Artificial bee colony based feature selection for motor imagery EEG data

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

doi:10.1007/978-81-322-1041-2_11

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 proceeding › Conference contribution › peer-review

19 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 language	English
Title of host publication	Proceedings of Seventh International Conference on Bio-Inspired Computing
Subtitle of host publication	Theories and Applications, BIC-TA 2012
Publisher	Springer Verlag
Pages	127-138
Number of pages	12
Edition	VOL. 2
ISBN (Print)	9788132210405
DOIs	https://doi.org/10.1007/978-81-322-1041-2_11
Publication status	Published (in print/issue) - 1 Jan 2013
Event	7th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2012 - Gwalior, Madhya Pradesh, India Duration: 14 Dec 2012 → 16 Dec 2012

Publication series

Name	Advances in Intelligent Systems and Computing
Number	VOL. 2
Volume	202 AISC
ISSN (Print)	2194-5357

Conference

Conference	7th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2012
Country/Territory	India
City	Gwalior, Madhya Pradesh
Period	14/12/12 → 16/12/12

Keywords

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

Access to Document

10.1007/978-81-322-1041-2_11

Cite this

Rakshit, P., Bhattacharyya, S., Konar, A., Khasnobish, A., Tibarewala, D. N., & Janarthanan, R. (2013). Artificial bee colony based feature selection for motor imagery EEG data. In Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2012 (VOL. 2 ed., pp. 127-138). (Advances in Intelligent Systems and Computing; Vol. 202 AISC, No. VOL. 2). Springer Verlag. https://doi.org/10.1007/978-81-322-1041-2_11

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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.",

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Rakshit, P, Bhattacharyya, S, Konar, A, Khasnobish, A, Tibarewala, DN & Janarthanan, R 2013, Artificial bee colony based feature selection for motor imagery EEG data. in Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2012. VOL. 2 edn, Advances in Intelligent Systems and Computing, no. VOL. 2, vol. 202 AISC, Springer Verlag, pp. 127-138, 7th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2012, Gwalior, Madhya Pradesh, India, 14/12/12. https://doi.org/10.1007/978-81-322-1041-2_11

Artificial bee colony based feature selection for motor imagery EEG data. / Rakshit, Pratyusha; Bhattacharyya, Saugat; Konar, Amit et al.
Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2012. VOL. 2. ed. Springer Verlag, 2013. p. 127-138 (Advances in Intelligent Systems and Computing; Vol. 202 AISC, No. VOL. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Tibarewala, D. N.

AU - Janarthanan, R.

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Rakshit P, Bhattacharyya S, Konar A, Khasnobish A, Tibarewala DN, Janarthanan R. Artificial bee colony based feature selection for motor imagery EEG data. In Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2012. VOL. 2 ed. Springer Verlag. 2013. p. 127-138. (Advances in Intelligent Systems and Computing; VOL. 2). doi: 10.1007/978-81-322-1041-2_11

Artificial bee colony based feature selection for motor imagery EEG data

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Keywords

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