Journal
PATTERN RECOGNITION
Volume 45, Issue 6, Pages 2137-2144Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.04.018
Keywords
Brain-computer interface (BCI); Electroencephalogram (EEG); Mutual information; Feature selection; Bayesian classification
Funding
- Science and Engineering Research Council of A*STAR (Agency for Science, Technology and Research)
- Enterprise Challenge, Prime Minister's Office, Singapore
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The common spatial pattern (CSP) algorithm is effective in decoding the spatial patterns of the corresponding neuronal activities from electroencephalogram (EEG) signal patterns in brain-computer interfaces (BCIs). However, its effectiveness depends on the subject-specific time segment relative to the visual cue and on the temporal frequency band that is often selected manually or heuristically. This paper presents a novel statistical method to automatically select the optimal subject-specific time segment and temporal frequency band based on the mutual information between the spatial-temporal patterns from the EEG signals and the corresponding neuronal activities. The proposed method comprises four progressive stages: multi-time segment and temporal frequency band-pass filtering, CSP spatial filtering, mutual information-based feature selection and nave Bayesian classification. The proposed mutual information-based selection of optimal spatial-temporal patterns and its one-versus-rest multi-class extension were evaluated on single-trial EEG from the BCI Competition IV Datasets IIb and IIa respectively. The results showed that the proposed method yielded relatively better session-to-session classification results compared against the best submission. (C) 2011 Elsevier Ltd. All rights reserved.
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