Recent Advances in EEGRecent Advances in EEG

T h l d A li tiTechnology and Applications

Tzyy-Ping Jung

Swartz Center for Computational Neuroscience and Center for Advanced Neurological Engineering

University of California San DiegoUniversity of California San Diegoand

Department of Computer ScienceNational Chiao-Tung University, Hsinchu, Taiwan

1 In spite of e iting ad ances in ne oscience

Serious Problems with My Work

1. In spite of exiting advances in neuroscience,

mathematical modeling and neurotechnology, the

current and dominant emphasis of this field failed to

fully capitalize the potential of neuroscience and

neurotechnology to address questions, needs andneurotechnology to address questions, needs and

applications outside of research settings.

2. My work has done very little, if any, to help needed

peoplepeople.

Current Limitations

• The lack of portable and user-acceptable systems p p yfor monitoring brain and body dynamics;

• The lack of mathematical mining and modeling methods to find statistical relationships betweenmethods to find statistical relationships between moment-to-moment variations in environmental, beha io al and b ain d namic eco dingsbehavioral, and brain dynamic recordings;

• How can we leverage scientific principles and• How can we leverage scientific principles and advanced technologies?

Neuroimaging modality for Neuroergonomics

PET MEGfMRIEEG

• In all modalities but EEG, the sensors are heavy.

• EEG is the only modality that does not require the y y qhead/body to be fixed.

• EEG might enable the monitoring of the brain functions g gof unconstrained participants performing normal tasks in the workplace and home. However, …..

EEG Electrodes

In common applications, EEG signals are measure by an electrode with electrolyte gel placed directly on the skin.

The coupling between skin and electrode can be described as a layered conductive and capacitive structure, with series combinations of parallel RC elements

Equivalent circuit elements.

EEG Electrodes

Comparison of electrical coupling of the skin-electrode interface between electrodes

Typically, one of the RC sections dominates and the electrical coupling may be simply represented as a single element with conductance gc in parallel with

it C Y (j ) j C

From Chi, Jung & Cauwenberghs, in press.

capacitance Cc , Yc(jω) = gc + jωCc.

EEG Electrodes

The conventional notion that low resistance (high conductance) is essential for good electrode performance could be misleading in certain cases.g p g

Source input-referred noise

Source input-referred noisepower density:

Vs, rms can be reduced to zero in two limits: either infinite coupling ,conductance (low-resistance contact sensing), or infinite coupling impedance (capacitive noncontact sensing). This presents a rather interesting dichotomy—either of the two extreme cases of zero resistance g yand infinite resistance of skin-electrode contact are actually optimalfor low-noise signal reception

Practical Design Considerations

•To abrade the skin to obtain a low contact resistance (5–10k ).( )

•To employ an amplifier with very high input impedance such that that the skin-electrode impedance becomes negligible.that the skin electrode impedance becomes negligible.

Comparison for EEG Electrodes

Standard wet electrodes : low skin impedance, and buffer the electrode against mechanical motion. But, they may be messy, time-consuming, irritating and the signal quality degrades over time.

Rigid metal electrodes: subject to motion artifacts

Dry foam electrode (Gruetzmann et al., 2007): comfortable and stable with increased resistance to motion artifact.

MEMS sensors: low skin impedance. But, they may be irritating and difficult to penetrate the hairs.

Microprobe electrodes: sensitive to motion artifacts.p

Non-contact sensors: sensitive to motion artifacts, poor settling times. Friction between the electrode and insulation g(e.g. hair) can cause large voltage excursion at the sensitive input.

Comparison between EEG Signals collected by Wet and dry Electrodes

Dry MEMS Sensor

Correlations between EEG acquired by a conventional and dry MEMS electrodes

1,000 seconds (4,000 pt.)

Novel Dry Sensors for Hairy Sites

No preparation, no gel, through the hair

Fast and low-cost fabrication (US$50 less than US$0.5)

Flexible substrate + Once-forming a set of dry electrode Flexible substrate + Once forming a set of dry electrode fingers -> easily contact with scalp

Flexible Substrate (Silicone)

Once-formingMulti-fingers

Scalp

Comparison EEG signals between using Wet and Novel Dry Sensors in Occipital Area

50

100

ro V

olt)

0.5

1

Correlation

0

mpl

itude

(mic

r

0

Cor

rela

tion

0 0 5 1 1 5 2 2 5-100

-50E

EG

Am

0 0 5 1 1 5 2 2 5

-1

-0.5

C

WetDry

40 Correlations between EEG acquired by a conventional and dry electrodes

0 0.5 1 1.5 2 2.50 0.5 1 1.5 2 2.5Time(sec)

20

30

Th l ti f t t l 96%

10

0 The correlation of total 96% data are greater than 0.97. (non-filtering raw data)

0.94 0.95 0.96 0.97 0.98 0.99 10

Correlation Coefficient

Novel Dry Foam-based EEG Electrode

Decreased Impedance Low Movement Artifacts In forehead and hairy site, the correlation with wet

electrode all are over 90% similarity. L t 0 5 US/ it d t Low cost <0.5 US/unit and easy to wear MRI Compatible

Polymer Foam + Conductive Fabric

Mobile & Wireless EEGLaboratory EEG

NCTU’ MW EEGNCTU’s MW-EEG

D MEMS Bi A & ADCDry MEMS EEG Sensor

Bio-Amp & ADC

μcontrollerμcontrollerand Bluetooth

Specification of MINDO

EEG Headband

F Di ib d Ci iFeatures Distributed Circuits

MiniaturizatioDAQ: 20 x 18 (2 or 4 pieces)

Miniaturization

Size (mm) MCU: 40 x 25

Weight < 100 gS li R 512HSampling Rate 512Hz

Bandwidth Filter to 1 - 50 Hz

Gain 6000 timesOutput current

(working) 31.58 mA(working)Battery Life

(3.7V, 1,100mA) 33 hours

Current Limitations

• The lack of portable and user-acceptable systems p p yfor monitoring brain and body dynamics;

• The lack of mathematical mining and modeling methods to find statistical relationships betweenmethods to find statistical relationships between moment-to-moment variations in environmental, beha io al and b ain d namic eco dingsbehavioral, and brain dynamic recordings;

• How can we leverage scientific principles and• How can we leverage scientific principles and advanced technologies?

A Typical Event-related Potential ExperimentExperiment

Difficulties in Observing Distributed EEG dynamics

Scalp EEG data

CortexLocal

Synchrony

Local SkinDomains of

synchronySynchrony

y y

Scalp EEG signals appear to be noisy because they each sum anoisy because they each sum a mixture of signals generated in

many brain areas.y

Scott Makeig / UCSD 05/08

From Jung et al., 2000.

Modeling and Mining Distributed EEG Dynamics

Cocktail Party

Independent component analysis (ICA) pioneered for EEG by Makeig, Bell, Jung &

Blind source separation by ICA p y g, , g

Sejnowski (1996), separates high-density EEG signals into their source contributions.

y

ICA separates brain and non-brain signals contributing to high-density EEG recordings This allows direct

Cortical source projections

EEG recordings. This allows direct analysis of distinct source activities in response to stimulus presentation and subject’s behavior.subject s behavior.

Cortical source localization

Subject BEM head model

Advanced forward head models allow advanced inverse models based on cortical topology to identify the sources of the scalp

localization

S. Makeig, 2007

p gy y pEEG recordings and to study their interrelationships.

Miniaturization of EEG Acquisition and On-Line processing a d O e p ocess g

I t t ti A lifi & Si D lt ICAInstrumentation Amplifier &Band-Pass Filter

Sigma-DeltaADC

ICAsignal process

DSP

Application

On-Chip Signal Processing

A VLSI Implementation of a 4-Channel Independent Component Analysis

ProcessorProcessor

Department of Electronics EngineeringNational Chiao Tung University, Taiwan

Prof. Wai-Chi Fang Teamwfang@mail nctu edu twwfang@mail.nctu.edu.tw

System-on-Chip Design and System Integration Laboratory

24

System Architecture of Mobile & Wireless BCI

Dry MEMS Sensors

EEG DAQ, Amp, and ADC

Wireless telemetry y

Wireless receiver

Wearable on-line signal processing

Visualization & feedback receiver signal processing & feedback

Work-in-Progress

VLSI implementation of bio-amp, ADC and wireless moduleVLSI implementation of bio amp, ADC and wireless module 16-channel High-end EEG measurement systemDSP PCB -> On-chip Signal Processing (SoC)p g g ( )

RAM_BRAM_ASDMA

RAM_BRAM_ASDMA

Core_A Core_BCore_A Core_B

Front-end chip: Bioamp+ filters + ADC Back-end Chip: Dual Core DSP

Current Limitations

• The lack of portable and user-acceptable systems p p yfor monitoring brain and body dynamics;

• The lack of mathematical mining and modeling methods to find statistical relationships betweenmethods to find statistical relationships between moment-to-moment variations in environmental, beha io al and b ain d namic eco dingsbehavioral, and brain dynamic recordings;

• How can we leverage scientific principles and• How can we leverage scientific principles and advanced technologies?

What is a BCI?A brain–computer interface (BCI), sometimes called a

What is a BCI?b a co pute te ace ( C ), so et es ca ed a

direct neural interface or a brain–machine interface, is a direct communication pathway between a brain and an

l d i BCI f i d i iexternal device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functionsfunctions.

Wolpaw et al. 2002.

BCIs for communication and device control

• ECoG -based BCIs.

ECoG control of vertical cursor movement using specific motor imagery to

th dmove the cursor up and rest to move it down. Modified from LeuthardtModified from LeuthardtEC et al, 2004.

BCIs for communication and device control

• EEG-based BCIs(A) P300 event-related

i l Cpotential BCI.

(B) Sensorimotor(B) Sensorimotor rhythm BCI.

(C) S d i l(C) Steady-state Visual Evoked Potential

P300 BCIsP300 BCIs

• Farwell and Donchin 1988

• P300 Speller

Mu BCIsMu BCIs

SSVEP BCIs

Photic driving

tStimulus >6Hz

t tSteady-state VEP

SSVEP BCIs

Passive or Affective BCIsPassive or Affective BCIs

(A) Cognitive state monitoring and management

(B) Emotional state estimation(B) Emotional state estimation

(C) Attention, intention

(D) Clinical applications, e.g. seizure detection and/or

prediction neurorehablitationprediction, neurorehablitation

(E) etc.

Testing the Mobile & Wireless BCI in Testing the Mobile & Wireless BCI in a VRa VR--based Dynamic Driving Simulatorbased Dynamic Driving Simulatora VRa VR based Dynamic Driving Simulatorbased Dynamic Driving Simulator

Implemented of Cognitive-state Monitoring on the DSP ModuleMonitoring on the DSP Module

4-channel EEG

Down-sampling

ICA(multiplying W to the data)

ICA Training

Copy the

Removing artifactual components

pynew W

when ICA convergescomponents

FFT

Auditory feedback to the subject FFT

Drowsiness

39

Drowsiness Detection

EEG-based Cognitive-Status MonitoringD El t dWet Electrodes Dry Electrodes

DSP and Display module

2 5 x 1 5 in2.5 x 1.5 in

Lin at. al., Proc. of the IEEE, July 2008.

Acknowledgement