WaveAnchor-Agent: From Waveform Detection to Interactive EEG Analysis

Fine-grained electroencephalography (EEG) interpretation requires localizing waveform events and explaining their spatial and temporal relationships. We introduce WaveAnchor-Agent (WA-Agent), an evidence-grounded workflow for fine-grained, generative EEG interpretation, together with a rubric-based benchmark for evaluating generated descriptions and dialogue. We build a physician-annotated dataset covering nine waveform classes and train a multi-scale temporal detector to localize events by channel and time. A planner-responder architecture connects waveform detection, deterministic measurement, and visualization tools to natural-language responses, supporting interactive analysis grounded in explicit signal-level evidence. Our benchmark covers waveform detection, selection, description, multi-turn conversation, and visualization. Its description and conversation tasks use 1,550 manually reviewed atomic rubric criteria to assess factual content beyond lexical similarity. On our benchmark, the detector achieves 45.22% test mAP at temporal IoU 0.5. WA-Agent achieves 93.71% selection accuracy and LLM-judged partial-credit rubric scores of 68.73% and 55.52% for description and conversation, respectively. Together, the workflow and benchmark provide a foundation for generating and evaluating fine-grained EEG explanations grounded in localized waveform evidence.