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A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Pal, Monalisa and Bhattacharyya, Saugat and Roy, Shounak and Konar, Amit and Tibarewala, DN and Janarthanan, R (2014) A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification. In: International Conference on Signal Processing and Communications (SPCOM), JUL 22-25, 2014, Banaglore, INDIA.

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Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Item Type: Conference Proceedings
Publisher: IEEE
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords: Discrete Wavelet Transform; Brain-Computer Interfacing; Bacterial Foraging Optimization Algorithm; Learning Automata; Distance Likelihood Ratio Test
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 30 Dec 2015 06:07
Last Modified: 30 Dec 2015 06:07
URI: http://eprints.iisc.ac.in/id/eprint/52980

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