A Probabilistic Framework for Learning Robust Common Spatial Patterns

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:4658-61. DOI: 10.1109/IEMBS.2009.5332646
Source: IEEE Xplore

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.

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Available from: Xiaorong Gao, Jul 18, 2014
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