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Sparse learning of band power features with genetic channel selection for effective classification of EEG signals.

Padfield, Natasha; Ren, Jinchang; Murray, Paul; Zhao, Huimin

Authors

Natasha Padfield

Jinchang Ren

Paul Murray

Huimin Zhao



Abstract

In this paper, we present a genetic algorithm (GA) based band power feature sparse learning (SL) approach for classification of electroencephalogram (EEG) (GABSLEEG) in motor imagery (MI) based brain-computer interfacing (BCI). The band power in the alpha and beta bands was extracted from the EEG segments and used as features to construct the SL dictionary, in which the GA was employed for channel selection. The GABSLEEG system was tested in three functional areas: i) classification of MI data and idle state data; ii) performance with decreased training data size; and iii) computational efficiency. The system was evaluated by dividing the data into training, validation, and testing sets. The proposed GABSLEEG model is found to significantly outperform conventional classifiers, including the support vector machine (SVM) classifier in (i-iii), and the random forest (RF) and the k-nearest neighbour (k-NN) classifiers in (i-ii). The GABSLEEG system consistently had a higher classification accuracy, sensitivity, and specificity. The average accuracy of the proposed system was 99.65%, on BCI Competition IV dataset 1 and 96.08% for BCI Competition III dataset IVa with the idle state included as a class, which was on a par with state-of-the-art SL and even deep learning approaches.

Citation

PADFIELD, N., REN, J., MURRAY, P. and ZHAO, H. 2021. Sparse learning of band power features with genetic channel selection for effective classification of EEG signals. Neurocomputing [online], 463, pages 566-579. Available from: https://doi.org/10.1016/j.neucom.2021.08.067

Journal Article Type Article
Acceptance Date Aug 15, 2021
Online Publication Date Aug 19, 2021
Publication Date Nov 6, 2021
Deposit Date Aug 20, 2021
Publicly Available Date Aug 20, 2022
Journal Neurocomputing
Print ISSN 0925-2312
Electronic ISSN 1872-8286
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 463
Pages 566-579
DOI https://doi.org/10.1016/j.neucom.2021.08.067
Keywords Brain-computer interface (BCI); Motor imagery (MI) electroencephalography (EEG); Sparse learning (SL); Genetic algorithm (GA); Channel selection
Public URL https://rgu-repository.worktribe.com/output/1411813