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A probabilistic framework for learning robust common spatial patterns

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4 Author(s)

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.

Published in:

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Date of Conference:

3-6 Sept. 2009

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