The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

The Brain-Computer Interface (BCI) permits persons with impairments to interact with the real world without using the neuromuscular pathways. BCIs are based on artificial intelligence piloted systems. They collect brain activity patterns linked to the mental process and transform them into commands...

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Autores principales:	Abdulhamit Subasi, Saeed Mian Qaisar
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/4d06db8b704e4a1c85745d348a62f8c2
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spelling	oai:doaj.org-article:4d06db8b704e4a1c85745d348a62f8c22021-11-22T01:09:45ZThe Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface2040-230910.1155/2021/1970769https://doaj.org/article/4d06db8b704e4a1c85745d348a62f8c22021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1970769https://doaj.org/toc/2040-2309The Brain-Computer Interface (BCI) permits persons with impairments to interact with the real world without using the neuromuscular pathways. BCIs are based on artificial intelligence piloted systems. They collect brain activity patterns linked to the mental process and transform them into commands for actuators. The potential application of BCI systems is in the rehabilitation centres. In this context, a novel method is devised for automated identification of the Motor Imagery (MI) tasks. The contribution is an effective hybridization of the Multiscale Principal Component Analysis (MSPCA), Wavelet Packet Decomposition (WPD), statistical features extraction from subbands, and ensemble learning-based classifiers for categorization of the MI tasks. The intended electroencephalogram (EEG) signals are segmented and denoised. The denoising is achieved with a Daubechies algorithm-based wavelet transform (WT) incorporated in the MSPCA. The WT with the 5th level of decomposition is used. Onward, the Wavelet Packet Decomposition (WPD), with the 4th level of decomposition, is used for subbands formation. The statistical features are selected from each subband, namely, mean absolute value, average power, standard deviation, skewness, and kurtosis. Also, ratios of absolute mean values of adjacent subbands are computed and concatenated with other extracted features. Finally, the ensemble machine learning approach is used for the classification of MI tasks. The usefulness is evaluated by using the BCI competition III, MI dataset IVa. Results revealed that the suggested ensemble learning approach yields the highest classification accuracies of 98.69% and 94.83%, respectively, for the cases of subject-dependent and subject-independent problems.Abdulhamit SubasiSaeed Mian QaisarHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Abdulhamit Subasi Saeed Mian Qaisar The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface
description	The Brain-Computer Interface (BCI) permits persons with impairments to interact with the real world without using the neuromuscular pathways. BCIs are based on artificial intelligence piloted systems. They collect brain activity patterns linked to the mental process and transform them into commands for actuators. The potential application of BCI systems is in the rehabilitation centres. In this context, a novel method is devised for automated identification of the Motor Imagery (MI) tasks. The contribution is an effective hybridization of the Multiscale Principal Component Analysis (MSPCA), Wavelet Packet Decomposition (WPD), statistical features extraction from subbands, and ensemble learning-based classifiers for categorization of the MI tasks. The intended electroencephalogram (EEG) signals are segmented and denoised. The denoising is achieved with a Daubechies algorithm-based wavelet transform (WT) incorporated in the MSPCA. The WT with the 5th level of decomposition is used. Onward, the Wavelet Packet Decomposition (WPD), with the 4th level of decomposition, is used for subbands formation. The statistical features are selected from each subband, namely, mean absolute value, average power, standard deviation, skewness, and kurtosis. Also, ratios of absolute mean values of adjacent subbands are computed and concatenated with other extracted features. Finally, the ensemble machine learning approach is used for the classification of MI tasks. The usefulness is evaluated by using the BCI competition III, MI dataset IVa. Results revealed that the suggested ensemble learning approach yields the highest classification accuracies of 98.69% and 94.83%, respectively, for the cases of subject-dependent and subject-independent problems.
format	article
author	Abdulhamit Subasi Saeed Mian Qaisar
author_facet	Abdulhamit Subasi Saeed Mian Qaisar
author_sort	Abdulhamit Subasi
title	The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface
title_short	The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface
title_full	The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface
title_fullStr	The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface
title_full_unstemmed	The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface
title_sort	ensemble machine learning-based classification of motor imagery tasks in brain-computer interface
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/4d06db8b704e4a1c85745d348a62f8c2
work_keys_str_mv	AT abdulhamitsubasi theensemblemachinelearningbasedclassificationofmotorimagerytasksinbraincomputerinterface AT saeedmianqaisar theensemblemachinelearningbasedclassificationofmotorimagerytasksinbraincomputerinterface AT abdulhamitsubasi ensemblemachinelearningbasedclassificationofmotorimagerytasksinbraincomputerinterface AT saeedmianqaisar ensemblemachinelearningbasedclassificationofmotorimagerytasksinbraincomputerinterface
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The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

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