title: Probabilistic Common Spatial Patterns for Multichannel EEG Analysis.
abstract: Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms. The proposed framework enables us to resolve the overfitting issue of CSP in a principled manner. We derive statistical inference algorithms that can alleviate the issue of local optima. In particular, an efficient algorithm based on eigendecomposition is developed for maximum a posteriori (MAP) estimation in the case of isotropic noise. For more general cases, a variational algorithm is developed for group-wise sparse Bayesian learning for the P-CSP model and for automatically determining the model size. The two proposed algorithms are validated on a simulated data set. Their practical efficacy is also demonstrated by successful applications to single-trial classifications of three motor imagery EEG data sets and by the spatio-temporal pattern analysis of one EEG data set recorded in a Stroop color naming task.
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.forename: Zhe
authors.initials: Z
authors.lastname: Chen
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Gao
authors.forename: Yuanqing
authors.initials: Y
authors.lastname: Li
authors.forename: Emery N
authors.initials: EN
authors.lastname: Brown
authors.forename: Shangkai
authors.initials: S
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004569:Electroencephalography / Q000145:classification*; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted