Graph Neural Network-Based EEG Classification: A Survey
Кілт сөздер:
Computer science, Artificial intelligence, Electroencephalography, Graph, Convolutional neural network, Pattern recognition (psychology), Machine learning, Entropy (arrow of time)Аңдатпа
Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.
Source and licence. Open-access article originally published as “Graph Neural Network-Based EEG Classification: A Survey” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024. DOI: 10.1109/tnsre.2024.3355750. © Dominik Klepl, Min Wu, Fei He. Reproduced unchanged under the Creative Commons Attribution 4.0 licence.
Әдебиеттер тізімі
Flach, P., Kull, M. (2015). Advances in Neural Information Processing Systems 28.
Wu, Z., Pan, S., Chen, F. et al. (2020). A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2020.2978386
Lawhern, V., Solon, A., Waytowich, N. et al. (2018). EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/aace8c
Zheng, W., Lu, B. (2015). Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks. IEEE Transactions on Autonomous Mental Development. https://doi.org/10.1109/tamd.2015.2431497
Lotte, F., Bougrain, L., Cichocki, A. et al. (2018). A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/aab2f2
Seo, Y., Defferrard, M., Vandergheynst, P. et al. (2018). Structured Sequence Modeling with Graph Convolutional Recurrent Networks. Lecture notes in computer science. https://doi.org/10.1007/978-3-030-04167-0_33
Smith, S. (2005). EEG in the diagnosis, classification, and management of patients with epilepsy. Journal of Neurology Neurosurgery & Psychiatry. https://doi.org/10.1136/jnnp.2005.069245
Murugappan, M., Ramachandran, N., Sazali, Y. (2010). Classification of human emotion from EEG using discrete wavelet transform. Journal of Biomedical Science and Engineering. https://doi.org/10.4236/jbise.2010.34054
Жүктеулер
Жарияланды
Журналдың саны
Бөлім
Лицензия
Авторлық құқық (c) 2024 Dominik Klepl, Min Wu, Fei He
Бұл жұмыс Creative Commons атрибуты бойынша лицензияланған. 4.0 Халықаралық лицензия.
Мақалалар Creative Commons Attribution 4.0 International (CC BY 4.0) лицензиясымен жарияланады.
