Details

Autor(en) / Beteiligte

• Hyohyeong Kang, Hyohyeong Kang
• Yunjun Nam, Yunjun Nam
• Seungjin Choi, Seungjin Choi

Titel

Composite Common Spatial Pattern for Subject-to-Subject Transfer

Ist Teil von

• IEEE signal processing letters, 2009-08, Vol.16 (8), p.683-686

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2009

Quelle

IEEE Xplore / Electronic Library Online (IEL)

Beschreibungen/Notizen

• 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.