Motor Imagery EEG Signal Classification Using Optimized Convolutional Neural Network

Motor Imagery (MI) signals help the Brain-Computer Interface framework (BCI) to enable the binding of the human brain to external devices. Thus, both BCI and MI together are instrumental in enhancing the lives of patients affected by motor neuron disorders. A novel MIElectroencephalography (EEG) signal identification and classification approach is proposed in this work. An error-free extraction algorithm is required to extract and classify the temporal and spatial features successfully. This paper proposes the Hilbert Transform (HT) for band energy analysis and Gabor Filter for the selection of optimal frequency band. In this work, the Wavelet Packet Decomposition (WPD) algorithm is used for feature extraction and it decomposes the signal into high and low-frequency components before extracting band coefficients. Moreover, the Convolution Neural Network (CNN) classifier is employed for the classification of MI-EEG tasks. The classification accuracy of the CNN classifier is enhanced using Sea Lion Optimization (SLno) algorithm. The approach is verified using MATLAB and the results are substantially better than those found in the current research, with an average classification accuracy rate of 96.44% by employing a smaller number of criteria, lessening resource consumption, and eliminating the influence of individual differences. The recommended method minimizes classification computation time while enhancing classification accuracy.