Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

Brain–computer interface (BCI) is an important alternative for disabled people that enables the innovative communication pathway among individual thoughts and different assistive appliances. In order to make an efficient BCI system, different physiological signals from the brain have been utilized f...

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Main Authors:	Rashid, Mamunur, Islam, Minarul, Norizam, Sulaiman, Bari, Bifta Sama, Saha, Ripon Kumar, Hasan, Md Jahid
Format:	Article
Language:	English
Published:	Springer Nature 2020
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/27512/ http://umpir.ump.edu.my/id/eprint/27512/ http://umpir.ump.edu.my/id/eprint/27512/ http://umpir.ump.edu.my/id/eprint/27512/1/Electrocorticography%20based%20motor%20imagery1.pdf

id	ump-27512
recordtype	eprints
spelling	ump-275122020-01-17T08:29:08Z http://umpir.ump.edu.my/id/eprint/27512/ Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach Rashid, Mamunur Islam, Minarul Norizam, Sulaiman Bari, Bifta Sama Saha, Ripon Kumar Hasan, Md Jahid TK Electrical engineering. Electronics Nuclear engineering Brain–computer interface (BCI) is an important alternative for disabled people that enables the innovative communication pathway among individual thoughts and different assistive appliances. In order to make an efficient BCI system, different physiological signals from the brain have been utilized for instances, steady-state visual evoked potential, motor imagery, P300, movement-related potential and error-related potential. Among these physiological signals, motor imagery is widely used in almost all BCI applications. In this paper, Electrocorticography (ECoG) based motor imagery signal has been classified using long short-term memory (LSTM). ECoG based motor imagery data has been taken from BCI competition III, dataset I. The proposed LSTM approach has achieved the classification accuracy of 99.64%, which is the utmost accuracy in comparison with other state-of-art methods that have employed the same data set. Springer Nature 2020 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27512/1/Electrocorticography%20based%20motor%20imagery1.pdf Rashid, Mamunur and Islam, Minarul and Norizam, Sulaiman and Bari, Bifta Sama and Saha, Ripon Kumar and Hasan, Md Jahid (2020) Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach. SN Applied Sciences, 2 (2). pp. 211-217. ISSN 2523-3963 (Print); 2523-3971 (Online) https://doi.org/10.1007/s42452-020-2023-x https://doi.org/10.1007/s42452-020-2023-x
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Rashid, Mamunur Islam, Minarul Norizam, Sulaiman Bari, Bifta Sama Saha, Ripon Kumar Hasan, Md Jahid Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach
description	Brain–computer interface (BCI) is an important alternative for disabled people that enables the innovative communication pathway among individual thoughts and different assistive appliances. In order to make an efficient BCI system, different physiological signals from the brain have been utilized for instances, steady-state visual evoked potential, motor imagery, P300, movement-related potential and error-related potential. Among these physiological signals, motor imagery is widely used in almost all BCI applications. In this paper, Electrocorticography (ECoG) based motor imagery signal has been classified using long short-term memory (LSTM). ECoG based motor imagery data has been taken from BCI competition III, dataset I. The proposed LSTM approach has achieved the classification accuracy of 99.64%, which is the utmost accuracy in comparison with other state-of-art methods that have employed the same data set.
format	Article
author	Rashid, Mamunur Islam, Minarul Norizam, Sulaiman Bari, Bifta Sama Saha, Ripon Kumar Hasan, Md Jahid
author_facet	Rashid, Mamunur Islam, Minarul Norizam, Sulaiman Bari, Bifta Sama Saha, Ripon Kumar Hasan, Md Jahid
author_sort	Rashid, Mamunur
title	Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach
title_short	Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach
title_full	Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach
title_fullStr	Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach
title_full_unstemmed	Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach
title_sort	electrocorticography based motor imagery movements classification using long short-term memory (lstm) based on deep learning approach
publisher	Springer Nature
publishDate	2020
url	http://umpir.ump.edu.my/id/eprint/27512/ http://umpir.ump.edu.my/id/eprint/27512/ http://umpir.ump.edu.my/id/eprint/27512/ http://umpir.ump.edu.my/id/eprint/27512/1/Electrocorticography%20based%20motor%20imagery1.pdf
first_indexed	2023-09-18T22:43:17Z
last_indexed	2023-09-18T22:43:17Z
_version_	1777417057249263616

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

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