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LPOA-DRN: Deep learning based feature fusion and optimization enabled Deep Residual Network for classification of Motor Imagery EEG signals

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Abstract

In this research, the classification method for MI-based EEG signals has been developed using the Lion Political Optimization Algorithm-based Deep Residual Network (LPOA-based DRN) to address these issues. The proposed model employs the technique of data augmentation to generate the best classification outcomes with additional training examples. By altering the training data, the developed strategy improves efficiency in terms of specificity, accuracy, and sensitivity with values of 0.921, 0.904, and 0.866.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Noorul Islaam University, Noorul Islam Centre for Higher Education, Thuckalay, Tamil Nadu, India

    Gouri M.S & K. S. Vijula Grace

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  1. Gouri M.S
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M.S, G., Grace, K.S.V. LPOA-DRN: Deep learning based feature fusion and optimization enabled Deep Residual Network for classification of Motor Imagery EEG signals. SIViP 17, 2167–2175 (2023). https://doi.org/10.1007/s11760-022-02431-9

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