ArticleFrontiers in neuroscience2021
Seizure Prediction in EEG Signals Using STFT and Domain Adaptation.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 83 citations in OpenAlex.
- Frequency-aware transformer networks for robust and generalizable EEG-based seizure detection.Scientific reports · 2026Article
- Advanced methodology for damping characterization of the human tympanic membrane using high-speed digital holography and STFT analysis.Scientific reports · 2026Article
- Single-channel EEG-based seizure prediction using deep learning.Scientific reports · 2026Article
- Advanced Methodology for Damping Characterization of the Human Tympanic Membrane Using High-Speed Digital Holographic and STFT Analysis.Research square · 2026Article
- Discrete wavelet transform-driven optimized deep learning-based framework for dyslexia detection using EEG signals.Frontiers in neuroinformatics · 2026Article
- Robust epileptic seizure prediction: A 3D-SERESNet framework for patient-specific and multi-patient generalization.iScience · 2025Article
- Research on fault diagnosis method of fiber optic sensing roller driven by dynamic model.Scientific reports · 2025Article
- EEG-Based Personal Identification by Special Design Domain-Adaptive Autoencoder.Sensors (Basel, Switzerland) · 2025Article
- Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- Privacy-preserving multi-source semi-supervised domain adaptation for seizure prediction.Cognitive neurodynamics · 2024Article
- Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.Sensors (Basel, Switzerland) · 2024Article
- A ResNet-LSTM hybrid model for predicting epileptic seizures using a pretrained model with supervised contrastive learning.Scientific reports · 2024Article
- An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding.NeuroImage. Clinical · 2023Article
- Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG.Frontiers in neuroscience · 2023Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional approaches commonly collect training and testing samples from the same patient due to inter-individual variability. However, the challenging problem of domain shift between various subjects remains unsolved, resulting in a low conversion rate to the clinic. In this work, a domain adaptation (DA)-based model is proposed to circumvent this issue. The short-time Fourier transform (STFT) is employed to extract the time-frequency features from raw EEG data, and an autoencoder is developed to map these features into high-dimensional space. By minimizing the inter-domain distance in the embedding space, this model learns the domain-invariant information, such that the generalization ability is improved by distribution alignment. Besides, to increase the feasibility of its application, this work mimics the data distribution under the clinical sampling situation and tests the model under this condition, which is the first study that adopts the assessment strategy. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can minimize the domain gap effectively compared with previous approaches.
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