Epilepsy is a chronic neurological disorder characterized by recurrent unprovoked seizures, affects over 50 million people worldwide, and poses significant risks, including sudden unexpected death in epilepsy (SUDEP). Conventional unimodal approaches, primarily reliant on electroencephalography (EEG), face several key challenges, including low SNR, nonstationarity, inter- and intrapatient heterogeneity, portability, and real-time applicability in clinical settings. To address these issues, a comprehensive survey highlights the concept of advanced multimodal learning for epileptic seizure detection and prediction (AMLSDP). The survey presents the evolution of epileptic seizure detection (ESD) and prediction (ESP) technologies across different eras. The survey also explores the core challenges of multimodal and non-EEG-based ESD and ESP. To overcome the key challenges of the multimodal system, the survey introduces the advanced processing strategies for efficient AMLSDP. Furthermore, this survey highlights future directions for researchers and practitioners. We believe this work will advance neurotechnology toward wearable and imaging-based solutions for epilepsy monitoring, serving as a valuable resource for future innovations in this domain.

翻译：癫痫是一种以反复无端发作为特征的慢性神经系统疾病，全球影响超过5000万人，并带来包括癫痫猝死（SUDEP）在内的重大风险。传统单模态方法主要依赖脑电图（EEG），面临若干关键挑战，包括低信噪比、非平稳性、患者间与患者内异质性、便携性及临床环境中的实时适用性。为解决这些问题，本综述系统阐述了用于癫痫发作检测与预测的先进多模态学习（AMLSDP）概念。文章梳理了癫痫发作检测（ESD）与预测（ESP）技术在不同时期的发展脉络，深入探讨了多模态与非EEG基础的ESD与ESP的核心挑战。为克服多模态系统的关键难题，本文介绍了实现高效AMLSDP的先进处理策略。此外，本综述还指明了未来研究者与实践者可能探索的方向。我们相信这项工作将推动神经技术向可穿戴与基于成像的癫痫监测解决方案发展，为该领域的未来创新提供重要参考。