1. SSVEP based BCI & Application
Presented by M.S. Riazi
Supervisor: Dr. M.B. Shamsollahi

2. Outline Introduction What we did at First New Application References
BCI
SSVEP
What we did at First
Application and Results
New Application
Previous Works
Block Diagram
Results
References

3. What is BCI ? Abbreviation of Brain Computer Interface
Is a direct communication pathway between the brain and an external
Falls in 2 major categories
External
Invasive

4. Typical BCI System
What is BCI ?

5. What Kind of Activities does Brain Have?
What is BCI ?
MTB Motor Imagery based BCI
MIB Mental task based BCI
P Related to unpredictability of stimulus
VEP Response to rapid visual stimulus
SSVEP Response to Oscillator stimulus

6. What is SSVEP ? Steady State Visual Evoked Potentials
Brain response to oscillator stimulus
If the stimulus oscillate with a specific frequency, The pattern with the same frequency will appear in the Occipital Lobe

7. When This oscillating pattern enters the Eyes, these photons are captured by visionary cells
What is SSVEP ?

8. By Recording the brain signals specially from the occipital lope and using FFT we see the following diagram
What is SSVEP ?

9. What We did at First
Using Multiple frequencies dedicated to one Class
Aim: To select our class much FASTER (less Avg. time)
Strategies->dedicating multiple frequencies by:
Time
Defining different signal level and adding 2 sinusoids arithmetically
Location
Using different locations for each frequency

10. What We did at First Our Work
Using Emotive EEG (14 channels) for acquiring Brain signals
Using MATLAB for processing the channels

11. Project Implementation Diagram
BCI 2000
Field Trip Buffer
MATLAB
Visual Studio (C#)
Emotive Driver
Feedback to user
Signal Acquisition

12. Project Implementation Flow
BCI 2000: An application which will handle the flow of Data among different side applications.
Field Trip Buffer: An interface application which gets Data from BCI 2000 and pass it to MATLAB
MATLAB: Core of all Processes! Gets Signal Data form Field Trip Buffer and give the Results to Visual Studio

13. MATLAB
Implementation of Paper “Towards an SSVEP Based BCI With High ITR” [4]
Main Idea is that we generate artificial channels by which we can reduce the noise influence will get its min value

14. MATLAB
Eigen values

15. Visual Studio Our Work 2nd Harmonic Time Location
Most Important Concern: Concise Timing
Time
Location

16. Testing Application Results
Subject looking at 6 Hz oscillating screen
Previous Data
New application ‘s Data

17. Subject looking at TIME MIXED 6 Hz and 18 Hz oscillating screen
Result: Energy between these Frequencies are scrambled
Results
Previous Data
New application ‘s Data

18. Subject looking at TIME MIXED 6 Hz and 10 Hz oscillating screen
Result: Energy between these Frequencies are scrambled
Results
Previous Data
New application ‘s Data

19. New Application
Aim: Person looks at phone dial keypad for each desired digit and then the digits are complete, a phone call will take a place with that number

20. New Application
Aim: Person looks at phone dial keypad for each desired digit and then the digits are complete, a phone call will take a place with that number

21. Previous Works A cell-phone-based brain–computer interface for communication in daily life

22. Previous Works Result A cell-phone-based brain–computer interface for communication in daily life

23. Project Implementation Diagram
BCI 2000
Field Trip Buffer
MATLAB
Visual Studio (C#)
Bluetooth
Emotive Driver
Android Platform
Wireless Data
Transfer
Feedback to user
Signal Acquisition
GSM Communication

24. Design Specifications
Key Pad with 12 dedicated buttons (numbers: 0 to 9, Backspace, #)
Frequencies Dedicated:
Constraints !
Dedicated with scatter position on Pad
Algorithm used: Minimum Energy Combination (MEC)
+ High Pass Filter
+ Threshold Scalable Classification
+ Common Mode Average Filter (CMA)
+ Dynamic Window Size (DWS)
+ Special Pattern Construction

25. Signal Processing Diagram Raw Signal

26. Signal Processing Diagram After CMA and HP Filter

27. Results Accuracy of classification
Rows: Threshold Columns: Time interval Fixed Time Interval
Signal: 4 Channels, 1 Minute Data Acquisition, Person looking at 12Hz (Besides and 14 Hz)

28. Results
Dynamic Window Size (DWS)
Accuracy Vs. Threshold

29. Results Dynamic Window Size (DWS)
Distribution of each window size used

30. References (1/3)
“A Study on SSVEP-Based BCI”, Zheng-Hua Wu and De-Zhong Yao ,JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA, VOL. 7, NO. 1, MARCH 2009
Simple communication using a SSVEP-based BCI, Guillermo Sanchez, Pablo F. Diez, Enrique Avila, Eric Laciar Leber, Journal ofPhysics:ConferenceSeries 332 (2011) doi: / /332/1/012017
“SSVEP and P300 based interfaces”, Fabrizio Beverina,Giorgio Palmas,Stefano Silvoni,Francesco Piccione, PsychNology Journal, 2003, Volume 1, Number 4, 331 – 354
Towards an SSVEP Based BCI With High ITR, Ivan Volosyak, Diana Valbuena, Thorsten L¨uth, and Axel Gr¨aser,
G. Dornhege, J. del R. Millan, T. Hinterberger, D. J. McFarland, and K.-R. M¨uller, Toward Brain-Computer Interfacing. MIT Press, 2007
G. Schalk, “Sensor modalities for brain-computer interfacing,” in Human-Computer Interaction, Part II, HCII 2009, LNCS 5611, 2009, pp. 616–622.
J. R. Wolpaw, H. Ramoser, D. J. McFarland, and G. Pfurtscheller, “EEG-based communication: improved accuracy by response verification,” IEEE Trans. Rehabil. Eng., vol. 6, no. 3, pp. 326–333, Sep
E. C. Lalor, S. P. Kelly, C. Finucane, R. Burke, R. Smith, R. B. Reilly, and G. McDarby, “Steady-state VEP-based brain- computer interface control in an immersive 3D gaming environment,” EURASIP J. Appl. Signal Process., vol. 19, pp –3164, 2005.

31. References (2/3)
J. R. Wolpaw et al., “Brain-computer interface technology: A review of the first international meeting,” IEEE Trans. Rehab. Eng., vol. 8, pp. 164–173, June 2000.Towards an SSVEP Based BCI With High ITR, Ivan Volosyak, Diana Valbuena, Thorsten L¨uth, and Axel Gr¨aser,
Design and Implementation of a Brain-Computer Interface With High Transfer Rates Ming Cheng*, Xiaorong Gao, Shangkai Gao, Senior Member, IEEE, and Dingfeng Xu
Visual stimulus design for high-rate SSVEP BCI Y. Wang, Y.-T. Wang and T.-P. Jung
A cell-phone-based brain–computer interface for communication in daily life Yu-Te Wang1, Yijun Wang1 and Tzyy- Ping Jung Swartz Center for Computational Neuroscience, Institute for Neural Computational, University of California, San Diego, La Jolla, CA, USA
Developing Stimulus Presentation on Mobile Devices for a Truly Portable SSVEP-based BCI Yu-Te Wang, Student Member, IEEE, Yijun Wang, Member, IEEE, Chung-Kuan Cheng, Fellow, IEEE, and Tzyy-Ping Jung*, Senior Member, IEEE
G. Bin, X. Gao, Z. Yan, B. Hong, and S. Gao, “An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method,” Journal of Neural Engineering, vol. 6, no. 4, 2009.

32. References (3/3)
D. Regan, Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. New York: Elsevier Pubs; 1989.
M. De Tommaso, V. Sciruicchio, M. Guido, G. Sasanelli, and F. Puca, “Steady-state visual evoked potentials in headache: diagnostic value in migraine and tension-type headache patients”. Cephalalgia, vol. 19, pp , Jan
Y.-T. Wang, Y. Wang, and T.-P. Jung, “A Cell-phone based Brain Computer Interface for Communication in Daily Life ", Journal of Neural Engineering, vol.8, no.2, pp. 1-5, 2011.
E. Lyskov, V. Ponomarev, M. Sandstrom, K. H. Mild, and S. Medvedev, “Steady-State Visual Evoked Potentials to Computer Monitor Flicker,” International Jurnal of Psychophysology, vol. 28, pp , 1998.

33. Grateful of Prof. M.B. Shamsollahi Omid Ghasemsani Sajad Karimi
Masih Bahrani
Javad Abedi
Mohammad Javad Seyed Talebi
Who helped me very much during this project

34. ThanK you iN adVanCe f0r your AtTentiOn 