eprintid: 10205463
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/20/54/63
datestamp: 2025-03-03 11:25:15
lastmod: 2025-03-03 11:25:15
status_changed: 2025-03-03 11:25:15
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Patel, Rishan
creators_name: Zhu, Ziyue
creators_name: Bryson, Barney
creators_name: Carlson, Tom
creators_name: Jiang, Dai
creators_name: Demosthenous, Andreas
title: Advancing EEG classification for neurodegenerative conditions using BCI: a graph attention approach with phase synchrony
ispublished: pub
divisions: UCL
divisions: B04
divisions: F46
keywords: motor imagery, amyotrophic lateral sclerosis, graph attention networks, electroencephalography,
brain computer interface (BCI)
note: 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.
abstract: 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.
date: 2025-01-20
date_type: published
publisher: ELS Publishing Co. Limited
official_url: https://doi.org/10.55092/neuroelectronics20250001
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2354915
doi: 10.55092/neuroelectronics20250001
lyricists_name: Patel, Rishan
lyricists_id: RJPAT28
actors_name: Patel, Rishan
actors_id: RJPAT28
actors_role: owner
full_text_status: public
publication: Neuroelectronics
volume: 2
number: 1
article_number: 0001
issn: 3006-1032
citation:        Patel, Rishan;    Zhu, Ziyue;    Bryson, Barney;    Carlson, Tom;    Jiang, Dai;    Demosthenous, Andreas;      (2025)    Advancing EEG classification for neurodegenerative conditions using BCI: a graph attention approach with phase synchrony.                   Neuroelectronics , 2  (1)    , Article 0001.  10.55092/neuroelectronics20250001 <https://doi.org/10.55092/neuroelectronics20250001>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10205463/1/Final%20Submission.pdf