WAVELET2VEC: A Filter Bank Masked Autoencoder for EEG-Based Seizure Subtype Classification

Electroencephalogram (EEG) based seizure subtype classification plays an important role in clinical diagnostics. However, existing deep learning approaches face two challenges in such applications: 1) convolutional or recurrent neural network based models have difficulty learning long-term dependenc...

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Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 1 - 5
Hauptverfasser: Peng, Ruimin, Zhao, Changming, Xu, Yifan, Jiang, Jun, Kuang, Guangtao, Shao, Jianbo, Wu, Dongrui
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 04.06.2023
Schlagworte:
Brain modeling
Deep learning
EEG
filter bank analysis
Filter banks
masked autoencoder
seizure subtype classification
Self-supervised learning
Training data
Transformers
Wavelet transforms
ISSN:2379-190X
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Zusammenfassung:Electroencephalogram (EEG) based seizure subtype classification plays an important role in clinical diagnostics. However, existing deep learning approaches face two challenges in such applications: 1) convolutional or recurrent neural network based models have difficulty learning long-term dependencies; and, 2) there are not enough labeled seizure sub-type data for training such models. This paper proposes a Transformer-based self-supervised learning model for EEG-based seizure subtype classification, which copes well with these two challenges. Filter bank analysis is first employed to improve Vision Transformer as a Wavelet Transformer (WaT) encoder, which generates multi-grained feature representations of EEG signals. Then, self-supervised learning is used to pre-train WaT from unlabeled EEG data. Experiments on two public datasets demonstrated that Wavelet2Vec outperformed several other supervised and self-supervised models in cross-subject seizure subtype classification.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10097183