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An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

Feng, Jian Kui and Jin, Jing and Daly, Ian and Zhou, Jiale and Niu, Yugang and Wang, Xingyu and Cichocki, Andrzej (2019) 'An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System.' Computational Intelligence and Neuroscience, 2019. ISSN 1687-5265

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Abstract

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

Item Type: Article
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 02 Oct 2019 09:43
Last Modified: 02 Oct 2019 10:15
URI: http://repository.essex.ac.uk/id/eprint/25438

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