Adapting Subject-Independent Task-Specific EEG Feature Masks using PSO

Adham Atyabi, Martin Luerssen, Sean Fitzgibbon, David Powers

    Research output: Contribution to conferencePaperpeer-review

    7 Citations (Scopus)

    Abstract

    Dimension reduction is an important step toward asynchronous EEG based BCI systems, with EA based Feature/ Electrode Reduction (FR/ER) methods showing significant potential for this purpose. A PSO based approach can reduce 99% of the EEG data in this manner while demonstrating generalizability through the use of 3 new subsets of features/electrodes that are selected based on the best performing subset on the validation set, the best performing subset on the testing set, and the most commonly used features/electrodes in the swarm. This study is focused on applying the subsets generated from 4 subjects on a 5th one. Two schemes for this are implemented based on i) extracting separate subsets of feature/electrodes for each subject (out of 4 subjects) and combining the final products together for use with the 5th subject, and ii) concatenating the preprocessed EEG data of 4 subjects together and extracting the desired subset with PSO for use with the 5th subject. The results indicate the feasibility of generating subsets of feature/electrode indexes that are task specific and can be used on new subjects.

    Original languageEnglish
    DOIs
    Publication statusPublished - 4 Oct 2012
    EventIEEE CEC 2012 -
    Duration: 10 Jul 2012 → …

    Conference

    ConferenceIEEE CEC 2012
    Period10/07/12 → …

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