Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 6; pp. 1865 - 1873
• Main Authors: Arvaneh, Mahnaz, Guan, Cuntai, Ang, Kai Keng, Quek, Chai
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-06-2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Applied sciences; Artificial Intelligence; Biological and medical sciences; Brain-computer interface (BCI); Computer science; control theory; systems; Computer systems and distributed systems. User interface; Computerized, statistical medical data processing and models in biomedicine; Covariance matrix; Databases, Factual; EEG channel selection; Eigenvalues and eigenfunctions; Electrocardiography - methods; Electrodiagnosis. Electric activity recording; Electroencephalography; Exact sciences and technology; Humans; Imagination; Investigative techniques, diagnostic techniques (general aspects); Medical management aid. Diagnosis aid; Medical sciences; Motor Activity; motor imagery; Nervous system; Neural Prostheses; Optimization; Signal Processing, Computer-Assisted; Software; sparse common spatial pattern (SCSP); Studies; Support vector machines; Testing; Electrophysiology; EEG channel selection; sparse common spatial pattern (SCSP); Man machine dialogue; motor imagery; Electroencephalography; Brain―computer interface (BCI); Pattern recognition; Optimization; Brain-computer interface; Electrodiagnosis; Accuracy; User interface; Biomedical engineering
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2011.2131142

Multichannel EEG is generally used in brain-computer interfaces (BCIs), whereby performing EEG channel selection 1) improves BCI performance by removing irrelevant or noisy channels and 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, common spatial pattern, and regularized common spatial pattern 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).