Dimension Reduction in EEG Data using Particle Swarm Optimization

Adham Atyabi, Martin Luerssen, Sean Fitzgibbon, David Powers

    Research output: Contribution to conferencePaperpeer-review

    10 Citations (Scopus)


    EEG data contains high-dimensional data that requires considerable computational power for distinguishing different classes. Dimension reduction is commonly used to reduces the necessary training time of the classifiers with some degree of accuracy lost. The dimension reduction is usually performed on either feature or electrode space. In this study, a new dimension reduction method that reduce the number of electrodes and features using variations of Particle Swarm Optimization (PSO) is used. The variation is in terms of parameter adjustment and adding a mutation operator to the PSO. The results are assessed based on the dimension reduction percentage, the potential of selected electrodes and the degree of performance lost. An Extreme Learning Machine (ELM) is used as the primary classifier to evaluate the sets of electrodes and features selected by PSO. Two alternative classifiers such as Polynomial SVM and Perceptron are used for further evaluation of the reduced dimension data. The results indicate the potential of variations of PSO for reducing up to 99% of the data with minimal performance lost.

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


    ConferenceIEEE CEC 2012
    Period10/07/12 → …


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