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The EEG signals are perceived through the two fundamental processes such as feature extraction and characterization process. This research concentrates on developing a predominant MI categorization model utilizing deep learning techniques. The prominence of this research relies on the combined features + proposed PROA-based RideNN process known as holo-entropy-based WPD, which extracts the most dominant feature from the EEG signals. The extracted features enhance the performance of the RideNN classifier. The analysis is done by utilizing the BCI Competition-IV-2a, -2b, and GigaScience datasets with respect to performance parameters, such as specificity, accuracy, and sensitivity. 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