Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 16, Issue 8, Pages 683-686Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2009.2022557
Keywords
Brain computer interface; common spatial pattern; EEG classification; transfer learning
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Funding
- National Core Research Center for Systems [R31-2008-000-10100-0]
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Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram ( EEG) classification. Most of existing CSP-based methods exploit covariance matrices on a subject-by-subject basis so that inter-subject information is neglected. In this paper we present modifications of CSP for subject-to-subject transfer, where we exploit a linear combination of covariance matrices of subjects in consideration. We develop two methods to determine a composite covariance matrix that is a weighted sum of covariance matrices involving subjects, leading to composite CSP. Numerical experiments on dataset IVa in BCI competition III confirm that our composite CSP methods improve classification performance over the standard CSP (on a subject-by-subject basis), especially in the case of subjects with fewer number of training samples.
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