OmniBrain: An Experience-Driven Agent for Evidence-Grounded Clinical EEG Diagnosis

Electroencephalography (EEG) diagnosis requires selecting measurements and interpreting ambiguous findings in clinical context. Foundation models provide diagnostic predictions, but these outputs do not specify which findings warrant further checks or how to assess them in context. Learning reusable diagnostic guidance is challenging because case labels evaluate final diagnoses, leaving intermediate analysis decisions unlabeled. We introduce OmniBrain, an EEG agent that learns interpretation and verification guidance from diagnostic feedback without updating model weights. Within each case, a Clinical Evidence Verification Loop (CEVL) organizes hypothesis-driven reasoning: stating expectations before analysis, interpreting measurements in context, and pursuing follow-up checks. The resulting diagnostic records are represented as Diagnostic Inference Graphs (DIGs) that preserve measurement context and evidence dependencies. Comparing successful and failed DIGs from the same recording holds the input fixed, exposing interpretation differences and unmatched follow-up checks. An LLM converts these contrasts into reusable guidance for subsequent cases. At inference, the learned experience guides signal analysis, after which the agent consults foundation-model predictions to form its final diagnosis. Against five frozen-backbone foundation models and their majority vote, OmniBrain achieves the highest task-level balanced accuracy on five of six EEG tasks. DIG-based extraction outperforms direct extraction on both evaluated tasks, and experience learned from one Parkinson's disease dataset transfers across five held-out datasets.