ArticleBiomedicines2022
Multi-Channel Vision Transformer for Epileptic Seizure Prediction.
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 64 citations in OpenAlex.
- Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.Brain informatics · 2026Article
- Single-channel EEG-based seizure prediction using deep learning.Scientific reports · 2026Article
- Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning.Parasites & vectors · 2026Article
- AI-Driven Electrographic Seizure Classification and Seizure Onset Detection Using Image- and Time-Series-Based Approaches.IEEE transactions on bio-medical engineering · 2026Article
- SBTM: epileptic seizure prediction from EEG signal using deep learning in blockchain-enabled smart healthcare monitoring with IoT networking.Scientific reports · 2026Article
- Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism.Frontiers in neuroscience · 2026Article
- A predictive approach to enhance time-series forecasting.Nature communications · 2025Article
- Dissociating physiological ripples and epileptiform discharges with vision transformers.bioRxiv : the preprint server for biology · 2025Article
- Self-Supervised Learning with Adaptive Frequency-Time Attention Transformer for Seizure Prediction and Classification.Brain sciences · 2025Article
- Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.Sensors (Basel, Switzerland) · 2025Review
- Using vision transformers for electrographic seizure classification to aid physician review of intracranial electroencephalography recordings.Frontiers in human neuroscience · 2025Article
- Improving diagnostic accuracy of routine EEG for epilepsy using deep learning.Brain communications · 2025Article
- EEG-Based Seizure Prediction Using Hybrid DenseNet-ViT Network with Attention Fusion.Brain sciences · 2024Article
- A ResNet-LSTM hybrid model for predicting epileptic seizures using a pretrained model with supervised contrastive learning.Scientific reports · 2024Article
- Utilizing portable electroencephalography to screen for pathology of Alzheimer's disease: a methodological advancement in diagnosis of neurodegenerative diseases.Frontiers in psychiatry · 2024Article
- CIT-EmotionNet: convolution interactive transformer network for EEG emotion recognition.PeerJ. Computer science · 2024Article
- DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Efficient graph convolutional networks for seizure prediction using scalp EEG.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication. The ability to predict the future occurrence of seizures would enable the patients to take precautions against probable injuries and administer timely treatment to abort or control impending seizures. In this study, we introduce a Transformer-based approach called Multi-channel Vision Transformer (MViT) for automated and simultaneous learning of the spatio-temporal-spectral features in multi-channel EEG data. Continuous wavelet transform, a simple yet efficient pre-processing approach, is first used for turning the time-series EEG signals into image-like time-frequency representations named Scalograms. Each scalogram is split into a sequence of fixed-size non-overlapping patches, which are then fed as inputs to the MViT for EEG classification. Extensive experiments on three benchmark EEG datasets demonstrate the superiority of the proposed MViT algorithm over the state-of-the-art seizure prediction methods, achieving an average prediction sensitivity of 99.80% for surface EEG and 90.28-91.15% for invasive EEG data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.