaw

equation.

Information transfer rate (bit rate) by calculating spatial filter is more than Oz-Cz

derivation

The result of information transfer rate achieving with spatial filter in both Synchronous

and Asynchronous compare to Oz-Cz combination, prove the advantage of spatial filter

enhancement.

The maximum information transfer rate achieved in this project is 2.2 for subject 1.

Information transfer rate for Synchronous method with spatial filter is 1.39±0.43 and for

Asynchronous method, is 1.12±0.34 in average.

In Asynchronous method, the average best window size is more than 1.6 second for both

Oz-Cz and spatial filter.

In Synchronous method, the average window size is about 0.4 second for Oz-Cz and

about 1 second for spatial filter.

In Synchronous method, the average shift is 0.8 second for Oz-Cz and 0.6 second for

spatial filter.



49

7. Future work

In this study, only three electrodes use to train spatial filter signal in the occipital region,

the spatial filter performance could be better by using more combination electrodes around the

occipital region use for training. Since, spatial filter is subjected dependent, for each subject can

use a specific combination of electrodes.

In this experiment, the SSVEP phase enhancement with spatial filter is studied to

separate the phase, as a future studies it is good to find a way to maximize the phase separation

probability.

The distance between LEDs in experiment setup can be study to determine the least

good distance to overcome the interference of phase.



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