Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and t...

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Autores principales:	Jing-Shan Huang, Wan-Shan Liu, Bin Yao, Zhan-Xiang Wang, Si-Fang Chen, Wei-Fang Sun
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	electroencephalogram (EEG) motor imagery (MI) wavelet packet decomposition (WPD) residual convolutional neural networks Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/6f2fa92d5cf5463a8bbcb2c652b17950
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spelling	oai:doaj.org-article:6f2fa92d5cf5463a8bbcb2c652b179502021-11-17T05:30:51ZElectroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks1662-453X10.3389/fnins.2021.774857https://doaj.org/article/6f2fa92d5cf5463a8bbcb2c652b179502021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.774857/fullhttps://doaj.org/toc/1662-453XThe classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.Jing-Shan HuangJing-Shan HuangWan-Shan LiuWan-Shan LiuBin YaoBin YaoZhan-Xiang WangZhan-Xiang WangZhan-Xiang WangSi-Fang ChenWei-Fang SunFrontiers Media S.A.articleelectroencephalogram (EEG)motor imagery (MI)wavelet packet decomposition (WPD)residualconvolutional neural networksNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electroencephalogram (EEG) motor imagery (MI) wavelet packet decomposition (WPD) residual convolutional neural networks Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	electroencephalogram (EEG) motor imagery (MI) wavelet packet decomposition (WPD) residual convolutional neural networks Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Jing-Shan Huang Jing-Shan Huang Wan-Shan Liu Wan-Shan Liu Bin Yao Bin Yao Zhan-Xiang Wang Zhan-Xiang Wang Zhan-Xiang Wang Si-Fang Chen Wei-Fang Sun Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
description	The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.
format	article
author	Jing-Shan Huang Jing-Shan Huang Wan-Shan Liu Wan-Shan Liu Bin Yao Bin Yao Zhan-Xiang Wang Zhan-Xiang Wang Zhan-Xiang Wang Si-Fang Chen Wei-Fang Sun
author_facet	Jing-Shan Huang Jing-Shan Huang Wan-Shan Liu Wan-Shan Liu Bin Yao Bin Yao Zhan-Xiang Wang Zhan-Xiang Wang Zhan-Xiang Wang Si-Fang Chen Wei-Fang Sun
author_sort	Jing-Shan Huang
title	Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
title_short	Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
title_full	Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
title_fullStr	Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
title_full_unstemmed	Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
title_sort	electroencephalogram-based motor imagery classification using deep residual convolutional networks
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/6f2fa92d5cf5463a8bbcb2c652b17950
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_version_	1718425931825020928

Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks

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