{ "meta_info": { "case_id": "MentalArithmetic_01.edf", "bench_subset": "NeuroBench-Core", "difficult": 1.5, "original_dataset": "MentalArithmetic" }, "agent_input": { "data_path": "data/core/MentalArithmetic_01.edf", "instruction": "Please select only the parietal (P*) and occipital (O*) EEG channels from the raw signal. Apply a 0.5-40 Hz FIR bandpass filter, then a 60 Hz FIR notch filter, and resample to 200 Hz. Compute the SEF95 value for each selected channel over the full available signal, then report the region-mean values as sef95_p_mean and sef95_o_mean." }, "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 values from the agent report:\n1) Mean SEF95 of parietal channels\n2) Mean SEF95 of occipital channels\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in JSON must be EXACTLY \"sef95_p_mean\" and \"sef95_o_mean\".\n5. Both values must be FLOAT.\n6. If one value is missing, return it as null.\n\n### OUTPUT TEMPLATE\n{\"sef95_p_mean\": , \"sef95_o_mean\": }", "metrics": [ { "metric_id": "sef95_p_accuracy", "type": "numeric_check", "target_key": "sef95_p_mean", "weight": 50, "params": { "gt_value": 21.916667, "tolerance": 1.095833 } }, { "metric_id": "sef95_o_accuracy", "type": "numeric_check", "target_key": "sef95_o_mean", "weight": 50, "params": { "gt_value": 21.125, "tolerance": 1.05625 } } ] } }