Cargando…

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

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 classi...

Descripción completa

Detalles Bibliográficos
Autores principales: Wen, Shixian, Yin, Allen, Tseng, Po-He, Itti, Laurent, Lebedev, Mikhail A., Nicolelis, Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463672/
https://www.ncbi.nlm.nih.gov/pubmed/34561503
http://dx.doi.org/10.1038/s41598-021-98578-5
_version_ 1784572444706603008
author Wen, Shixian
Yin, Allen
Tseng, Po-He
Itti, Laurent
Lebedev, Mikhail A.
Nicolelis, Miguel
author_facet Wen, Shixian
Yin, Allen
Tseng, Po-He
Itti, Laurent
Lebedev, Mikhail A.
Nicolelis, Miguel
author_sort Wen, Shixian
collection PubMed
description 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 Online
Article
Text
id pubmed-8463672
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-84636722021-09-29 Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface Wen, Shixian Yin, Allen Tseng, Po-He Itti, Laurent Lebedev, Mikhail A. Nicolelis, Miguel Sci Rep Article 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. Nature Publishing Group UK 2021-09-24 /pmc/articles/PMC8463672/ /pubmed/34561503 http://dx.doi.org/10.1038/s41598-021-98578-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wen, Shixian
Yin, Allen
Tseng, Po-He
Itti, Laurent
Lebedev, Mikhail A.
Nicolelis, Miguel
Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463672/
https://www.ncbi.nlm.nih.gov/pubmed/34561503
http://dx.doi.org/10.1038/s41598-021-98578-5
work_keys_str_mv AT wenshixian capturingspiketraintemporalpatternwithwaveletaveragecoefficientforbrainmachineinterface
AT yinallen capturingspiketraintemporalpatternwithwaveletaveragecoefficientforbrainmachineinterface
AT tsengpohe capturingspiketraintemporalpatternwithwaveletaveragecoefficientforbrainmachineinterface
AT ittilaurent capturingspiketraintemporalpatternwithwaveletaveragecoefficientforbrainmachineinterface
AT lebedevmikhaila capturingspiketraintemporalpatternwithwaveletaveragecoefficientforbrainmachineinterface
AT nicolelismiguel capturingspiketraintemporalpatternwithwaveletaveragecoefficientforbrainmachineinterface