Evolutionary Approach for Selection of Optimal EEG Electrode Positions and Features for Classification of Cognitive Tasks

Lahiri, R. and Rakshit, P. and Konar, A. and Nagar, Atulya K. (2016) Evolutionary Approach for Selection of Optimal EEG Electrode Positions and Features for Classification of Cognitive Tasks. In: IEEE Congress on Evolutionary Computation 2016, 24-29 July 2016, Vancouver, Canada.

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10. CEC-17082-Evolutionary Approach for Selection of Optimal EEG Electrode Positions and Features for Classification of Cognitive Tasks.pdf

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Official URL: http://ieeexplore.ieee.org/document/7744411/

Abstract

This paper proposes a novel evolutionary approach to the optimal selection of electrodes as well as relevant EEG features for effective classification of cognitive tasks. The problem has been formulated in the framework of a single objective optimization problem with an aim to simultaneously satisfying three criteria. The first criterion deals with maximization of the correlation between the features of EEG sources before and after the selection of optimal electrodes. The second criterion is concerned with minimization of the mutual information between the features of the selected EEG electrodes. The last criterion aims at maximization of the ratio of the difference between the selected features of the EEG sources between and within any two cognitive tasks. A self-adaptive variant of FA (referred to as SAFA) is proposed to solve the above optimization problem by proficiently balancing the tradeoff between the computational accuracy and the run-time complexity. Experiments undertaken over wide variety of cognitive tasks reveal that the proposed algorithm outperforms the other standard algorithms (applied to the same problem) in terms of accuracy and computational overhead.

Item Type: Conference or Workshop Item (Paper)
Additional Information and Comments: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
Keywords: EEG electrodes; source signal; sink signal; EEG feature; firefly algorithm.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Atulya Nagar
Date Deposited: 09 Jun 2016 11:46
Last Modified: 24 Nov 2016 11:54
URI: http://hira.hope.ac.uk/id/eprint/1441

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