Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements

Abstract Compared to the abilities of the animal brain, many Artificial Intelligence systems have limitations which emphasise the need for a Brain-Inspired Artificial Intelligence paradigm. This paper proposes a novel Brain-Inspired Spiking Neural Network (BI-SNN) model for incremental learning of s...

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Autores principales:	Kaushalya Kumarasinghe, Nikola Kasabov, Denise Taylor
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/af1659f8030f4cc8bbb59d3d389d1c40
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spelling	oai:doaj.org-article:af1659f8030f4cc8bbb59d3d389d1c402021-12-02T13:24:17ZBrain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements10.1038/s41598-021-81805-42045-2322https://doaj.org/article/af1659f8030f4cc8bbb59d3d389d1c402021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81805-4https://doaj.org/toc/2045-2322Abstract Compared to the abilities of the animal brain, many Artificial Intelligence systems have limitations which emphasise the need for a Brain-Inspired Artificial Intelligence paradigm. This paper proposes a novel Brain-Inspired Spiking Neural Network (BI-SNN) model for incremental learning of spike sequences. BI-SNN maps spiking activity from input channels into a high dimensional source-space which enhances the evolution of polychronising spiking neural populations. We applied the BI-SNN to predict muscle activity and kinematics from electroencephalography signals during upper limb functional movements. The BI-SNN extends our previously proposed eSPANNet computational model by integrating it with the ‘NeuCube’ brain-inspired SNN architecture. We show that BI-SNN can successfully predict continuous muscle activity and kinematics of upper-limb. The experimental results confirmed that the BI-SNN resulted in strongly correlated population activity and demonstrated the feasibility for real-time prediction. In contrast to the majority of Brain–Computer Interfaces (BCIs) that constitute a ‘black box’, BI-SNN provide quantitative and visual feedback about the related brain activity. This study is one of the first attempts to examine the feasibility of finding neural correlates of muscle activity and kinematics from electroencephalography using a brain-inspired computational paradigm. The findings suggest that BI-SNN is a better neural decoder for non-invasive BCI.Kaushalya KumarasingheNikola KasabovDenise TaylorNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kaushalya Kumarasinghe Nikola Kasabov Denise Taylor Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements
description	Abstract Compared to the abilities of the animal brain, many Artificial Intelligence systems have limitations which emphasise the need for a Brain-Inspired Artificial Intelligence paradigm. This paper proposes a novel Brain-Inspired Spiking Neural Network (BI-SNN) model for incremental learning of spike sequences. BI-SNN maps spiking activity from input channels into a high dimensional source-space which enhances the evolution of polychronising spiking neural populations. We applied the BI-SNN to predict muscle activity and kinematics from electroencephalography signals during upper limb functional movements. The BI-SNN extends our previously proposed eSPANNet computational model by integrating it with the ‘NeuCube’ brain-inspired SNN architecture. We show that BI-SNN can successfully predict continuous muscle activity and kinematics of upper-limb. The experimental results confirmed that the BI-SNN resulted in strongly correlated population activity and demonstrated the feasibility for real-time prediction. In contrast to the majority of Brain–Computer Interfaces (BCIs) that constitute a ‘black box’, BI-SNN provide quantitative and visual feedback about the related brain activity. This study is one of the first attempts to examine the feasibility of finding neural correlates of muscle activity and kinematics from electroencephalography using a brain-inspired computational paradigm. The findings suggest that BI-SNN is a better neural decoder for non-invasive BCI.
format	article
author	Kaushalya Kumarasinghe Nikola Kasabov Denise Taylor
author_facet	Kaushalya Kumarasinghe Nikola Kasabov Denise Taylor
author_sort	Kaushalya Kumarasinghe
title	Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements
title_short	Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements
title_full	Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements
title_fullStr	Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements
title_full_unstemmed	Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements
title_sort	brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/af1659f8030f4cc8bbb59d3d389d1c40
work_keys_str_mv	AT kaushalyakumarasinghe braininspiredspikingneuralnetworksfordecodingandunderstandingmuscleactivityandkinematicsfromelectroencephalographysignalsduringhandmovements AT nikolakasabov braininspiredspikingneuralnetworksfordecodingandunderstandingmuscleactivityandkinematicsfromelectroencephalographysignalsduringhandmovements AT denisetaylor braininspiredspikingneuralnetworksfordecodingandunderstandingmuscleactivityandkinematicsfromelectroencephalographysignalsduringhandmovements
_version_	1718393114667778048

Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements

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