A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning

Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person’s intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices s...

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Autores principales:	Amira Echtioui, Ayoub Mlaouah, Wassim Zouch, Mohamed Ghorbel, Chokri Mhiri, Habib Hamam
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	EEG BCI motor imagery Common Spatial Pattern (CSP) Wavelet Packet Decomposition (WPD) deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/ec2125dd63e1406bb67bcc7ef76aac58
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spelling	oai:doaj.org-article:ec2125dd63e1406bb67bcc7ef76aac582021-11-11T15:02:25ZA Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning10.3390/app112199482076-3417https://doaj.org/article/ec2125dd63e1406bb67bcc7ef76aac582021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9948https://doaj.org/toc/2076-3417Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person’s intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices such as exoskeletons, wheelchairs, prostheses, and even independent driving. Therefore, classifying the motor imagery tasks of these signals is important for a Brain-Computer Interface (BCI) system. Classifying the MI tasks from EEG signals is difficult to offer a good decoder due to the dynamic nature of the signal, its low signal-to-noise ratio, complexity, and dependence on the sensor positions. In this paper, we investigate five multilayer methods for classifying MI tasks: proposed methods based on Artificial Neural Network, Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These proposed methods use different spatial and temporal characteristics extracted from raw EEG data. We demonstrate that our proposed CNN1-based method outperforms state-of-the-art machine/deep learning techniques for EEG classification by an accuracy value of 68.77% and use spatial and frequency characteristics on the BCI Competition IV-2a dataset, which includes nine subjects performing four MI tasks (left/right hand, feet, and tongue). The experimental results demonstrate the feasibility of this proposed method for the classification of MI-EEG signals and can be applied successfully to BCI systems where the amount of data is large due to daily recording.Amira EchtiouiAyoub MlaouahWassim ZouchMohamed GhorbelChokri MhiriHabib HamamMDPI AGarticleEEGBCImotor imageryCommon Spatial Pattern (CSP)Wavelet Packet Decomposition (WPD)deep learningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9948, p 9948 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG BCI motor imagery Common Spatial Pattern (CSP) Wavelet Packet Decomposition (WPD) deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	EEG BCI motor imagery Common Spatial Pattern (CSP) Wavelet Packet Decomposition (WPD) deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Amira Echtioui Ayoub Mlaouah Wassim Zouch Mohamed Ghorbel Chokri Mhiri Habib Hamam A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning
description	Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person’s intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices such as exoskeletons, wheelchairs, prostheses, and even independent driving. Therefore, classifying the motor imagery tasks of these signals is important for a Brain-Computer Interface (BCI) system. Classifying the MI tasks from EEG signals is difficult to offer a good decoder due to the dynamic nature of the signal, its low signal-to-noise ratio, complexity, and dependence on the sensor positions. In this paper, we investigate five multilayer methods for classifying MI tasks: proposed methods based on Artificial Neural Network, Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These proposed methods use different spatial and temporal characteristics extracted from raw EEG data. We demonstrate that our proposed CNN1-based method outperforms state-of-the-art machine/deep learning techniques for EEG classification by an accuracy value of 68.77% and use spatial and frequency characteristics on the BCI Competition IV-2a dataset, which includes nine subjects performing four MI tasks (left/right hand, feet, and tongue). The experimental results demonstrate the feasibility of this proposed method for the classification of MI-EEG signals and can be applied successfully to BCI systems where the amount of data is large due to daily recording.
format	article
author	Amira Echtioui Ayoub Mlaouah Wassim Zouch Mohamed Ghorbel Chokri Mhiri Habib Hamam
author_facet	Amira Echtioui Ayoub Mlaouah Wassim Zouch Mohamed Ghorbel Chokri Mhiri Habib Hamam
author_sort	Amira Echtioui
title	A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning
title_short	A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning
title_full	A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning
title_fullStr	A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning
title_full_unstemmed	A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning
title_sort	novel convolutional neural network classification approach of motor-imagery eeg recording based on deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ec2125dd63e1406bb67bcc7ef76aac58
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A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning

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