Bayesian common spatial patterns with Dirichlet process priors for multi-subject EEG classification
Hyohyeong Kang, Seungjin Choi · 2012
Multi-subject electroencephalography (EEG) classification involves the categorization of brain waves measured from multiple subjects, each of whom undergoes the same mental task. Common spatial patterns (CSP) or probabilistic CSP (PCSP) are widely used for extracting discriminative features from EEG, although they are trained on a subject-by-subject basis and inter-subject information is neglected. Moreover, the performance is degraded when only a few training samples are available for each subject. In this paper, we present a method for Bayesian CSP with Dirichlet process (DP) priors, where spatial patterns (corresponding to basis vectors) are simultaneously learned and clustered across subjects using variational Bayesian inference, which facilitates a flexible mixture model where the number of components are also learned. Spatial patterns in the same cluster share the hyperparameters of their prior distributions, which means information transfer is facilitated among subjects with similar spatial patterns. Numerical experiments using the BCI competition IV 2a dataset demonstrated the high performance of our method, compared with existing PCSP and Bayesian CSP methods with a single prior distribution.