Datasets:
| { | |
| "meta_info": { | |
| "case_id": "SEED-V-01.cnt", | |
| "bench_subset": "NeuroBench-Core", | |
| "difficult": 1.0, | |
| "original_dataset": "SEED-V" | |
| }, | |
| "agent_input": { | |
| "data_path": "data/core/SEED-V-01.cnt", | |
| "instruction": "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta." | |
| }, | |
| "eval_config": { | |
| "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}", | |
| "metrics": [ | |
| { | |
| "metric_id": "dominant_band_accuracy", | |
| "type": "categorical_check", | |
| "target_key": "dominant_band", | |
| "weight": 70, | |
| "params": { | |
| "gt_value": "delta" | |
| } | |
| }, | |
| { | |
| "metric_id": "secondary_band_accuracy", | |
| "type": "categorical_check", | |
| "target_key": "secondary_band", | |
| "weight": 30, | |
| "params": { | |
| "gt_value": "beta" | |
| } | |
| } | |
| ] | |
| } | |
| } | |