%0 Journal Article
%@ 3006-1032
%A Patel, Rishan
%A Zhu, Ziyue
%A Bryson, Barney
%A Carlson, Tom
%A Jiang, Dai
%A Demosthenous, Andreas
%D 2025
%F discovery:10205463
%I ELS Publishing Co. Limited
%J Neuroelectronics
%K motor imagery, amyotrophic lateral sclerosis, graph attention networks, electroencephalography,  brain computer interface (BCI)
%N 1
%T Advancing EEG classification for neurodegenerative conditions using BCI: a graph attention approach with phase synchrony
%U https://discovery.ucl.ac.uk/id/eprint/10205463/
%V 2
%X Accurately classifying electroencephalogram (EEG) signals, especially for individuals  with neurodegenerative conditions such as amyotrophic lateral sclerosis (ALS), poses a significant  challenge due to high inter-subject and inter-session changes in signal. This study introduces a novel  three-layer graph attention network (GAT) model for motor imagery (MI) classification, utilizing phase  locking value (PLV) as the graph input. The GAT model outperforms state-of-the-art deep learning  methods, demonstrating notable improvements with a two-class accuracy of 74.06% on an ALS dataset  (approximately 320 trials collected over 1-2 months), and 71.89% on the BCI Comp IV 2a Dataset. This  improvement demonstrates the effectiveness of graph-based representations to enhance classification  performance for neurodegenerative conditions. There are statistically significant reductions in variance  compared to state-of-the-art, due to subject-specific attention given by the model during testing. These  results support the hypothesis that phase-locking value-based graph representations can enhance neural  representations in BCIs, offering promising avenues for more personalized approaches in MI classification.  This study highlights the potential for further optimizing GAT architectures and feature sets, pointing to  future research directions that could improve performance and efficiency in MI classification tasks whilst  establishing a lightweight methodology.
%Z Copyright©2025 by the authors. Published by ELS Publishing. This work is licensed under a Creative Commons  Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any  medium provided the original work is properly cited.