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Artificial Bee Colony Based Feature Selection for Motor Imagery EEG Data

  • Pratyusha Rakshit
  • Saugat Bhattacharyya
  • Amit Konar
  • Anwesha Khasnobish
  • D. N. Tibarewala
  • R. Janarthanan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 202)

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.

Keywords

Brain-computer interface Electroencephalography Motor imagery Feature selection Power spectral density Artificial bee colony 

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Copyright information

© Springer India 2013

Authors and Affiliations

  • Pratyusha Rakshit
    • 1
  • Saugat Bhattacharyya
    • 2
  • Amit Konar
    • 1
  • Anwesha Khasnobish
    • 2
  • D. N. Tibarewala
    • 2
  • R. Janarthanan
    • 1
  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.School of Bioscience and EngineeringJadavpur UniversityKolkataIndia

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