The Impact of PSO based Dimension Reduction on EEG classification

Adham Atyabi, Martin Luerssen, Sean Fitzgibbon, David Powers

    Research output: Contribution to conferencePaperpeer-review

    5 Citations (Scopus)

    Abstract

    The high dimensional nature of EEG data due to large electrode numbers and long task periods is one of the main challenges of studying EEG. Evolutionary alternatives to conventional dimension reduction methods exhibit the advantage of not requiring the entire recording sessions for operation. Particle Swarm Optimization (PSO) is an Evolutionary method that achieves performance through evaluation of several generations of possible solutions. This study investigates the feasibility of a 2 layer PSO structure for synchronous reduction of both electrode and task period dimensions using 4 motor imagery EEG data. The results indicate the potential of the proposed PSO paradigm for dimension reduction with insignificant losses in classification and the practical uses in subject transfer applications.

    Original languageEnglish
    Pages220-231
    Number of pages12
    DOIs
    Publication statusPublished - 2012
    EventBrain Informatics 2012 -
    Duration: 4 Dec 2012 → …

    Conference

    ConferenceBrain Informatics 2012
    Period4/12/12 → …

    Keywords

    • Brain Computer Interface
    • Electroencephalogram
    • Particle Swarm Optimization

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