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Neural networks for epilepsy detection and prediction with EEG signals: a systematic review

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Publicat a:The Artificial Intelligence Review vol. 59, no. 1 (Jan 2026), p. 30
Autor principal: Wu, Youpeng
Altres autors: Lu, Lun, Xu, Ao, Wang, Yinan, Li, Zhiwei, Yang, Zhuanyi, Zeng, Lingli, Li, Qingjiang
Publicat:
Springer Nature B.V.
Matèries:
Epilepsy
Data processing
Accuracy
Neurological diseases
Datasets
Convulsions & seizures
Brain
Neurological disorders
Brain research
Artificial neural networks
Neural networks
Electroencephalography
Data quality
Explainable artificial intelligence
Internet of Things
Popularity
Networks
Artificial intelligence
Systematic review
Graph neural networks
Pipelines
Support vector machines
Classification
Information
Algorithms
Patients
Real time
Biometrics
Comparative analysis
Accés en línia:Citation/Abstract
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Resum:Epilepsy is a neurological disorder characterized by abnormal neuronal discharges in the brain. As a rich source of biometric information, electroencephalography (EEG) provides favorable conditions for automated detection. Traditional algorithms and manual analysis possess solid theoretical foundations and good interpretability, however, these methods predominantly require extensive domain expertise and involve lengthy processing pipelines for complex data. The advent of artificial intelligence (AI) has facilitated the application of neural networks in the detection and prediction of epilepsy. Although such approaches heavily rely on high-quality annotated data, suffer from limited model interpretability, and involve complex training and parameter tuning, these efficient, real-time, end-to-end models still demonstrate significant potential in epilepsy analysis. This review systematically analyzes and summarizes the neural network technologies used in 341 papers published in the past three years, employing the PRISMA standard procedure. To facilitate readers’ related research, the review also summarizes the basic information of 16 publicly available datasets, common features, and metrics. Specifically, this review offers a comprehensive evaluation of diverse neural network architectures, concluding that convolutional neural networks have become a prevalent choice as classic neural networks. Furthermore, graph neural networks and transformers are experiencing a marked surge in popularity. The application of hybrid neural networks to fully extract information from EEG is also a growing trend. The review concludes with a comprehensive discussion and summary of the technical characteristics, research directions, and limitations of current methods, including patient-to-patient identification, explainable AI, dataset bias, and zone location.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-025-11441-1
Font:ABI/INFORM Global