title: A probabilistic framework for learning robust common spatial patterns.
abstract: Robustness in signal processing is crucial for the purpose of reliably interpreting physiological features from noisy data in biomedical applications. We present a robust algorithm based on the reformulation of a well-known spatial filtering and feature extraction algorithm named Common Spatial Patterns (CSP). We cast the problem of learning CSP into a probabilistic framework, which allows us to gain insights into the algorithm. To address the overfitting problem inherent in CSP, we propose an expectation-maximization (EM) algorithm for learning robust CSP using from a Student-t distribution. The efficacy of the proposed robust algorithm is validated with both simulated and real EEG data.
authors: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. weiwu@neurostat.mit.edu
authors: Wei
authors: W
authors: Wu
authors: Zhe
authors: Z
authors: Chen
authors: Shangkai
authors: S
authors: Gao
authors: Emery N
authors: EN
authors: Brown
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008959:Models, Neurological; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted