Intelligent Learning Techniques for Epileptic Seizure Prediction Using EEG Signals: A Comprehensive Review - Zahraa Hadi Kazem Al-janabi; Mohammad Khalaf Rahim Al-juaifari (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2026)
Epileptic seizure prediction facilitates proactive rather than reactive medical treatment, thereby promoting patient safety and improved living conditions. One of the most prominent tools employed for epileptic seizure prediction purposes is electroencephalography, considering its temporal resolution and ability to detect brain activity. This paper offers an organized and tightly focused review on new intelligent learning methods applied for epileptic seizure prediction via EEG. This review discusses publicly and privately accessible datasets, preprocessing and segmentation, feature spaces, machine learning and deep neural networks, evaluation schemes, and postprocessing. It points out major bottlenecks preventing more effective implementation, including an overdependence on a few common datasets, preprocessing conventions, cross-validation on different patients, and poor interpretation and implementation strategies. This paper proposes future developments on more reliable seizure predictors.