Search Results for author:
Found 2 papers, 0 papers with code
EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks
Brain-Computer Interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control.
EEG_GLT-Net: Optimising EEG Graphs for Real-time Motor Imagery Signals Classification
Our paper introduces the EEG Graph Lottery Ticket (EEG_GLT) algorithm, an innovative technique for constructing adjacency matrices for EEG channels.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
