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Conf Proc IEEE Eng Med Biol Soc. 2009;2009:4658-61. doi: 10.1109/IEMBS.2009.5332646.

A probabilistic framework for learning robust common spatial patterns.

Author information

1
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. weiwu@neurostat.mit.edu

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.

PMID:
19963618
DOI:
10.1109/IEMBS.2009.5332646
[Indexed for MEDLINE]

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