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Sensors 2017, 17(11), 2576; https://doi.org/10.3390/s17112576

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

1
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2
Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Wuhan University of Technology, Ministry of Education, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Received: 4 September 2017 / Revised: 3 November 2017 / Accepted: 3 November 2017 / Published: 8 November 2017
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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Abstract

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. View Full-Text
Keywords: motor imagery; electroencephalography; brain–computer interface; common spatial pattern; firefly algorithm; learning automata motor imagery; electroencephalography; brain–computer interface; common spatial pattern; firefly algorithm; learning automata
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Liu, A.; Chen, K.; Liu, Q.; Ai, Q.; Xie, Y.; Chen, A. Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata. Sensors 2017, 17, 2576.

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