Sun, Hao and Jin, Jing and Daly, Ian and Huang, Yitao and Zhao, Xueqing and Wang, Xingyu and Cichocki, Andrzej (2023) Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. Journal of Neuroscience Methods, 339. p. 109969. DOI https://doi.org/10.1016/j.jneumeth.2023.109969
Sun, Hao and Jin, Jing and Daly, Ian and Huang, Yitao and Zhao, Xueqing and Wang, Xingyu and Cichocki, Andrzej (2023) Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. Journal of Neuroscience Methods, 339. p. 109969. DOI https://doi.org/10.1016/j.jneumeth.2023.109969
Sun, Hao and Jin, Jing and Daly, Ian and Huang, Yitao and Zhao, Xueqing and Wang, Xingyu and Cichocki, Andrzej (2023) Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. Journal of Neuroscience Methods, 339. p. 109969. DOI https://doi.org/10.1016/j.jneumeth.2023.109969
Abstract
Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.
Item Type: | Article |
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Uncontrolled Keywords: | Motor imagery (MI); Electroencephalogram (EEG); Feature learning; Graph representation; Self -attention |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 18 Sep 2023 14:17 |
Last Modified: | 07 Aug 2024 16:53 |
URI: | http://repository.essex.ac.uk/id/eprint/36356 |
Available files
Filename: Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Embargo Date: 6 September 2024