Composite Common Spatial Pattern for Subject-to-Subject Transfer

• Published in: IEEE signal processing letters Vol. 16; no. 8; pp. 683 - 686
• Main Authors: Hyohyeong Kang, Hyohyeong Kang, Yunjun Nam, Yunjun Nam, Seungjin Choi, Seungjin Choi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain computer interface; Brain computer interfaces; Classification; common spatial pattern; Computer interfaces; Control systems; Covariance; Covariance matrix; EEG classification; Electric potential; Electroencephalography; Feature extraction; Mathematical analysis; Mathematical models; Matrices; Matrix methods; Scalp; Spatial filters; transfer learning; Wheelchairs
• DOI: 10.1109/LSP.2009.2022557
• ISSN: 1070-9908

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.