Neural spike sorting with a self-training semisupervised support vector machine.

Abstract : Brain decoding would be a replacement for some nerve injured patients to communicate motor functions with a prosthesis device. Decoding algorithms translate ensemble of firing rates to the intended function. Firing rates for each individual neuron are obtained from labeling the detected spikes. This labeling process-also known as spike sorting-could be done from the range of fully automated to a heavily operator dependent manners. On the other hand we could use merits of both automation and operator's watch in a semi-supervised approach. In this study we explored the application of a self-training SVM classifier algorithm to label spikes with a small training dataset. Result shows the proved monotonically increasing convergence and consequently the ability of this algorithm to significantly reduce the operator's effort for continuous supervision. It provides in addition a significant improvement with respect to the previously used SVMs.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01696295
Contributor : Frédéric Davesne <>
Submitted on : Tuesday, January 30, 2018 - 11:47:48 AM
Last modification on : Monday, October 28, 2019 - 10:50:22 AM

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  • HAL Id : hal-01696295, version 1

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Abed Ghanbari, Mohammad-Bagher Shamsollahi, Vincent Vigneron, Abdessalam Kifouche. Neural spike sorting with a self-training semisupervised support vector machine.. 35th Annual International Conference of Engineering in Medicine and Biology Society (EMBC 2013), Jul 2013, Osaka, Japan. pp.6007--6010. ⟨hal-01696295⟩

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