Document Detail

Optimizing the Channel Selection and Classification Accuracy in EEG-based BCI.
MedLine Citation:
PMID:  21427014     Owner:  NLM     Status:  Publisher    
Multi-channel EEG is generally used in Brain- Computer-Interfaces (BCIs) whereby performing EEG channel selection (1) improves BCI performance by removing irrelevant or noisy channels; (2) enhances user convenience from the use of lesser channels. This paper proposes a novel Sparse Common Spatial Pattern (SCSP) algorithm for EEG channel selection. The proposed SCSP algorithm is formulated as an optimization problem to select the least number of channels within a constraint of classification accuracy. As such, the proposed approach can be customized to yield the best classification accuracy by removing the noisy and irrelevant channels, or retain the least number of channels without compromising the classification accuracy obtained by using all the channels. The proposed SCSP algorithm is evaluated using two motor imagery datasets, one with a moderate number of channels and another with a large number of channels. In both datasets, the proposed SCSP channel selection significantly reduced the number of channels, and outperformed existing channel selection methods based on Fisher Criterion, Mutual Information, Support Vector Machine, CSP and Regularized CSP in classification accuracy. The proposed SCSP algorithm also yielded an average improvement of 10% in classification accuracy compared to the use of three channels (C3, C4 and Cz).
M Arvaneh; C Guan; K Ang; H Quek
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-3-22
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  -     ISSN:  1558-2531     ISO Abbreviation:  -     Publication Date:  2011 Mar 
Date Detail:
Created Date:  2011-3-23     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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