Multi-class Classification of Motor Imagery EEG Signals Using Deep Learning Models

The accurate classification of Motor Imagery (MI) electroencephalography (EEG) signals is crucial for advancing Brain-Computer Interface (BCI) technologies, particularly for individuals with disabilities. In this study, we present a sophisticated deep learning methodology that systematically evaluates three models CNN, RNN, and BiLSTM, to identify the optimal approach for MI signal classification. Leveraging the BCI Competition IV 2a dataset, we applied a pre-processing step removing three EOG channels and retaining 22 EEG channels, extracting 288 MI epochs, each lasting 3 s. Our findings highlight the superior performance of the proposed RNN model, achieving a remarkable maximum accuracy of 98%. This outcome signifies a significant advancement in MI signal classification, demonstrating the potential of deep learning techniques to enhance BCI precision. The study contributes by introducing a novel methodology and showcasing its efficacy through rigorous evaluation against benchmarks, providing valuable insights for the development of more robust and accurate BCI systems.