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B2-ViT Net: Broad Vision Transformer Network With Broad Attention for Seizure Prediction
Open accessAbstract
Seizure prediction are necessary for epileptic patients. The global spatial interactions among channels, and long-range temporal dependencies play a crucial role in seizure onset prediction. In addition, it is necessary to search for seizure prediction features in a vast space to learn new generalized feature representations. Many previous deep learning algorithms have achieved some results in automatic seizure prediction. However, most of them do not consider global spatial interactions among channels and long-range temporal dependencies together, and only learn the feature representation in the deep space. To tackle these issues, in this study, an novel bi-level programming seizure prediction model, B2-ViT Net, is proposed for learning the new generalized spatio-temporal long-range correlation features, which can characterize the global interactions among channels in spatial, and long-range dependencies in temporal required for seizure prediction. In addition, the proposed model can comprehensively learn generalized seizure prediction features in a vast space due to its strong deep and broad feature search capabilities. Sufficient experiments are conducted on two public datasets, CHB-MIT and Kaggle datasets. Compared with other existing methods, our proposed model has shown promising results in automatic seizure prediction tasks, and provides a certain degree of interpretability.
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“Furthermore, multimodal systems have shown promise in delivering fairer and more equitable care, as they can incorporate broader patient contexts, reducing disparities that often arise when models are trained on limited or homogeneous data (10–12).”
From The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities (2026) · cites this paper as [12]
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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,313
Deep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751
WAVELET TRANSFORMS AND THEIR APPLICATIONS TO TURBULENCE
Marie Farge, A. (1992) · Annual Review of Fluid Mechanics · doi:10.1146/annurev.fl.24.010192.002143 · Cited by 3,069
Chen, C. L. P., & Liu, Z. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2716952 · Cited by 1,957
Kwan, P., Schachter, S. C., & Brodie, M. J. (2011) · New England Journal of Medicine · doi:10.1056/nejmra1004418 · Cited by 1,113
Moshé, S. L., Perucca, E., Ryvlin, P., & Tomson, T. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60456-6 · Cited by 937
Application of machine learning to epileptic seizure onset detection and treatment
Shoeb, A. H. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 855
Tsiouris, Κ. Μ., Pezoulas, V. C., Zervakis, M. E., Konitsiotis, S., Koutsouris, D., & Fotiadis, D. I. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.019 · Cited by 665
Truong, N. D., Nguyen, A. D., Kuhlmann, L., Bonyadi, M. R., Yang, J., Ippolito, S. J., & Kavehei, O. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.04.018 · Cited by 640
Efficient Epileptic Seizure Prediction Based on Deep Learning
Daoud, H., & Bayoumi, M. (2019) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2019.2929053 · Cited by 503
Seizure prediction – ready for a new era
Kuhlmann, L., Lehnertz, K., Richardson, M. P., Schelter, B., & Zaveri, H. P. (2018) · Nature Reviews Neurology · doi:10.1038/s41582-018-0055-2 · Cited by 450
Samiee, K., Kovács, P., & Gabbouj, M. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2360101 · Cited by 417
GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition
Zhang, T., Wang, X., Xu, X., & Chen, C. L. P. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2937768 · Cited by 397
EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations
Li, P., Liu, H., Si, Y., Li, C., Li, F., Zhu, X., Huang, X., Zeng, Y., Yao, D., Zhang, Y., et al. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2897651 · Cited by 395
Focal Onset Seizure Prediction Using Convolutional Networks
Khan, H., Marcuse, L. V., Fields, M. C., Swann, K., & Yener, B. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2785401 · Cited by 367
Short-time fourier transform: two fundamental properties and an optimal implementation
Durak, L., & Arikan, O. (2003) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2003.810293 · Cited by 345 · cited 1×
“In this paper, STFT [31] is used to preprocess the raw EEG data to extract time-frequency features, which converts the original EEG signal into a time-frequency matrix.”
MethodEpilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network
Zhang, Y., Guo, Y., Yang, P., Chen, W., & Lo, B. P. L. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2933046 · Cited by 322
Maiwald, T., Matthias Winterhalder, A., Aschenbrenner-Scheibe, R., Voss, H. U., Schulze‐Bonhage, A., & Timmer, J. (2004) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2004.02.013 · Cited by 299
Crowdsourcing reproducible seizure forecasting in human and canine epilepsy
Brinkmann, B. H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S. C., Chen, M., Tieng, Q. M., He, J., Muñoz–Almaraz, F. J., Botella-Rocamora, P., et al. (2016) · Brain · doi:10.1093/brain/aww045 · Cited by 275
Global and regional functional connectivity maps of neural oscillations in focal epilepsy
Englot, D. J., Hinkley, L. B., Kort, N. S., Imber, B. S., Mizuiri, D., Honma, S. M., Findlay, A. M., Garrett, C., Cheung, P. L., Mantle, M., et al. (2015) · Brain · doi:10.1093/brain/awv130 · Cited by 236
Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach
Özcan, A. R., & Ertürk, S. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2943707 · Cited by 203
Zandi, A. S., Tafreshi, R., Javidan, M., & Dumont, G. A. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2237399 · Cited by 174
van Dellen, E., Douw, L., Baayen, J. C., Heimans, J. J., Ponten, S. C., Vandertop, W. P., Velis, D. N., Stam, C. J., & Reijneveld, J. C. (2009) · PLoS ONE · doi:10.1371/journal.pone.0008081 · Cited by 151
An Effective Dual Self-Attention Residual Network for Seizure Prediction
Yang, X., Zhao, J., Sun, Q. L., Lu, J., & Ma, X. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3103210 · Cited by 147
Cho, D., Min, B., Kim, J., & Lee, B. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2618937 · Cited by 144
Seizure detection using scalp‐ EEG
Baumgartner, C., & Koren, J. P. (2018) · Epilepsia · doi:10.1111/epi.14052 · Cited by 136
EEG-based mild depression recognition using convolutional neural network
Li, X., La, R., Wang, Y., Niu, J., Zeng, S., Sun, S., & Zhu, J. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-01959-2 · Cited by 136
BMT-Net: Broad Multitask Transformer Network for Sentiment Analysis
Zhang, T., Gong, X., & Chen, C. L. P. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3050508 · Cited by 135
EEG-based seizure prediction via Transformer guided CNN
Li, C., Huang, X., Song, R., Qian, R., Liu, X., & Chen, X. (2022) · Measurement · doi:10.1016/j.measurement.2022.111948 · Cited by 115
Chen, Y., Yan, J., Jiang, M., Zhang, T., Zhao, Z., Zhao, W., Zheng, J., Yao, D., Zhang, R., Kendrick, K. M., et al. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3154755 · Cited by 113
Epilepsy mortality and risk factors for death in epilepsy: a population-based study
Ridsdale, L., Charlton, J., Ashworth, M. J., Richardson, M. P., & Gulliford, M. C. (2011) · British Journal of General Practice · doi:10.3399/bjgp11x572463 · Cited by 110
Patient-Specific Seizure Prediction via Adder Network and Supervised Contrastive Learning
Zhao, Y., Li, C., Liu, X., Qian, R., Song, R., & Chen, X. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3180155 · Cited by 99
Hierarchical Lifelong Learning by Sharing Representations and Integrating Hypothesis
Zhang, T., Su, G., Qing, C., Xu, X., Cai, B., & Xing, X. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2884996 · Cited by 97
Continuous Seizure Detection Based on Transformer and Long-Term iEEG
Sun, Y., Jin, W., Si, X., Zhang, X., Cao, J., Wang, L., Yin, S., & Ming, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3199206 · Cited by 84
Seizure detection algorithm based on improved functional brain network structure feature extraction
Jiang, L., He, J., Pan, H., Wu, D., Jiang, T., & Liu, J. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104053 · Cited by 72
Ngamga, E. J., Bialonski, S., Marwan, N., Kurths, J., Geier, C., & Lehnertz, K. (2016) · Physics Letters A · doi:10.1016/j.physleta.2016.02.024 · Cited by 67
The Dynamics of the Epileptic Brain Reveal Long-Memory Processes
Cook, M. J., Varsavsky, A., Himes, D. M., Leyde, K. W., Berkovic, S. F., Oâ€TMBrien, T., & Mareels, I. (2014) · Frontiers in Neurology · doi:10.3389/fneur.2014.00217 · Cited by 66
Gao, Y., Liu, A., Wang, L., Qian, R., & Chen, X. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3260845 · Cited by 61
BViT: Broad Attention-Based Vision Transformer
Li, N., Chen, Y., Li, W., Ding, Z., Zhao, D., & Nie, S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3264730 · Cited by 40
Source-Free Domain Adaptation for Privacy-Preserving Seizure Prediction
Zhao, Y., Feng, S., Li, C., Song, R., Deng, L., & Chen, X. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3297323 · Cited by 39
Seizure Prediction using Convolutional Neural Networks and Sequence Transformer Networks
Chen, R., & Parhi, K. K. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629732 · Cited by 34
Research works printed but not linked: 1
- Dosovitskiy, An image is worth 16 × 16 words: Transformers for image recognition at scale, 1
Cited by
Sorted by citations
Zhang, X., Zhang, X., Huang, Q. N., & Chen, F. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1468967 · Cited by 88
Chen, J., Jin, S., Cui, Y., Wei, C., Polat, K., & Alenezi, F. S. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115532 · Cited by 31
Elumalai, S., Rajendran, S., & Khalid, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-34377-6 · Cited by 21
Targeted Protein Degradation in Cancer: PROTACs, New Targets, and Clinical Mechanisms
Faryal, B., Abideen, Z. U., Irfan, M., Ahmed, H., Jalilov, F. S., Abduraximova, L., & Ashraf, G. A. (2026) · Biomolecules · doi:10.3390/biom16020325 · Cited by 24
Interactive multi-agent convolutional broad learning system for EEG emotion recognition
Shi, S., Liu, W., & Chen, C. L. P. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125420 · Cited by 18
Metadata
- Authors
- Shi, S., & Liu, W.
- Affiliations
- Kunming University of Science and Technology (CN)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2024
- Volume
- 32
- Pages
- 178-188
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W4390204163
- Cited by
- 124
- References
- 43
- Citations by year
- 2026: 77 · 2025: 39 · 2024: 8
- FWCI
- 18.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (12371460)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Clinical Neurology › Epilepsy research and treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- seizure prediction, epilepsy, vision transformer, long-range temporal dependencies, spatiotemporal feature learning, bi-level optimization, EEG, CHB-MIT, interpretability
- MeSH terms
- Algorithms, Electroencephalography, Humans, Electric Power Supplies, Seizures
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