ERIA: An Instruction-Optimized Agent for Clinical EEG Reporting

Clinical EEG interpretation is substantially more complex than classifying short signal segments; it must assess technical quality, characterize background and behavioral states across long recordings, identify interictal and ictal abnormalities, reconcile uncertain evidence, and synthesize these findings into a clinically meaningful report. We introduce ERIA (EEG Reporting and Instruction-Optimized Agent), a locally deployable EEG interpretation framework that enables a single 27B language-model controller to analyze whole-recording EEGs by orchestrating specialized parametric and signal-processing tools. ERIA combines specialized tools for detecting background abnormality, sleep–wake states, seizure-like activity, and interictal epileptiform discharges with deterministic spectral, amplitude, symmetry, montage, and signal-quality analyses, covering multiple clinically relevant domains reflected in EEG reporting guidelines. Rather than relying on EEG-specific controller fine-tuning, a proprietary frontier model, or multi-agent orchestration, ERIA keeps the controller weights, specialist tools, and safety constraints fixed and optimizes only its natural-language analysis policy with GEPA (Genetic-Pareto), a reflective prompt optimizer, using feedback from expert-written EEG reports. Compared with the raw policy, GEPA substantially improves physician-aligned whole-recording report quality, while the resulting policy transfers to external EEG benchmarks. We evaluate performance on CerebraGloss-Bench short-segment clinical interpretation and BrainBench Foundational Analysis, where ERIA performs competitively with the models evaluated in the original benchmarks. These results show that carefully selected specialist tools and language-level policy optimization can extend the clinical and general EEG capabilities of a single locally executable model while preserving local inference, traceable tool use, and a comparatively simple deployment architecture.