A probabilistic framework for learning robust common spatial patterns

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

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Electroencephalography / methods*
  • Female
  • Humans
  • Models, Neurological*
  • Normal Distribution
  • Signal Processing, Computer-Assisted*