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<https://discovery.ucl.ac.uk/id/eprint/10205463> <http://purl.org/dc/terms/title> "Advancing EEG classification for neurodegenerative conditions using BCI: a graph attention approach with phase synchrony"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10205463> <http://purl.org/ontology/bibo/abstract> "Accurately classifying electroencephalogram (EEG) signals, especially for individuals\r\nwith neurodegenerative conditions such as amyotrophic lateral sclerosis (ALS), poses a significant\r\nchallenge due to high inter-subject and inter-session changes in signal. This study introduces a novel\r\nthree-layer graph attention network (GAT) model for motor imagery (MI) classification, utilizing phase\r\nlocking value (PLV) as the graph input. The GAT model outperforms state-of-the-art deep learning\r\nmethods, demonstrating notable improvements with a two-class accuracy of 74.06% on an ALS dataset\r\n(approximately 320 trials collected over 1-2 months), and 71.89% on the BCI Comp IV 2a Dataset. This\r\nimprovement demonstrates the effectiveness of graph-based representations to enhance classification\r\nperformance for neurodegenerative conditions. There are statistically significant reductions in variance\r\ncompared to state-of-the-art, due to subject-specific attention given by the model during testing. These\r\nresults support the hypothesis that phase-locking value-based graph representations can enhance neural\r\nrepresentations in BCIs, offering promising avenues for more personalized approaches in MI classification.\r\nThis study highlights the potential for further optimizing GAT architectures and feature sets, pointing to\r\nfuture research directions that could improve performance and efficiency in MI classification tasks whilst\r\nestablishing a lightweight methodology."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10205463> <http://purl.org/dc/terms/date> "2025-01-20" .
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