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    A Bayesian model averaging methodology for detecting EEG artifacts

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    Authors
    Schetinin, Vitaly
    Maple, Carsten
    Issue Date
    2007
    Subjects
    artifact detection
    electroencephalogram
    machine learning
    uncertainty estimation
    
    Metadata
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    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.
    Citation
    Schetinin, V.; Maple, C.; (2007) A Bayesian Model Averaging Methodology for Detecting EEG Artifacts, Digital Signal Processing, 15th International Conference on , pp.499-502
    Publisher
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
    URI
    http://hdl.handle.net/10547/270594
    DOI
    10.1109/ICDSP.2007.4288628
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4288628
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    1424408822
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICDSP.2007.4288628
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

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