A Review of Artificial Intelligence-Based Methods for the Detection of Intracranial and Scalp EEG Epileptic Spikes and High-Frequency Oscillations

Background/Objectives: Surgical resection of the epileptogenic zone (EZ) is the most effective treatment for drug-resistant epilepsy, yet its success relies on accurate localization. Interictal spikes and high-frequency oscillations (HFOs) are important electrophysiological biomarkers for the EZ, but their visual identification in long-term recordings is labor-intensive, subjective, and prone to inter-rater variability. Artificial Intelligence (AI) offers a promising solution for automating this process. This review provides a comprehensive synthesis of AI-based methods for detecting both spikes and HFOs across invasive intracranial electroencephalography (iEEG) and non-invasive scalp EEG. Methods: We organized this review by recording modality (iEEG, scalp EEG) and biomarker type (spikes, HFOs), tracing the methodological evolution of biomarker detection, ranging from early rule-based heuristics to feature-engineered machine learning (ML) and modern end-to-end deep learning (DL). We then described the clinical application of these detected events, specifically their role in localizing the epileptogenic zone and defining state-dependent prognostic markers. Next, we provided an overview of validated software tools and annotated datasets essential for benchmarking new algorithms. Finally, we listed critical challenges and offered perspective on the future directions of this field. Results: In iEEG, spike and HFO detection methods have matured from prone-to-error energy thresholds to advanced deep learning and neuromorphic systems that significantly reduce false positives and enable real-time network analysis. In scalp EEG, detection analysis has progressed from manual heuristics to robust deep neural networks that successfully overcome low signal-to-noise ratios and muscle artifacts to achieve expert-level diagnostic performance. The clinical utility of AI-based detection includes precise epileptogenic zone localization, specifically through spike-HFO co-occurrence, and the monitoring of state-dependent fluctuations for objective prognostication. To ensure reliable deployment, researchers need to validate models against standardized datasets and commercial benchmarks while tailoring architectures to specific data scales and real-time processing constraints. Conclusions: AI-driven automation is essential for realizing the clinical potential of EEG biomarkers, yet widespread adoption is currently impeded by several factors, such as the lack of interpretability in “black box” models, the generalization gap across diverse centers, computational constraints preventing real-time implementation, labeling bottlenecks, and complex regulatory and ethical requirements. Future research should prioritize Explainable AI (XAI), multi-center validation, self-supervised learning, and the development of “human-in-the-loop” workflows to robustly integrate automated detection into routine presurgical evaluation.