A Bayesian approach for spatio-spectral filter optimization in BCI

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    In this paper, we propose a novel Bayesian frame-work for discriminative feature extraction for motor imagery classification in an EEG-based BCI, in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatio-spectral filter optimization is formulated as the estimation of an unknown posterior pdf that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on two public databases.

    Original languageEnglish
    Title of host publication2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
    Pages22-23
    Number of pages2
    DOIs
    Publication statusPublished - 2013
    Event2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 - Gangwon Province, Korea, Republic of
    Duration: 2013 Feb 182013 Feb 20

    Publication series

    Name2013 International Winter Workshop on Brain-Computer Interface, BCI 2013

    Other

    Other2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
    Country/TerritoryKorea, Republic of
    CityGangwon Province
    Period13/2/1813/2/20

    Keywords

    • Bayesian Frame-work
    • Brain-Computer Interface
    • Motor Imagery Classification
    • Spatio-Spectral Filter Optimization

    ASJC Scopus subject areas

    • Human-Computer Interaction

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