April 30th, 2010 M.Sc. Project Presentation 3 - … novel MEMS EEG sensor for drowsiness ... Conventional Wet Electrode (BIOSEMI) & Prototype Electrode (ENOBIO ... 2010 M.Sc. Project

M.Sc. Project for Biomedical Engineering

By: SHAN Qing

Student ID: 08069470

Supervised By: Prof. Wen J. LI

Date: April 30th, 2010

OutlineBasic Knowledge on EEG

• Brain Waves

• EEG Measurement

• EEG Applications

Existing EEG-Based Motion Control Technology

• Overview

• Case I: Using Mu and Beta Rhythm

• Case II: Using Event-Related Evoked Potential

• Conclusion

How Can MEMS Technology Help

• Problems of existing commercial electrodes

• Case I: MEMS Based Silicon Spiked Electrode Array

• Case II: Dry Electrode Using CNT Arrays

• Summary on the Advantages of MEMS EEG Electrodes

Future Potentials

Conclusion

Q & A

April 30th, 2010 2M.Sc. Project Presentation


Brain Waves

• Amplitude ranging from 0.5 ~ 100µV (peak to peak)

• Cerebral EEG falls in range of 1 ~ 20Hz

• Categorized based on frequency

• Beta : > 13Hz; Alpha: 8 ~ 13Hz; Theta: 4~8 Hz; Delta: 0.5 ~ 4Hz

Basics*

• Dominant during normal state of wakefulness with open eyes

• Closely linked to motor behavior

Beta Wave*

• Induced by closing eyes and by relaxation

Alpha Wave*

• Alpha-range Activity that is seen over the sensorimotor cortex

• Characteristics relates to the movement of the contralateral arm or mental imagery of movement of the contralateral arm

Mu Rhythm*

April 30th, 2010 4M.Sc. Project Presentation* Reference [1-3]

EEG Measurement


The electroencephalogram (EEG) is defined as electrical activity of an alternating type recorded from the scalp surface after being picked up by metal electrodes and conductive media.*

Types of Electrodes*

• Disposable (pre-gelled, and gel-less)

• Reusable Disc Electrodes (require conductive gel; usually made of Ag/Ag-Cl)

• Saline Electrode

• Headbands and Electrode Caps

• Invasive Needle Electrodes

Figure 1. EEG Measurement Setup*

* Reference [1-3]

Neuromarketing*

EEG Applications


ITEM PRICE

EPOC USD 500

Force Trainer

USD 60.54

MindFlex USD 79.99

Video 1. MindFlex*

Clinical Applications*

• Monitor alertness, coma, and brain death

• Locate areas of damage after head injury, stroke, tumor, etc.

• Investigate epilepsy and locate seizure origin

• Etc.,

* Reference [1][7][8]


Overview


Alternatives

Motion Control based on Mu and Beta Rhythm

Motion Control based on Event-Related Potentials

Motion control through recognizing the brain’s intentions from EEG is probably NOT possible in the foreseeable future*

Complexity of signal Degraded signal Extremely variable signal

* Reference [6][9]

Case I: Using Mu and Beta Rhythm


This case demonstrated a noninvasive Brain Computer Interface (BCI) that can provide 2D motion control using beta and mu rhythms recorded from the scalp. The project is carried out in the Wadsworth Center at State University of New York.*

• People can learn to control certain features of the EEG

• Alpha Wave can be induced by relaxation (Working principle of Force Trainer and MindFlex)

• Mu and Beta Rhythm wax and wane in association with actual movement or imagination of movement

Over 60 Years of Study

* Reference [4][10]

Case I: Using Mu and Beta Rhythm


Figure 3. Topographical and Spectral Properties of User’s EEG Control*

Figure 2. Study Protocol*

RH RV: Right Side Amplitude LH LHV: Left Side AmplitudewRH wLH : Weight aH aV bH bV: Tunable Coefficient

* Reference [10]

Case I: Using Mu and Beta Rhythm 4 Subjects Participated Performance gradually improved over training sessions


Figure 4. Cursor Trajectories of Users* Video 2. Cursor Controlled by User*

* Reference [10]

Case II: Using Event-Related Potentials

• Measured brain response that is directly the result of a thought or perception. *

• Any stereotyped electrophysiological response to an internal or external stimulus.*

• No need to provide motor or verbal response

• Focusing on the change in electrophysiological signal that occurs immediately following the stimulus event

What is Event-Related Potentials (ERP)?


Project done by Universidad de Zaragoza, Spain. The team developed a non-invasive brain-actuated wheelchair that relies on P300 neurophysiological protocol.*

* Reference [5][11]



Figure 5. Module Diagram of the Brain-Actuated Wheelchair*

Training Process

• Present to all possible stimulus

• EEG signal after the stimulus will be recorded

• Feature Vector will be extracted for each stimulus

Real-time Recognition Process

• Options flash one by one

• EEG signal after each stimulus will be taken to compare with the feature vector of that stimulus type

• Stimulus of the greatest probability will be regard as the choice

* Reference [11]

• The system was used and validated by five healthy subjects in three consecutive steps: screening, virtual environment driving and wheelchair driving sessions

• All subjects accomplished two different tasks with relative easiness

• Recognition accuracy of higher than 90% within one hour

Experimental Result

April 30th, 2010 M.Sc. Project Presentation 14


Figure 6. Subject Navigating along Hallway by Concentrating on Graphic Interface on Screen*

* Reference [11]

Conclusion


Frequency Domain Analysis

Time Domain Analysis

Amplitude of the EEG in a particular frequency band

(Rhythm)Commands

The form or magnitude of the voltage changed evoked by a stereotyped

stimulus (Evoked Potential)Commands


Problems of Existing Commercial Electrodes


Require conductive gel for adhesion and lowering electrode-skin interface impedance

Require careful skin preparation before experiments

• Conductivity of gel gradually decrease due to the hardened of gel

• Uncomfortable and inconvenient

• Cause skin red and itchy feeling

Case I: MEMS Based Silicon Spiked Electrode Array

Designed to pierce the stratum corneum (SC) into the electrically conducting tissue layer stratum germinativum (SG), but not reach the dermis layer so as to avoid pain or bleeding.*


A novel MEMS EEG sensor for drowsiness detection application. Joint project between National Chiao-Tung University, Taiwan & University of California, San Diego.

* Reference [13]



Figure 8. SEM of the Fabricated Dry Electrodes*Figure 7. Fabrication Process*

• Fabricated on a silicon wafer with high aspect ratio

• Thick PR film patterned with circular dots to provide etching hard mask

• Isotropic etching to obtain probe tip

• Anisotropic etching to obtain probe shaft

• Wet etching to release hard mask at the probe tip

• Coat Ti/Pt using DC sputtering

• Mounted on flexible PCB using silver glue

Fabrication Process*

* Reference [13]



Figure 10. The EEG power spectrum*

Figure 9. Positions of Wet and Dry Electrodes & Raw EEG Data Recorded*

* Reference [13]

• The recorded signals by dry electrodes are extremely comparable to those obtained by corresponding wet electrodes

Results

Case II: Dry Electrode Using CNT Arrays

This project team aimed at designing a dry electrophysiology sensor using CNT to eliminate skin preparation and gel application requirements in order to reduce noise while improving wearability.*


A new dry electrode sensor for surface biopotential applications. The project is done by University of Barcelona, Spain and University of Surrey, UK.

* Reference [14]


• The MWCNT arrays were mounted on commercial active electrodes with onsite amplifiers and connect to commercial off-the-shelf recording equipment

• MWCNT arrays were grown on highly doped Si substrate using plasma-enhanced chemical vapor deposition (PECVD) of acetylene over an iron catalyst

Prototype


Figure 11. Electrode Prototype Design*

Figure 12. Electron microscope image of the MWCNT array** Reference [14]


• Noise measured by the new electrodes is low and rather similar to that of the commercial electrodes

• Human tests carried out for both spontaneous EEG recording and ERPs

Test Results


Figure 13. Conventional Wet Electrode (BIOSEMI) & Prototype Electrode (ENOBIO)* * Reference [14]

Advantages of MEMS EEG Electrodes

No Longer needs Conductive Gel

Allow smaller electrode size

Higher selectivity

Decrease electrode-skin interface impedance



Future Potentials

• More promising way at this stage ERP

• Drawback of ERP long decision time

• Drawback of ERP training required

• To start with BCI 2000

• Integrate with Gyro or Accelerometer

3D Motion Control

• Dry Electrodes

• Alternative electrode designs

• Corresponding Amplifier and Connector Design

MEMS Application



Conclusion

No evidence proving that it is possible to extract the exact intensions of human beings from their EEG signals

Promising alternative approaches

• the amplitude of rhythms can function as a command

• an evoked potential or evoked response can serve as a command

Dry Electrodes fabricated using MEMS technology will help to improve signal quality, selectivity, comfort and convenience


References

April 30th, 2010 M.Sc. Project Presentation 29

[1] M. Teplan, "Fundamentals of EEG Measurement," Measurement Science Review, vol. 2, p. Section 2, 2002.

[2J. D. Bronzino, "Principles of Electroencephalography," in The Biomedical Engineering Handbook, J.D.Bronzino, Ed. Florida, USA: CRC Press, 1995, pp. 201-212.

[3] F. H. Lopes da Silva E. Niedermeyer, Electroencephalography: Basic principles, clinical applications and related fields, 3rd ed., Lippincott, Ed. Philadelphia, USA: Williams & Wilkins, 1993.

[4] Wikipedia. (2010, March) Wikipedia. [Online]. http://en.wikipedia.org/wiki/Electroencephalography

[5] Wikipedia. (2010, February) Wikipedia. [Online]. http://en.wikipedia.org/wiki/Event-related_potential

[6] R.D.Bickford, "Electroencephalography," in Encyclopedia of Neuroscience, Adelman G., Ed. Cambridge, USA: Birkhauser, 1987, pp. 371-373.

[7] Emotiv. (2009) Emotiv Homepage. [Online]. http://emotiv.com/

[8] USA Today. (2009, July) Toy trains 'Star Wars' fans to use The Force. [Online]. http://www.usatoday.com/life/lifestyle/2009-01-06-force-trainer-toy_N.htm

[9] D. J. McFarland, and T. M. Vaughan J. R. Wolpaw, “Brain-Computer Interface Research at the Wadsworth Center,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 222-226, June 2000.

[10] Jonathan R. Wolpaw and Dennis J. McFarland, "Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans," PNAS, vol. 101, no. 51, pp. 17849 - 17854, December 2004.

[11] Antelis, and J. Minguez I. Iturrate, "Synchronous EEG Brain-Actuated Wheelchair with Automated Navigation," in 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009, pp. 2318-2325.

[12] D. C. Harrison H. A. Miller, “Biomedical Electrode Technology,” Academic Press, 1974.

[13] Li-Wei Ko, Chin-Teng Lin, Chao-Ting Hong, Tzyy-Ping Jung, Sheng-Fu Liang, Jong-Liang Jeng Jin-Chern Chiou, "Using novel MEMS EEG sensors in detecting drowsiness application," in Biomedical Circuits and Systems Conference, London, UK, 2006, pp. 33-36.

[14] S. Dunne, L. Fuentemilla, C. Grau, E. Farres, J. Marco-Pallares, P.C.P. Watts, S.R.P.Silva G. Ruffini, "First human trials of a dry electrophysiology sensor using a carbon nanotube array interface," Sensors and Actuators A: Physical, pp. 275-279, March 2008.

http://en.wikipedia.org/wiki/Electroencephalography

http://en.wikipedia.org/wiki/Event-related_potential



http://emotiv.com/

http://www.usatoday.com/life/lifestyle/2009-01-06-force-trainer-toy_N.htm











Thank you very much for your attention!


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April 30th, 2010 M.Sc. Project Presentation 3 - … novel MEMS EEG sensor for drowsiness ... Conventional Wet Electrode (BIOSEMI) & Prototype Electrode (ENOBIO ... 2010 M.Sc. Project