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EEG channel selection based on sequential backward floating search for motor imagery classification
Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordin...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633952/ https://www.ncbi.nlm.nih.gov/pubmed/36340754 http://dx.doi.org/10.3389/fnins.2022.1045851 |
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author | Tang, Chao Gao, Tianyi Li, Yuanhao Chen, Badong |
author_facet | Tang, Chao Gao, Tianyi Li, Yuanhao Chen, Badong |
author_sort | Tang, Chao |
collection | PubMed |
description | Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods. |
format | Online Article Text |
id | pubmed-9633952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96339522022-11-05 EEG channel selection based on sequential backward floating search for motor imagery classification Tang, Chao Gao, Tianyi Li, Yuanhao Chen, Badong Front Neurosci Neuroscience Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods. Frontiers Media S.A. 2022-10-21 /pmc/articles/PMC9633952/ /pubmed/36340754 http://dx.doi.org/10.3389/fnins.2022.1045851 Text en Copyright © 2022 Tang, Gao, Li and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Tang, Chao Gao, Tianyi Li, Yuanhao Chen, Badong EEG channel selection based on sequential backward floating search for motor imagery classification |
title | EEG channel selection based on sequential backward floating search for motor imagery classification |
title_full | EEG channel selection based on sequential backward floating search for motor imagery classification |
title_fullStr | EEG channel selection based on sequential backward floating search for motor imagery classification |
title_full_unstemmed | EEG channel selection based on sequential backward floating search for motor imagery classification |
title_short | EEG channel selection based on sequential backward floating search for motor imagery classification |
title_sort | eeg channel selection based on sequential backward floating search for motor imagery classification |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633952/ https://www.ncbi.nlm.nih.gov/pubmed/36340754 http://dx.doi.org/10.3389/fnins.2022.1045851 |
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