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Neural Spike Sorting and Improvement of Non-stationary Continuous Hand Movement Decoding

Ghanbari, Abdollah | 2014

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 45616 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Shamsollahi, Mohammad Bagher
  7. Abstract:
  8. Brain is the most complicated organ of body which controls the activity of all other organs. Understanding its function and its language could give us a direct communication pathway for connecting injured motor organ and it could be useful for functional repairing. Neurons are atoms of a vast network that generate the brain signals. Processing these signals would help to translate brain’s language and has three main stages: spike detection from signal, spike sorting, and intention extraction from encoded signal.
    In this research, we use a dataset of rat’s extracellular recordings during a time interval in which a rat pressed the liver several times to receive water as an award. Since spikes were detected by the recording setup, before any post processing; we improve this stage’s results. Next step is spike sorting which uses a novel idea based on Semi-supervised self-training Support Vector Machine.
    In next step, we used the result of former steps to extract a task with decoding the train of spikes. Initially, we exploit firing rates of the neurons. Next, we used several approaches on linear Baysian modelling and nonlinear mapping. In Baysian modelling approach we developed Dual Kalman filter and compared the results with Optimal Linear Estimation, Kalman Filter, Support Vector Regression and Radial Basis Function. The computed normalized mean square error for Dual Kalman filter is 5.21 which compared to other methods shows significant improve on the accuracy
  9. Keywords:
  10. Extracellular Recording ; Support Vector Machine (SVM) ; Neural Spikes ; Spike Sorting ; Hand Motion ; Neural Decoding

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