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Neural Spike Detection and Classification Using Massively Parallel Graphics Processing

Ervin, Brian
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868773

Abstract Details

2013, MS, University of Cincinnati, Engineering and Applied Science: Electrical Engineering.
A Brain-Computer Interface (BCI) is a direct communication line that bypasses the neuromuscular pathway and allows brain signals to directly control a program or neuroprosthetic device in a closed loop system. Advancements in electrode fabrication techniques using biological microelectromechanical systems (BioMEMS) can produce arrays with hundreds or thousands of channels, providing much better control over the system. Unfortunately, traditional real-time computing techniques are outpaced by the flood of input from these electrode arrays. However, the advent of general purpose graphics processing units (GPGPUs) for inexpensive, massively parallel processing allow for programs capable of handling thousands of channels real-time. This thesis describes a filter, spike detector, and spike sorter for real-time processing of real and simulated neural recordings, speed and accuracy comparisons between the traditional multi-core CPU algorithms and the many-core GPU algorithm. The algorithm will be implemented and released as open source for use with BCI2000, a free general-purpose BCI system.
Ali Minai, Ph.D. (Committee Chair)
J. Adam Wilson, Ph.D. (Committee Member)
Fred Beyette, Ph.D. (Committee Member)
69 p.
Electrical Engineering
BCI; CUDA; Spike Sorting; Spike Detection; Neural Spikes; Parallel Processing

Recommended Citations

Citations

  • Ervin, B. (2013). Neural Spike Detection and Classification Using Massively Parallel Graphics Processing [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868773

    APA Style (7th edition)

  • Ervin, Brian. Neural Spike Detection and Classification Using Massively Parallel Graphics Processing. 2013. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868773.

    MLA Style (8th edition)

  • Ervin, Brian. "Neural Spike Detection and Classification Using Massively Parallel Graphics Processing." Master's thesis, University of Cincinnati, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868773

    Chicago Manual of Style (17th edition)

ucin1377868773
604
© 2013, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.