From brain signals to smart modulation: A scoping review of electroencephalography signal processing, network analysis, and neuromodulation in epilepsy

Abstract

Despite substantial recent progress in epilepsy related technologies and algorithms, strong retrospective performance often fails to translate into reliable long term clinical use because of drift, class imbalance, stimulation artifacts, and limited or absent ground-truth feedback. This review provides a deployment-oriented synthesis of epilepsy related methods across the sensing-to-control pipeline, spanning data acquisition, preprocessing, spontaneous event and evoked-response analysis, connectivity analysis, brain modeling, and closed-loop control. It is based on a structured scoping search, with studies selected and synthesized according to translational relevance, validation setting, and role within the sensing-to-control pipeline. The methods are organized using a shared four axis framework: temporal scale and latency constraints, observability and representation, vulnerability to non-stationarity and drift, and deployment role. This framework highlights how upstream constraints shape the reliability of downstream inference and intervention. We argue that algorithm-guided neuromodulation in epilepsy is best understood as a constrained control problem under partial observability and non-stationarity. Within this framing, we revisit three recurring questions central to system design: whether seizure prediction is achievable, the validation status of candidate biomarkers, and the balance between focal and network models of epileptogenicity. These are distinct from the broader research agenda summarized by the four open questions below. For each pipeline stage, we identify reporting and evaluation priorities aligned with clinically relevant endpoints rather than offline accuracy alone. Together, this review highlights engineering priorities for clinically durable systems and generates testable hypotheses about seizure dynamics and network controllability.