{
    "meta_info": {
        "case_id": "ISRUC_01.edf",
        "bench_subset": "NeuroBench-Core",
        "difficult": 1.5,
        "original_dataset": "ISRUC"
    },
    "agent_input": {
        "data_path": "data/core/ISRUC_01.edf",
        "instruction": "Please select only the central (C*) 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_c_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 central 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_c_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_c_mean\": <float|null>, \"sef95_o_mean\": <float|null>}",
        "metrics": [
            {
                "metric_id": "sef95_c_accuracy",
                "type": "numeric_check",
                "target_key": "sef95_c_mean",
                "weight": 50,
                "params": {
                    "gt_value": 20.875,
                    "tolerance": 1.04375
                }
            },
            {
                "metric_id": "sef95_o_accuracy",
                "type": "numeric_check",
                "target_key": "sef95_o_mean",
                "weight": 50,
                "params": {
                    "gt_value": 14.0,
                    "tolerance": 0.7
                }
            }
        ]
    }
}