Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces

Designing energy efficient and high performance brain-machine interfaces with millions of recording electrodes for in-situ analysis remains a challenge. Here, the authors develop a memristor-based neural signal analysis system capable of filtering and identifying epilepsy-related brain activities wi...

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Autores principales:	Zhengwu Liu, Jianshi Tang, Bin Gao, Peng Yao, Xinyi Li, Dingkun Liu, Ying Zhou, He Qian, Bo Hong, Huaqiang Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4a8f976011da4016a9966b92e61dcf55
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spelling	oai:doaj.org-article:4a8f976011da4016a9966b92e61dcf552021-12-02T16:35:00ZNeural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces10.1038/s41467-020-18105-42041-1723https://doaj.org/article/4a8f976011da4016a9966b92e61dcf552020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18105-4https://doaj.org/toc/2041-1723Designing energy efficient and high performance brain-machine interfaces with millions of recording electrodes for in-situ analysis remains a challenge. Here, the authors develop a memristor-based neural signal analysis system capable of filtering and identifying epilepsy-related brain activities with an accuracy of 93.46%.Zhengwu LiuJianshi TangBin GaoPeng YaoXinyi LiDingkun LiuYing ZhouHe QianBo HongHuaqiang WuNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Zhengwu Liu Jianshi Tang Bin Gao Peng Yao Xinyi Li Dingkun Liu Ying Zhou He Qian Bo Hong Huaqiang Wu Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces
description	Designing energy efficient and high performance brain-machine interfaces with millions of recording electrodes for in-situ analysis remains a challenge. Here, the authors develop a memristor-based neural signal analysis system capable of filtering and identifying epilepsy-related brain activities with an accuracy of 93.46%.
format	article
author	Zhengwu Liu Jianshi Tang Bin Gao Peng Yao Xinyi Li Dingkun Liu Ying Zhou He Qian Bo Hong Huaqiang Wu
author_facet	Zhengwu Liu Jianshi Tang Bin Gao Peng Yao Xinyi Li Dingkun Liu Ying Zhou He Qian Bo Hong Huaqiang Wu
author_sort	Zhengwu Liu
title	Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces
title_short	Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces
title_full	Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces
title_fullStr	Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces
title_full_unstemmed	Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces
title_sort	neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/4a8f976011da4016a9966b92e61dcf55
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Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces

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