Matt Waldersen T.J. Strzelecki Rick Schuman Krishna Jharjaria.
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Transcript of Matt Waldersen T.J. Strzelecki Rick Schuman Krishna Jharjaria.
What We’re Doing
The proposed project will be a mobile brain-computer interface.
Various computer applications will be presented to the user on a head mounted display system.
The user will be able to navigate between different applications presented on the heads up display through eye gestures detected by an electrooculogram (EOG).
The user will be able to select different applications by increasing their level of concentration measured by an electroencephalogram (EEG).
Project-Specific Success Criteria
1) An ability to encode/decode data packets from a NeuroSky EEG.
2) An ability for a user to select applications based on signals from a NueroSky EEG.
3) An ability for a user to navigate between different applications on a display using EOG signals.
4) An ability for the system to interactively train the user to effectively operate the device.
5) An ability to display a live video stream from an external camera module, and integrate applications into the video system.
Motherboard Constraint
Processor Speeds around 1.0 GHz Utilizes Multithreading, Graphics Optimization
Plenty of Memory 2 GB System Memory 512 MB RAM
Needs at least 8 GPIO pins High Res Display No more than 12 Volt Supply USB out for head-mounted camera Head-mounted, Mobile, Lightweight Low Power
Motherboard Comparison
Intel D2550 1.86 GHz 1M Cache 4 GB max RAM 8 GPIO 8 USB VGA 1 lb. & 17cm x 17cm 12 V supply
Raspberry Pi 0.8 Ghz 256 MB RAM 8 GPIO 2 USB 86mm x 54mm VGA/HDMI 45 g weight 5 V supply
Microcontroller Criteria
Signal Processing abilities Digital Communication Optimized for C compiler Resources and reference
material Processing
speed Price
DSPIC33EP512MU10 (PIC)
Has USB capabilities Extensive DSP Library with
built in FFT function
4-UART; 4-SPI; 2-I2C
Optimized for C compiler
Large online community
~53K of RAM
DSP56857 (FREESCALE)
Team is familiar with CodeWarrior IDE
120 MIPS
Built in voltage regulator
0-UART; 1-SPI; 0-I2C
Low Power Consumption
24K of RAM
Microcontroller Comparison
Microcontroller Selection Rationale DSP Library allows us to further filter a very sensitive EOG
signal. FFT function will allow us to decompose “raw EEG” signal
at 512 Hz instead of headset values which refresh at 1 Hz. Optimization for C compiler will allow greater simplicity in
implementing k-nearest neighbor algorithm for EOG signal classification.
Will be able to communicate with the EOG, the EEG, the FPGA and the single board computer.
Large online community and online documentation will aid in troubleshooting process
FPGA Design Criteria
Large area to implement Artificial Neural Network
Number of I/O pins needed Resources and
reference material Built in functionality Price
FPGA Design Constraints
Calculated a need of around 12,700 slices. Which equates to about 24,000 logic blocks, based on ANN’s previously made on FPGA’s
Need approximately 20 I/O pins (most FPGA have many more than needed)
Low power, and low cost Built in functionality to help with
development of algorithm Level of difficulty in designing
FPGA Design Comparison
Xilinx FPGA (Spartan-6) Library for floating point
arithmetic Built in 18 bit multipliers Documented ANN on
Xilinx FPGA’s Abundant reference
material on designing and programming
Cheaper than Cyclone 1.14V – 1.26V More than enough I/O pins
Altera FPGA (Cyclone-II) Prior knowledge of Altera
FPGA’s and Altera software from 437
1.15 V – 1.25 V More expensive than
Spartan More than enough I/O pins Unable to find documented
successful ANN on Altera devices