A probabilistic framework for learning robust common spatial patterns
Name
Wu-2009-A probabilistic fram.pdf
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1019.42 KB
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Checksum (MD5)
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Author(s)
Gao, Shangkai
Wu, Wei
Chen, Zhe
Brown, Emery N.
Date Issued
September 2009
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Wei Wu et al. “A probabilistic framework for learning robust common spatial patterns.” Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE. 2009. 4658-4661. © 2009 Institute of Electrical and Electronics Engineers.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1109/IEMBS.2009.5332646
