An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System
Joint Authors
Wang, Xingyu
Feng, Jian Kui
Jin, Jing
Daly, Ian
Zhou, Jiale
Niu, Yugang
Cichocki, Andrzej
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-13
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Feng, Jian Kui& Jin, Jing& Daly, Ian& Zhou, Jiale& Niu, Yugang& Wang, Xingyu…[et al.]. 2019. An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129587
Modern Language Association (MLA)
Feng, Jian Kui…[et al.]. An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1129587
American Medical Association (AMA)
Feng, Jian Kui& Jin, Jing& Daly, Ian& Zhou, Jiale& Niu, Yugang& Wang, Xingyu…[et al.]. An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129587
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129587