Bayesian common spatial patterns

Common spatial patterns (CSP) or its probabilistic counterpart, probabilistic CSP (PCSP), is a popular discriminative feature extraction method for automatically classifying electroencephalography (EEG) brain waves. Models for CSP or PCSP are trained on a subject-by-subject basis, so inter-subject information, which might be available when brain waves are measured from multiple subjects who undergo the same mental task, is neglected. In this paper we present a brief overview of our recent work on how Bayesian multi-task learning is applied to multi-subject EEG classification, treating subjects as tasks to capture inter-subject relatedness in Bayesian treatment of PCSP.