Differential evolution with temporal difference Q-learning based feature selection for motor imagery EEG data

Saugat Bhattacharyya, Pratyusha Rakshiti, Amit Konar, D. N. Tibarewala, Swagatam Das, Atulya K. Nagar

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

1 Citation (Scopus)

Abstract

Electroencephalograph (EEG) based Braincomputer Interface (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 Differential Evolution with Temporal Difference Q-Learning (DE-TDQL)-based clustering algorithm to reduce the features and have acquired their corresponding accuracy. Experiments with synthetic and real-world data provide evidence that such an approach leads to improved classification performance. Superiority of the new method is demonstrated by comparing it with three classification methods including Linear Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine-Radial Basis Function. Self-Adaptive Differential Evolution, Differential Evolution/current-to-best/l, Particle Swarm Optimization and Genetic Algorithm-based clustering approaches have also been used here to study the relative performance of the proposed adaptive memetic algorithm-based clustering technique with respect to runtime and classification accuracy.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
PublisherIEEE Computer Society
Pages138-145
Number of pages8
ISBN (Print)9781467358712
DOIs
Publication statusPublished - 1 Jan 2013
Event2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, Singapore
Duration: 16 Apr 201319 Apr 2013

Publication series

NameProceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

Conference

Conference2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
CountrySingapore
CitySingapore
Period16/04/1319/04/13

Keywords

  • brain-computer interface
  • differential evolution with temporal difference q-learning
  • electroencephalography
  • feature selection
  • motor imagery
  • power spectral density

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  • Cite this

    Bhattacharyya, S., Rakshiti, P., Konar, A., Tibarewala, D. N., Das, S., & Nagar, A. K. (2013). Differential evolution with temporal difference Q-learning based feature selection for motor imagery EEG data. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 (pp. 138-145). [6609177] (Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013). IEEE Computer Society. https://doi.org/10.1109/CCMB.2013.6609177