# Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

arXiv: https://arxiv.org/abs/2002.01925 · HTML: https://mlsanity.com/paper/2002.01925/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 30 of 33)

- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals](https://mlsanity.com/paper/2306.13102/) (2023)
- [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023)
- [Dynamical Survival Analysis with Controlled Latent States](https://mlsanity.com/paper/2401.17077/) (2024)
- [Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures](https://mlsanity.com/paper/2205.03239/) (2022)
- [A Meta-GNN approach to personalized seizure detection and classification](https://mlsanity.com/paper/2211.02642/) (2022)
- [Towards trustworthy seizure onset detection using workflow notes](https://mlsanity.com/paper/2306.08728/) (2023)
- [The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary study](https://mlsanity.com/paper/2505.13021/) (2025)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [Improving self-supervised pretraining models for epileptic seizure detection from EEG data](https://mlsanity.com/paper/2207.06911/) (2022)
- [ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges](https://mlsanity.com/paper/2304.14919/) (2023)
- [Random Forest classifier for EEG-based seizure prediction](https://mlsanity.com/paper/2106.04510/) (2021)
- [EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers](https://mlsanity.com/paper/2209.11172/) (2022)
- [Importance of methodological choices in data manipulation for validating epileptic seizure detection models](https://mlsanity.com/paper/2302.10672/) (2023)
- [Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals](https://mlsanity.com/paper/2305.04325/) (2023)
- [Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression](https://mlsanity.com/paper/2404.15311/) (2024)
- [A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction](https://mlsanity.com/paper/2105.02823/) (2021)
- [Epileptic Seizure Risk Assessment by Multi-Channel Imaging of the EEG](https://mlsanity.com/paper/2204.07034/) (2022)
- [Review of medical data analysis based on spiking neural networks](https://mlsanity.com/paper/2212.02234/) (2022)
- [Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy](https://mlsanity.com/paper/2302.10901/) (2023)
- [SeizureFormer: A Transformer Model for IEA-Based Seizure Risk Forecasting](https://mlsanity.com/paper/2504.16098/) (2025)
- [Imaging the time series of one single referenced EEG electrode for Epileptic Seizures Risk Analysis](https://mlsanity.com/paper/2206.14520/) (2022)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures](https://mlsanity.com/paper/2408.03449/) (2024)
- [Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study](https://mlsanity.com/paper/2408.03472/) (2024)
- [From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals](https://mlsanity.com/paper/2410.03385/) (2024)
- [On the challenges of detecting MCI using EEG in the wild](https://mlsanity.com/paper/2501.17871/) (2025)
- [Affordable EEG, Actionable Insights: An Open Dataset and Evaluation Framework for Epilepsy Patient Stratification](https://mlsanity.com/paper/2511.01879/) (2025)
- [NeuroCanvas: VLLM-Powered Robust Seizure Detection by Reformulating Multichannel EEG as Image](https://mlsanity.com/paper/2602.04769/) (2026)
- [CG-MambaNet: A spatiotemporal framework for cross-patient epileptic seizure prediction using CNN-GCN-Mamba-BiLSTM with event-level clinical evaluation](https://mlsanity.com/paper/2606.08226/) (2026)

## Abstract (arXiv)

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.