Multiplying the Mileage of Your Dataset with Subwindowing

Adham Atyabi, Sean Fitzgibbon, David Powers

    Research output: Contribution to conferencePaper

    11 Citations (Scopus)

    Abstract

    This study is focused on improving the classification performance of EEG data through the use of some data restructuring methods. In this study, the impact of having more training instances/samples vs. using shorter window sizes is investigated. The BCI2003 IVa dataset is used to examine the results. The results not surprisingly indicate that, up to a certain point, having higher numbers of training instances significantly improves the classification performance while the use of shorter window sizes tends to worsen performance in a way that usually cannot fully be compensated for by the additional instances, but tends to provide useful gain in overall performance for small divisors into two or three subepochs. We have moreover determined that use of an incomplete set of overlapping windows can have little effect, and is inapplicable for the smallest divisors, but that use of overlapping subepochs from three specific non-overlapping areas (start, middle and end) of a superepoch tends to contribute significant additional information. Examination of a division into five equal non-overlapping areas indicates that for some subjects the first or last fifth contributes significantly less information than the middle three fifths.

    Original languageEnglish
    Pages173-184
    Number of pages12
    DOIs
    Publication statusPublished - 20 Sep 2011
    EventBrain Informatics 2011 -
    Duration: 7 Sep 2011 → …

    Conference

    ConferenceBrain Informatics 2011
    Period7/09/11 → …

    Keywords

    • Electroencephalogram
    • Overlapping window
    • Window size

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  • Cite this

    Atyabi, A., Fitzgibbon, S., & Powers, D. (2011). Multiplying the Mileage of Your Dataset with Subwindowing. 173-184. Paper presented at Brain Informatics 2011, . https://doi.org/10.1007/978-3-642-23605-1_19