Enhancing EEG Motor Imagery Time Point Signal Classification through Reinforcement Learning and Graph Neural Networks

• Published in: Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) pp. 130 - 135
• Main Authors: Aung, Htoo Wai, Li, Jiao Jiao, An, Yang, Su, Steven W.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.12.2024
• Subjects: Accuracy; Brain modeling; Brain-computer interfaces; Brain-Computer Interfaces (BCIs); Convolutional neural networks; Electroencephalograph Motor Imagery (EEG MI); Electroencephalography; Graph neural networks; Motors; Pattern classification; Real-time systems; Reinforcement learning; Reinforcement Learning (RL); Spectral Graph Convolutional Neural Networks (GCNs)
• ISSN: 1946-0759
• DOI: 10.1109/ICMLA61862.2024.00024

Brain-Computer Interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph Neural Networks (GNNs) outperform Convolutional Neural Networks (CNNs) in this regard, by leveraging the spatial relationships between EEG electrodes through adjacency matrices. The EEG_GLT-Net framework, featuring the state-of-the-art EEG_GLT adjacency matrix method, has notably enhanced EEG MI signal classification, evidenced by an average accuracy of 83.95% across 20 subjects on the PhysioNet dataset. This significantly exceeds the 76.10% accuracy rate achieved using the Pearson Correlation Coefficient (PCC) method within the same framework. In this research, we advance the field by applying a Reinforcement Learning (RL) approach to the classification of EEG MI signals. Our innovative method empowers the RL agent, enabling not only the classification of EEG MI data points with higher accuracy, but effective identification of EEG MI data points that are less distinct. We present the EEG_RL-Net, an enhancement of the EEG_GLT-Net framework. The EEG_RL-Net model showcases exceptional classification performance, achieving an unprecedented average accuracy of 95.36% across 20 subjects within 18.20 milliseconds. This model illustrates the transformative effect of the RL in EEG MI real time point classification.