A Real-Time Framework for EEG Signal Decoding With Graph Neural Networks and Reinforcement Learning

Area of Science:

• Neuroscience
• Machine Learning
• Biomedical Engineering

Background:

• Brain-computer interfaces (BCIs) require accurate decoding of electroencephalography (EEG) motor imagery (MI) signals.
• Graph neural networks (GNNs) improve EEG MI classification by utilizing spatial electrode relationships via adjacency matrices.
• Existing GNN frameworks like EEG_GLT-Net show promise but can be further enhanced.

Purpose of the Study:

• To develop an advanced BCI system for enhanced EEG MI signal classification.
• To investigate the application of reinforcement learning (RL) for improved accuracy and identification of subtle EEG MI signals.
• To introduce the EEG_RL-Net, an enhanced GNN framework incorporating RL.

Main Methods:

• The study integrated a trained EEG Graph Convolutional Network (GCN) block from EEG_GLT-Net with a dueling deep Q network (DQN) for RL-based classification.
• The novel EEG_RL-Net was tested on the PhysioNet dataset, analyzing EEG MI signals from 20 subjects.
• Adjacency matrix density was optimized to 13.39% within the EEG_GLT-Net framework.

Main Results:

• EEG_RL-Net achieved a superior average classification accuracy of 96.40% across 20 subjects.
• This represents a significant improvement over the 83.95% accuracy of the EEG_GLT-Net and 76.10% of the Pearson correlation coefficient (PCC) method.
• The model demonstrated high performance within a 25 ms timeframe, highlighting its efficiency.

Conclusions:

• Reinforcement learning significantly enhances the classification of EEG motor imagery signals.
• EEG_RL-Net offers a powerful new approach for BCI applications, improving accuracy and identifying less distinct EEG MI data points.
• The findings underscore the potential of RL in advancing BCI technology for more effective device control.