bibtext: @InProceedings{ Fan_ADomain_MICCAI2024,
                   author = { Fan, Xiaoya and Xu, Pengzhi and Zhao, Qi and Hao, Chenru and Zhao, Zheng and Wang, Zhong },
                   title = { { A Domain Adaption Approach for EEG-based Automated Seizure Classification with Temporal-Spatial-Spectral Attention } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: Electroencephalography (EEG) based automated seizure classification can significantly ameliorate seizure diagnosis and treatment. However, the intra- and inter- subject variability in EEG data make it a challenging task. Especially, a model trained on data from multiple subjects typically degenerates when applied to new subjects. In this study, we propose an attention based deep convolutional neural network with domain adaption to tackle these issues. The model is able to learn domain-invariant temporal-spatial-spectral (TSS) features by jointly optimizing a feature extractor, a seizure classifier and a domain discriminator. The feature extractor extracts multi-level TSS features by an attention module. The domain discriminator is designed to determine which domain, i.e., source or target, the features come from. With a gradient reversal layer, it allows extraction of domain-invariant features. Thus, the classifier is able to give accurate prediction for unseen subjects by leveraging knowledge learned from the source domain. We evaluated our approach using the Temple University Hospital EEG Seizure Corpus (TUSZ) v1.5.2. Results demonstrate that the proposed approach achieves the state-of-the-art performance on seizure classification. The code is available at https://github.com/Dondlut/EEG_DOMAIN.
title: A Domain Adaption Approach for EEG-based Automated Seizure Classification with Temporal-Spatial-Spectral Attention
authors: Fan, Xiaoya Xu, Pengzhi Zhao, Qi Hao, Chenru Zhao, Zheng Wang, Zhong