Semi-supervised feature extraction for EEG classification

• Published in: Pattern analysis and applications : PAA Vol. 16; no. 2; pp. 213 - 222
• Main Authors: Tu, Wenting, Sun, Shiliang
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.05.2013; Springer
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data processing. List processing. Character string processing; Electrodiagnosis. Electric activity recording; Exact sciences and technology; Investigative techniques, diagnostic techniques (general aspects); Learning and adaptive systems; Medical sciences; Memory organisation. Data processing; Nervous system; Pattern Recognition; Short Paper; Software; Extreme energy ratio; Density ratio; Semi-supervised learning; Regularization; Feature extraction EEG classification; Capability index; Brain; Energy method; Preservation; Electroencephalography; Calibration; Covariance matrix; Pattern recognition; Adaptive method; Time varying system; Experimental result; Manifold; User interface; Classification; Selection criterion; Spatial filters; Pattern extraction; Spatial representation

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...