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Deep neural network with harmony search based optimal feature selection of EEG signals for motor imagery classification

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Abstract

The brain computer interface (BCI) is a method of measuring brain activity via electrical impulses. However, for measuring brain activity, electrodes are placed on human brain. The purpose of this paper is to investigate a novel electroencephalograph (EEG) motor imagery classification technique with greater accuracy. The EEG output is extremely sensitive to human activity when recorded. As a result, EEG pre-processing is required before meaningful information from an EEG signal can be recovered; however, after pre-processing, key features can be recovered easily. In order to achieve effective classification results, feature selection is crucial. To get the best feature set for motor imagery (MI) classification, the Harmony search algorithm of feature selection is utilised. Finally, long short-term memory (LSTM) deep neural networks are used to classify EEG data. The alpha and beta waves are considered in this paper. For pre-processing and feature extractions of EEG fundamental approaches are considered to prove the utility of LSTM DNN. The results are obtained using computer simulation. The accuracy of the proposed method is found to be greater than 97%.

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  1. Department of ECE, Deenbandhu Chhotu Ram University of Science and Technology-[DCRUST], Muruthal Sonepat, Haryana, India

    Abhilasha Nakra & Manoj Duhan

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  1. Abhilasha Nakra
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  2. Manoj Duhan
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Correspondence to Abhilasha Nakra.

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Nakra, A., Duhan, M. Deep neural network with harmony search based optimal feature selection of EEG signals for motor imagery classification. Int. j. inf. tecnol. 15, 611–625 (2023). https://doi.org/10.1007/s41870-021-00857-x

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