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Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata
Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713053/ https://www.ncbi.nlm.nih.gov/pubmed/29117100 http://dx.doi.org/10.3390/s17112576 |
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author | Liu, Aiming Chen, Kun Liu, Quan Ai, Qingsong Xie, Yi Chen, Anqi |
author_facet | Liu, Aiming Chen, Kun Liu, Quan Ai, Qingsong Xie, Yi Chen, Anqi |
author_sort | Liu, Aiming |
collection | PubMed |
description | Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems. |
format | Online Article Text |
id | pubmed-5713053 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-57130532017-12-07 Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata Liu, Aiming Chen, Kun Liu, Quan Ai, Qingsong Xie, Yi Chen, Anqi Sensors (Basel) Article Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems. MDPI 2017-11-08 /pmc/articles/PMC5713053/ /pubmed/29117100 http://dx.doi.org/10.3390/s17112576 Text en © 2017 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Aiming Chen, Kun Liu, Quan Ai, Qingsong Xie, Yi Chen, Anqi Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata |
title | Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata |
title_full | Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata |
title_fullStr | Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata |
title_full_unstemmed | Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata |
title_short | Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata |
title_sort | feature selection for motor imagery eeg classification based on firefly algorithm and learning automata |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713053/ https://www.ncbi.nlm.nih.gov/pubmed/29117100 http://dx.doi.org/10.3390/s17112576 |
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