Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface

Abstract Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, ma...

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Autores principales:	Shixian Wen, Allen Yin, Po-He Tseng, Laurent Itti, Mikhail A. Lebedev, Miguel Nicolelis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d5836d99c3f14482b691c159979bcd53
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spelling	oai:doaj.org-article:d5836d99c3f14482b691c159979bcd532021-12-02T15:15:05ZCapturing spike train temporal pattern with wavelet average coefficient for brain machine interface10.1038/s41598-021-98578-52045-2322https://doaj.org/article/d5836d99c3f14482b691c159979bcd532021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98578-5https://doaj.org/toc/2045-2322Abstract Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description—wavelet average coefficients (WAC)—to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders ( Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.Shixian WenAllen YinPo-He TsengLaurent IttiMikhail A. LebedevMiguel NicolelisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shixian Wen Allen Yin Po-He Tseng Laurent Itti Mikhail A. Lebedev Miguel Nicolelis Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface
description	Abstract Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description—wavelet average coefficients (WAC)—to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders ( Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.
format	article
author	Shixian Wen Allen Yin Po-He Tseng Laurent Itti Mikhail A. Lebedev Miguel Nicolelis
author_facet	Shixian Wen Allen Yin Po-He Tseng Laurent Itti Mikhail A. Lebedev Miguel Nicolelis
author_sort	Shixian Wen
title	Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface
title_short	Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface
title_full	Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface
title_fullStr	Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface
title_full_unstemmed	Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface
title_sort	capturing spike train temporal pattern with wavelet average coefficient for brain machine interface
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d5836d99c3f14482b691c159979bcd53
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_version_	1718387601182818304

Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface

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