A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification

Abstract Motor Imagery is a classical method of Brain Computer Interaction, in which electroencephalogram (EEG) signal features evoked by the imaginary body movements are recognized, and relevant information is extracted. Recently, various deep learning methods are being focused on finding an easy-t...

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Autores principales:	Tianjun Liu, Deling Yang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aa3f36c8890e494db50668f1e1a678fe
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id	oai:doaj.org-article:aa3f36c8890e494db50668f1e1a678fe
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spelling	oai:doaj.org-article:aa3f36c8890e494db50668f1e1a678fe2021-12-02T14:49:24ZA three-branch 3D convolutional neural network for EEG-based different hand movement stages classification10.1038/s41598-021-89414-x2045-2322https://doaj.org/article/aa3f36c8890e494db50668f1e1a678fe2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89414-xhttps://doaj.org/toc/2045-2322Abstract Motor Imagery is a classical method of Brain Computer Interaction, in which electroencephalogram (EEG) signal features evoked by the imaginary body movements are recognized, and relevant information is extracted. Recently, various deep learning methods are being focused on finding an easy-to-use EEG representation method that can preserve both temporal information as well as spatial information. To further utilize the spatial and temporal features of EEG signals, we proposed a 3D representation of EEG and an end-to-end EEG three-branch 3D convolutional neural network, to address the class imbalance problem (dataset show unequal distribution among their classes), we proposed a class balance cropped strategy. Experimental results indicated that there are also a problem of the different classification difficulty for different classes in motor stages classification tasks, we introduce focal loss to address problem of ‘easy-hard’ examples, when trained with the focal loss, the three-branch 3D-CNN network achieve good performance (relatively more balanced classification accuracy of binary classifications) on the WAY-EEG-GAL data set. Experimental results show that the proposed method is a good method, which can improve classification effect of different motor stages classification.Tianjun LiuDeling YangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tianjun Liu Deling Yang A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification
description	Abstract Motor Imagery is a classical method of Brain Computer Interaction, in which electroencephalogram (EEG) signal features evoked by the imaginary body movements are recognized, and relevant information is extracted. Recently, various deep learning methods are being focused on finding an easy-to-use EEG representation method that can preserve both temporal information as well as spatial information. To further utilize the spatial and temporal features of EEG signals, we proposed a 3D representation of EEG and an end-to-end EEG three-branch 3D convolutional neural network, to address the class imbalance problem (dataset show unequal distribution among their classes), we proposed a class balance cropped strategy. Experimental results indicated that there are also a problem of the different classification difficulty for different classes in motor stages classification tasks, we introduce focal loss to address problem of ‘easy-hard’ examples, when trained with the focal loss, the three-branch 3D-CNN network achieve good performance (relatively more balanced classification accuracy of binary classifications) on the WAY-EEG-GAL data set. Experimental results show that the proposed method is a good method, which can improve classification effect of different motor stages classification.
format	article
author	Tianjun Liu Deling Yang
author_facet	Tianjun Liu Deling Yang
author_sort	Tianjun Liu
title	A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification
title_short	A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification
title_full	A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification
title_fullStr	A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification
title_full_unstemmed	A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification
title_sort	three-branch 3d convolutional neural network for eeg-based different hand movement stages classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aa3f36c8890e494db50668f1e1a678fe
work_keys_str_mv	AT tianjunliu athreebranch3dconvolutionalneuralnetworkforeegbaseddifferenthandmovementstagesclassification AT delingyang athreebranch3dconvolutionalneuralnetworkforeegbaseddifferenthandmovementstagesclassification AT tianjunliu threebranch3dconvolutionalneuralnetworkforeegbaseddifferenthandmovementstagesclassification AT delingyang threebranch3dconvolutionalneuralnetworkforeegbaseddifferenthandmovementstagesclassification
_version_	1718389528524226560

A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification

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