EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks

arXiv:2405.00723v1 Announce Type: cross
Abstract: 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 …

abstract arxiv brain classification cnns computer control convolutional convolutional neural networks cs.ai cs.lg decoding eeg eess.sp framework gnns graph graph neural networks interfaces networks neural networks regard reinforcement reinforcement learning relationships spatial through type