Deep Learning-Based Brain-Computer Interface for Accurate EEG Signal Decoding

Abstract

One of the most cutting-edge technologies available today, brain-computer interface (BCI) systems rely heavily on the interpretation of electroencephalogram (EEG) data to allow for real communication between the human brain and an external device. This research presents a deep learning-based EEG categorization algorithm that might enhance BCI accuracy and real-time performance. The work utilizes the BCI Competition IV 2a dataset and focuses on enhancing the decoding of motor imagery EEG signals using sophisticated machine learning and deep learning architectures including TCFormer Fusion, SNN, ATCNet, CNN, and RNN. Ensuring sufficient representation of temporal and spatial interdependence, the proposed system incorporates preprocessing, feature extraction, and classification of EEG signals. In comparison to the other models, the RNN model outperforms them all with impressively high values for accuracy (ACC)—94.7%—precision (PRE)—recall (REC)—and F1-score (F1)—94.8%. The results demonstrate the efficacy of RNNs for learning sequential EEG patterns. In summary, the proposed method offers substantial enhancements in EEG signal decoding ACC, showcasing its potential suitability for dependable and effective BCI systems in healthcare and assistive technology.