Semi-supervised feature extraction for EEG classification

Pattern Anal. Appl., pp. 213-222, 2013.

Cited by: 19|Views7
EI WOS SCOPUS

Abstract:

Two semi-supervised feature extraction methods are proposed for electroencephalogram (EEG) classification. They aim to alleviate two important limitations in brain---computer interfaces (BCIs). One is on the requirement of small training sets owing to the need of short calibration sessions. The second is the time-varying property of signa...More

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