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Graph Attention Based Spatial Temporal Network for EEG Signal Representation

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Advances in Computational Intelligence Systems (UKCI 2023)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1453))

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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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Aberystwyth University, Wales, UK

    James Ronald Msonda

  2. Malawi University of Business and Applied Sciences, Blantyre, Malawi

    James Ronald Msonda & Chuan Lu

  3. Department of Psychology, Aberystwyth University, Wales, UK

    Zhimin He

Authors
  1. James Ronald Msonda
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  2. Zhimin He
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  3. Chuan Lu
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Corresponding author

Correspondence to James Ronald Msonda .

Editor information

Editors and Affiliations

  1. Aston University, Birmingham, UK

    Nitin Naik

  2. Cardiff Metropolitan University, Cardiff, UK

    Paul Jenkins

  3. Aston University, Birmingham, UK

    Paul Grace

  4. Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  5. Devi Ahilya University, Indore, India

    Shaligram Prajapat

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Msonda, J.R., He, Z., Lu, C. (2024). Graph Attention Based Spatial Temporal Network for EEG Signal Representation. In: Naik, N., Jenkins, P., Grace, P., Yang, L., Prajapat, S. (eds) Advances in Computational Intelligence Systems. UKCI 2023. Advances in Intelligent Systems and Computing, vol 1453. Springer, Cham. https://doi.org/10.1007/978-3-031-47508-5_23

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