Optimized Deep Learning Approach for Motor Imagery EEG Classification

Motor Imagery (MI)-based electroencephalography (EEG) signal classification plays a pivotal role in enhancing Brain-Computer Interface (BCI) systems, particularly in medical diagnostics, neurorehabilitation, and assistive technologies. However, EEG signals are inherently non-stationary and often contaminated with noise and artifacts, making accurate classification a significant challenge. Additionally, the high dimensionality of raw EEG data further complicates the feature extraction and classification process. To address these issues, we present an optimized deep learning approach that integrates a Deep Neural Network (DNN) with Teacher Learning-Based Optimization (TLBO). This hybrid model is designed to enhance the quality of feature selection, reduce irrelevant information, and improve overall classification performance. The proposed method involves a three-stage pipeline: discrete wavelet transforms (DWT)-based feature extraction, TLBO for selecting the most informative features and mitigating noise, and a DNN for classification. The TLBO algorithm, inspired by the pedagogical process between teachers and students, facilitates global optimization in the feature space. This integration ensures that only the most discriminative EEG features are used for training, thereby improving the robustness and generalization ability of the classification model. Extensive experimental validation has been performed using benchmark datasets from BCI Competition III and IV. The results demonstrate that the proposed approach significantly outperforms traditional classifiers and other deep learning baselines in terms of accuracy, precision, sensitivity, and specificity. For example, classification accuracy improved up to 97.05% in certain frequency bands, surpassing conventional methods such as BN and EBL. These findings highlight the potential of the proposed method for real-time BCI applications, such as motor control for prosthetic devices, wheelchair navigation, and communication systems for individuals with severe motor impairments. This work contributes to the advancement of intelligent BCI systems by offering a scalable, accurate, and computationally efficient solution for MI-EEG signal classification.