A method for classifying mental tasks in the space of EEG transforms

Renato Cordeiro De Amorim, Boris Mirkin, John Q. Gan

    Research output: Contribution to conferencePaperpeer-review

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    Abstract

    In this article we describe a new method for supervised classification of EEG signals. This method applies to the power spectrum density data and assigns
    class-dependent information weights to individual pixels, so that the decision is defined by the summary weights of the most informative pixel features. We
    experimentally analyze several versions of the approach. The informative features appear to be rather similar among different individuals, thus supporting the view that there are subject independent general brain patterns for the same mental task
    Original languageEnglish
    Publication statusPublished - 2009
    EventUK Workshop on Computational Intelligence - University of Nottingham, Nottingham, United Kingdom
    Duration: 7 Sept 20099 Sept 2009

    Workshop

    WorkshopUK Workshop on Computational Intelligence
    Country/TerritoryUnited Kingdom
    CityNottingham
    Period7/09/099/09/09

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