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A Bayesian model averaging methodology for detecting EEG artifacts

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

In this paper we describe a Bayesian Model Averaging (BMA) methodology developed for detecting artifacts in electroencephalograms (EEGs). The EEGs can be heavily corrupted by cardiac, eye movement, muscle and noise artifacts, so that EEG experts need to automatically detect them with a given level of confidence. In theory, the BMA methodology allows experts to evaluate the confidence in decision making most accurately. However, the non- stationary nature of EEGs makes the use of this methodology difficult. In our experiments with the sleep EEGs, the proposed BMA technique is shown to provide a better performance in terms of predictive accuracy.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Publication milestones

  • Published - 01/01/2007

Publication status

Published - 01/01/2007

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

1424408822

ISBN (Electronic)

1424408822

Publication IDs

  • handle.net: 10547/270594
  • Scopus: 47649102117

Host publication title

nan

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