ssvep based bci & application presented by m.s. riazi supervisor: dr. m.b. shamsollahi

SSVEP based BCI & Application

Presented by M.S. RiaziSupervisor: Dr. M.B. Shamsollahi

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Outline• Introduction

• BCI

• SSVEP

• What we did at First• Application and Results

• New Application• Introduction

• Previous Works

• Block Diagram

• Results

• References

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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

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Typical BCI System

What is BCI ?

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What Kind of Activities does Brain Have?

• MTB Motor Imagery based BCI

• MIB Mental task based BCI

• P300 Related to unpredictability of stimulus

• VEP Response to rapid visual stimulus

• SSVEP Response to Oscillator stimulus

What is BCI ?

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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

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• When This oscillating pattern enters the Eyes, these photons are captured by visionary cells

What is SSVEP ?

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• By Recording the brain signals specially from the occipital lope and using FFT we see the following diagram

What is SSVEP ?

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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

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What We did at First•Using Emotive EEG (14 channels) for acquiring Brain signals

•Using MATLAB for processing the channels

Our Work

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Project Implementation Diagram

Emotive Driver

BCI 2000

Field Trip

BufferMATLAB

Visual Studio (C#)

Feedback to user

Signal Acquisiti

on

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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

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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

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Eigen values

MATLAB

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Visual StudioOur Work

•Most Important Concern: Concise Timing

Location

Time

2nd Harmoni

c

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Testing Application Results• Subject looking at 6 Hz oscillating screen

Previous Data

New application ‘s Data

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Results

Previous Data

• Subject looking at TIME MIXED 6 Hz and 18 Hz oscillating screen

• Result: Energy between these Frequencies are scrambled

New application ‘s Data

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Results

Previous Data

New application ‘s Data

• Subject looking at TIME MIXED 6 Hz and 10 Hz oscillating screen

• Result: Energy between these Frequencies are scrambled

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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

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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

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Previous WorksA cell-phone-based brain–computerinterface for communication in daily life

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Previous Works ResultA cell-phone-based brain–computerinterface for communication in daily life

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GSM Communication

Project Implementation Diagram

BCI 2000

Field Trip

BufferMATLAB

Visual Studio (C#)

Feedback to userSignal

Acquisition

Bluetooth

Android

Platform

Emotive Driver

Wireless Data Transfer

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Design Specifications• Key Pad with 12 dedicated buttons (numbers: 0 to 9, Backspace, #)

• Frequencies Dedicated: • 6 8 10 11 13 15 17 19 21 23 25 29 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

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Signal Processing DiagramRaw Signal

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Signal Processing DiagramAfter CMA and HP Filter

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Results• Accuracy of classification • Rows: Threshold Columns: Time interval Fixed Time

Interval

• Signal: 4 Channels, 1 Minute Data Acquisition, Person looking at 12Hz (Besides buttons: @10Hz and 14 Hz)

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Results• Dynamic Window Size (DWS)

• Accuracy Vs. Threshold

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Results• Dynamic Window Size (DWS)

• Distribution of each window size used

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References (1/3)1. “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

2. Simple communication using a SSVEP-based BCI, Guillermo Sanchez, Pablo F. Diez, Enrique Avila, Eric Laciar Leber, Journal ofPhysics:ConferenceSeries 332 (2011) 012017 doi:10.1088/1742-6596/332/1/012017

3. “SSVEP and P300 based interfaces”, Fabrizio Beverina,Giorgio Palmas,Stefano Silvoni,Francesco Piccione, PsychNology Journal, 2003, Volume 1, Number 4, 331 – 354

4. Towards an SSVEP Based BCI With High ITR, Ivan Volosyak, Diana Valbuena, Thorsten L¨uth, and Axel Gr¨aser,

5. G. Dornhege, J. del R. Millan, T. Hinterberger, D. J. McFarland, and K.-R. M¨uller, Toward Brain-Computer Interfacing. MIT Press, 2007

6. G. Schalk, “Sensor modalities for brain-computer interfacing,” in Human-Computer Interaction, Part II, HCII 2009, LNCS 5611, 2009, pp. 616–622.

7. 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. 1998.

8. 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. 3156–3164, 2005.

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References (2/3)9. 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,

10.Design and Implementation of a Brain-Computer Interface With High Transfer Rates Ming Cheng*, Xiaorong Gao, Shangkai Gao, Senior Member, IEEE, and Dingfeng Xu

11.Visual stimulus design for high-rate SSVEP BCI Y. Wang, Y.-T. Wang and T.-P. Jung

12.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

13.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

14.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.

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References (3/3)15.D. Regan, Human brain electrophysiology: evoked potentials and evoked magnetic fields in

science and medicine. New York: Elsevier Pubs; 1989.

16.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. 23-26, Jan. 1999.

17.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.

18.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. 285-290, 1998.

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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

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ThanK you iN adVanCe f0r your AtTentiOn