Artificial bee colony based feature selection for motor imagery EEG data

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of Seventh International Conference on Bio-Inspired Computing
Subtitle of host publicationTheories and Applications, BIC-TA 2012
PublisherSpringer Verlag
Pages127-138
Number of pages12
EditionVOL. 2
ISBN (Print)9788132210405
DOIs
Publication statusPublished - 1 Jan 2013
Event7th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2012 - Gwalior, Madhya Pradesh, India
Duration: 14 Dec 201216 Dec 2012

Publication series

NameAdvances in Intelligent Systems and Computing
NumberVOL. 2
Volume202 AISC
ISSN (Print)2194-5357

Conference

Conference7th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2012
CountryIndia
CityGwalior, Madhya Pradesh
Period14/12/1216/12/12

Keywords

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

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