  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, which incorporates the trained EEG GCN Block from
EEG_GLT-Net at an adjacency matrix density of 13.39% alongside the RL-centric
Dueling Deep Q Network (Dueling DQN) block. The EEG_RL-Net model showcases
exceptional classification performance, achieving an unprecedented average
accuracy of 96.40% across 20 subjects within 25 milliseconds. This model
illustrates the transformative effect of the RL in EEG MI time point
classification.
