Alternative CSP approaches for multimodal distributed BCI data

Stephanie Brandl, Klaus Robert Muller, Wojciech Samek

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

    3 Citations (Scopus)

    Abstract

    Brain-Computer Interfaces (BCIs) are trained to distinguish between two (or more) mental states, e.g., left and right hand motor imagery, from the recorded brain signals. Common Spatial Patterns (CSP) is a popular method to optimally separate data from two motor imagery tasks under the assumption of an unimodal class distribution. In out of lab environments where users are distracted by additional noise sources this assumption may not hold. This paper systematically investigates BCI performance under such distractions and proposes two novel CSP variants, ensemble CSP and 2-step CSP, which can cope with multimodal class distributions. The proposed algorithms are evaluated using simulations and BCI data of 16 healthy participants performing motor imagery under 6 different types of distraction. Both methods are shown to significantly enhance the performance compared to the standard procedure.

    Original languageEnglish
    Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3742-3747
    Number of pages6
    ISBN (Electronic)9781509018970
    DOIs
    Publication statusPublished - 2017 Feb 6
    Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
    Duration: 2016 Oct 92016 Oct 12

    Publication series

    Name2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

    Other

    Other2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
    Country/TerritoryHungary
    CityBudapest
    Period16/10/916/10/12

    Bibliographical note

    Publisher Copyright:
    © 2016 IEEE.

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition
    • Artificial Intelligence
    • Control and Optimization
    • Human-Computer Interaction

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