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Matt Waldersen T.J. Strzelecki Rick Schuman Krishna Jharjaria. Mind Readers. 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.
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Matt Waldersen T.J. Strzelecki Rick Schuman Krishna Jharjaria Mind Readers
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-SpecificSuccess Criteria • An ability to encode/decode data packets from a NeuroSky EEG. • An ability for a user to select applications based on signals from a NueroSky EEG. • An ability for a user to navigate between different applications on a display using EOG signals. • An ability for the system to interactively train the user to effectively operate the device. • 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
Microcontroller Comparison dsPIC33EP512MU10 (PIC) 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 • 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
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 • Altera FPGA (Cyclone-II) • Prior knowledge of Altera FPGA’s and Alterasoftware 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 • 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