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Portable BCI Stimulator

Portable BCI Stimulator. Final Presentation Group: 17 Bonnie Chen, Siyuan Wu, Randy Lefkowitz TA: Ryan May ECE 445 Monday, April 29 th , 2013. Overview. Introduction Features BCI/EEG System Overview Design of Individual Modules Testing and Verifications Future Development Sponsors.

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Portable BCI Stimulator

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  1. Portable BCI Stimulator Final Presentation Group: 17 Bonnie Chen, Siyuan Wu, Randy Lefkowitz TA: Ryan May ECE 445 Monday, April 29th, 2013

  2. Overview • Introduction • Features • BCI/EEG System Overview • Design of Individual Modules • Testing and Verifications • Future Development • Sponsors

  3. Introduction • Most brain computer interfaces (BCI) limited to laboratory settings • We would like to help make the EEG system more portable through bluetooth • Allows people to communicate without any type of movement

  4. Features • Portability • Wearable Size • Rechargeable Battery • Wireless control • Bluetooth • Compatible with most computers • Variable frequency and Intensity • Set by User

  5. BCI/EEG System Overview

  6. Stimulator Top Level

  7. Computer/Wireless Module Overview • Wireless communication between PC and Bluetooth Module through terminal • Retrieve LED number, frequency and intensity level from user • Check the validity of command

  8. Wireless Transmitter/Receiver • Built in Bluetooth 2.0 communication • Standard TTL Bluetooth receiver • Data sent wirelessly from PC to Arduino

  9. Receiver – Arduino Diagram

  10. Input Flowchart

  11. Microcontroller Module • Calculates Runtime • Determines if each LED should toggle • Sets LED values • Latches values into LED driver

  12. Timing Flowchart

  13. Dividing Interval for On and Off • Calculate LED state time (on/off) • Interval = current – previous • Compare to Required Time • 1/(2*frequency) • Toggle if needed • Save new time • Previous = current

  14. TL5940 LED Driver Overview • 16 Output Channels • Rref = 2k ohms • Intensity set by PWM • Frequency controlled by Arduino TLC5940 library

  15. TLC-5940 Library • TLC.set(channel,intensity); • Loads TLC Register • Tlc.update(); • Latches data into LED driver

  16. TLC-5940 Arduino Connections

  17. 7.4V Power Supply Venom 1250mAh 10C 7.4V Lithium Ion Battery

  18. LED Array • Powered by 5V output from Arduino • Flashes at frequency values between 1-9 Hz based on Arduino Code • LED Intensities based on PWM values from LED Driver • 5-10 LEDs mounted on adjustable frame

  19. Lilypad Micro LEDs • 3.3mm long • Forward Voltage of 3.2-4.0V • 200mA forward current • Power Dissipation of 120mW

  20. PCB Schematic

  21. PCB Design Top Bottom

  22. Final Design

  23. Demo with the EEG

  24. Review of Requirements • Wireless Communication • Portability • Sufficient Power • Successful Classification over different frequencies on EEG System

  25. Testing and Verifications • EEG Classification • Frequency Bandwidth • Power Budget

  26. EEG Classification • Demo Frequencies • 6, 7, 8, 9 Hz • Classification • All 4 Frequencies classified correctly (within 0.3Hz) • Intensity of 20 out of 4096 • Fast Response • Comfortable Viewing

  27. Frequency Bandwidth • Record LED Driver Output on Oscilloscope • Analyze EEG data with MATLAB • Compare Variance with EEG Classifier Sensitivity • Adjust Sensitivity values of EEG Program Accordingly • Test user response on EEG with updated sensitivity values

  28. Frequency Bandwidth Cont. 1 Hz: μ = 1.0912, σ2 = 0.41 6 Hz: μ = 6.0694, σ2 = 0.36 7 Hz: μ = 7.1238, σ2 = 1.37 8 Hz: μ = 8.1937, σ2 = 2.01 9 Hz: μ = 9.2745, σ2 = 5.22 10 Hz: μ = 10.495, σ2 = 10.77 • Higher Frequencies produced less stable results • SSVEP measurements generally 5-15 Hz • Less accurate frequencies cause slower EEG response times

  29. Frequency Bandwidth Cont.

  30. Power Budget Component Imax (mA) Voltage Microcontroller 40 * 2 output pins 7-12V (ideal) LEDs 20 * (10 LEDs) 3.2V (green, white) Bluetooth Module 40 3.3V LED Driver 120 5V ________________ _________________ _________________ Total 440 ----------------- Estimated Usage time = 1250 [mAh] / 440 [mA] ≅ 3 hours of charge Factors to consider: - PWM value will never be over 50% (the blinking LED is on less than half of the time) - Able to get same results using 5 LEDs instead of 10 LEDs

  31. Future Development • Safer operating limits for near-eye LEDs • Determine ideal threshold for response time, classification, and stability of the system. • Improvements on mounting frame mechanics (aesthetics and functionality) • Use Feedback from the EEG to implement commands that can control a range of devices (Quadcopter, Paralysis Assistance)

  32. Sponsors A special thanks to the following people who helped make this project happen • James Norton (Beckman) • Erik Johnson (Beckman) • David Jun (Beckman) • Ryan May • Professor Carney • The friendly folks in the ECE parts shop

  33. Thanks!!

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