B2-ViT net: broad vision transformer network with broad attention for seizure prediction

Shi, Shuiling; Liu, Wenqi

doi:10.1109/tnsre.2023.3346955

Cited by 124EEG and Brain-Computer InterfacesEpilepsy research and treatmentMachine Learning in Healthcare

B2-ViT net: broad vision transformer network with broad attention for seizure prediction

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Shi, Shuiling, Liu, Wenqi·2024·IEEE Transactions on Neural Systems and Rehabilitation Engineering

DOI 10.1109/tnsre.2023.3346955

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Abstract

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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Multimodal systems have shown promise in delivering fairer and more equitable care by incorporating broader patient contexts, reducing disparities.
“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]
The paper’s Limitations and Future Directions section discusses a patient-dependent EEG seizure-prediction model that cannot yet be applied across patients. Searches for multimodal systems, equitable care, bias, and disparities found no passage addressing whether broader patient contexts reduce disparities. The indexed passages provide no page numbers. (LLM, conf=0.76)
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Authors: Shi, S., & Liu, W.
Affiliations: Kunming University of Science and Technology (CN)
ORCID: Shi, Shuiling
Liu, Wenqi
Venue: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2024
Volume: 32
Pages: 178-188
ISSN: 1534-4320 (ISSN-L)
1558-0210
DOI: 10.1109/tnsre.2023.3346955
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
Licence: cc-by
creativecommons.org/licenses/by/4.0/legalcode (vor)
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 › Psychiatry and Mental health › 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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