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EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives
Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain’s motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9774545/ https://www.ncbi.nlm.nih.gov/pubmed/36550932 http://dx.doi.org/10.3390/bioengineering9120726 |
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author | Abdullah, Faye, Ibrahima Islam, Md Rafiqul |
author_facet | Abdullah, Faye, Ibrahima Islam, Md Rafiqul |
author_sort | Abdullah, |
collection | PubMed |
description | Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain’s motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10–30% of total channels, provided excellent performance compared to other existing studies. |
format | Online Article Text |
id | pubmed-9774545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97745452022-12-23 EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives Abdullah, Faye, Ibrahima Islam, Md Rafiqul Bioengineering (Basel) Review Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain’s motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10–30% of total channels, provided excellent performance compared to other existing studies. MDPI 2022-11-24 /pmc/articles/PMC9774545/ /pubmed/36550932 http://dx.doi.org/10.3390/bioengineering9120726 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Abdullah, Faye, Ibrahima Islam, Md Rafiqul EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives |
title | EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives |
title_full | EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives |
title_fullStr | EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives |
title_full_unstemmed | EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives |
title_short | EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives |
title_sort | eeg channel selection techniques in motor imagery applications: a review and new perspectives |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9774545/ https://www.ncbi.nlm.nih.gov/pubmed/36550932 http://dx.doi.org/10.3390/bioengineering9120726 |
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