Optimized hybrid Transformer-based model for EEG classification: application to wheelchair control

The purpose of this study is to improve the classification of electroencephalogram (EEG) signals for motor imagery-based brain-computer interfaces (MI-BCIs), thus enabling more reliable user interactions in assistive technologies such as autonomous wheelchairs. The study contributes by systematically investigating and comparing the performance of both simple (Transformer, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM)) and hybrid deep learning architectures (Transformer + CNN, Transformer + LSTM, CNN + LSTM) for EEG signal classification using multiple evaluation metrics, including accuracy, ROC AUC, confusion matrices, and prediction time. The findings demonstrate that the Transformer + CNN hybrid model consistently outperforms all other architectures, thereby achieving perfect classification accuracy (1.00), zero misclassifications, flawless AUC scores (1.000), and a competitive inference time (1.17 s), thus making it highly suitable for real-time MI-BCI applications. In contrast, simple models, particularly LSTM, show lower accuracies and reduced stabilities. Overall, this study highlights the advantage of combining attention mechanisms and convolutional architectures to effectively capture both spatial and contextual patterns in nonstationary EEG signals, thus establishing Transformer-based hybrid models as a promising approach for robust, real-time EEG classifications in next-generation MI-BCI systems.