Northampton Electronic Collection of Theses and Research

Graph Attention Based Spatial Temporal Network for EEG Signal Representation

Msonda, J. R., He, Z. and Lu, C. (2024) Graph Attention Based Spatial Temporal Network for EEG Signal Representation. Advances in Intelligent Systems and Computing. 1453, 286–298. 2194-5357.

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Msonda_et_al_2024_Graph_Attention_Based_Spatial_Temporal_Network_for_EEG_Signal_Representation.pdf - Accepted Version
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Abstract:
Graph attention networks (GATs) based architectures have proved to be powerful at implicitly learning relationships between adjacent nodes in a graph. For electroencephalogram (EEG) signals, however, it is also essential to highlight electrode locations or underlying brain regions which are active when a particular event related potential (ERP) is evoked. Moreover, it is often important to identify corresponding EEG signal time segments within which the ERP is activated. We introduce a GAT Inspired Spatial Temporal (GIST) network that uses multilayer GAT as its base for three attention blocks: edge attentions, followed by node attention and temporal attention layers, which focus on relevant brain regions and time windows for better EEG signal classification performance, and interpretability. We assess the capability of the architecture by using publicly available Transcranial Electrical Stimulation (TES), neonatal pain (NP) and DREAMER EEG datasets. With these datasets, the model achieves competitive performance. Most importantly, the paper presents attention visualisation and suggests ways of interpreting them for EEG signal understanding.
Uncontrolled Keywords:
EEG models, Electroencephalography, Graph neural networks, Attention mechanism, Interpretable machine learning
Creators:
Msonda, J. R., He, Z. and Lu, C.
Faculties, Divisions and Institutes:
Faculties > Faculty of Health & Society > Psychology
Date:
1 February 2024
Date Type:
Publication
Page Range:
286–298
Journal or Publication Title:
Advances in Intelligent Systems and Computing
Volume:
1453
Number of Pages:
13
Language:
English
ISSN:
2194-5357
Status:
Published / Disseminated
Refereed:
Yes
Related URLs:
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