Artificial Bee Colony Based Feature Selection for Motor Imagery EEG Data View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Pratyusha Rakshit , Saugat Bhattacharyya , Amit Konar , Anwesha Khasnobish , D. N. Tibarewala , R. Janarthanan

ABSTRACT

Brain-computer Interface (BCI) has widespread use in Neuro-rehabilitation engineering. Electroencephalograph (EEG) based BCI research aims to decode the various movement related data generated from the motor areas of the brain. One of the issues in BCI research is the presence of redundant data in the features of a given dataset, which not only increases the dimensions but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant features of a dataset to improve the accuracy of classification. For this, we have employed Artificial Bee Colony (ABC) cluster algorithm to reduce the features and have acquired their corresponding accuracy. It is seen that for a reduced features of 200, the highest accuracy of 64.29 %. The results in this paper validate our claim. More... »

PAGES

127-138

References to SciGraph publications

  • 2004-07. A new cluster validity measure and its application to image compression in PATTERN ANALYSIS AND APPLICATIONS
  • Book

    TITLE

    Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012)

    ISBN

    978-81-322-1040-5
    978-81-322-1041-2

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-81-322-1041-2_11

    DOI

    http://dx.doi.org/10.1007/978-81-322-1041-2_11

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1027778406


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