AI-based localization of the epileptogenic zone using intracranial EEG

Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end-to-end learning. DL has achieved remarkable success in challenging tasks such as facial recognition, self-driving cars, and chatbots utilizing natural language processing. This progress extends to clinical practice and research in epilepsy. In this review, we provide an overview of AI applications in one of the most challenging yet crucial tasks in epilepsy: delineating the epileptogenic zone (EZ) using intracranial EEG. We discuss recent ML and DL real-world workflows in EEG analysis, including preprocessing, training, evaluation, and interpretation. Then, we provide an overview of AI-based approaches for EZ identification using interictal EEG, with a particular focus on high-frequency oscillations. Lastly, we address the considerations and challenges that must be overcome to maximize the potential of AI-based algorithms in EZ identification and biomarker discovery. We hope this review will foster discussions within the community on how to leverage AI to advance research and improve clinical practices in epilepsy. PLAIN LANGUAGE SUMMARY: AI has the potential to revolutionize epilepsy care by enhancing how clinicians pinpoint the brain regions responsible for seizures. This article highlights how AI can analyze interictal EEG (recorded between seizures) to detect subtle brain signals linked to epilepsy and to accurately define the epileptogenic zone. The AI-based approach may lead to quicker diagnoses, more personalized treatments, and better surgical outcomes for patients with epilepsy.