A Prior neurophysiologic knowledge free tensor-based scheme for single trial EEG classification

Publication Type:
Journal Article
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2009, 17 (2), pp. 107 - 115
Issue Date:
Filename Description Size
Thumbnail2011000261OK.pdf1.26 MB
Adobe PDF
Full metadata record
Single trial electroencephalogram (EEG) classification is essential in developing braincomputer interfaces (BCIs). However, popular classification algorithms, e.g., common spatial patterns (CSP), usually highly depend on the prior neurophysiologic knowledge for noise removing, although this knowledge is not always known in practical applications. In this paper, a novel tensor-based scheme is proposed for single trial EEG classification, which performs well without the prior neurophysiologic knowledge. In this scheme, EEG signals are represented in the spatial-spectral-temporal domain by the wavelet transform, the multilinear discriminative subspace is reserved by the general tensor discriminant analysis (GTDA), redundant indiscriminative patterns are removed by Fisher score, and the classification is conducted by the support vector machine (SVM). Applications to three datasets confirm the effectiveness and the robustness of the proposed tensor scheme in analyzing EEG signals, especially in the case of lacking prior neurophysiologic knowledge. © 2006 IEEE.
Please use this identifier to cite or link to this item: