TR2021-136

EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals


    •  Demir, A., Koike-Akino, T., Wang, Y., Erdogmus, D., Haruna, M., "EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals", International IEEE EMBS Conference on Neural Engineering, October 2021.
      BibTeX TR2021-136 PDF Video Presentation
      • @inproceedings{Demir2021oct,
      • author = {Demir, Andac and Koike-Akino, Toshiaki and Wang, Ye and Erdogmus, Deniz and Haruna, Masaki},
      • title = {EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals},
      • booktitle = {International IEEE EMBS Conference on Neural Engineering},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-136}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Machine Learning, Optimization, Signal Processing

Abstract:

Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This ap- proach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore, we develop various graph neural network (GNN) models that projects electrodes onto the nodes of a graph, where the node features are represented as EEG channel samples collected over a trial, and nodes can be connected by weighted/unweighted edges according to a flexible policy formu- lated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework outperforms standard CNN classifiers across ErrP, RSVP, and MI datasets, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Another practical advantage of our GNN-based framework is that it can be used in EEG channel selection, which is critical for reducing computational cost, and designing portable EEG headsets.

 

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  •  Demir, A., Koike-Akino, T., Wang, Y., Haruna, M., Erdogmus, D., "EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals", arXiv, June 2021.
    BibTeX arXiv
    • @article{Demir2021jun,
    • author = {Demir, Andac and Koike-Akino, Toshiaki and Wang, Ye and Haruna, Masaki and Erdogmus, Deniz},
    • title = {EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals},
    • journal = {arXiv},
    • year = 2021,
    • month = jun,
    • url = {https://arxiv.org/abs/2106.09135}
    • }