foundational_analysis/cases/case03/case03_06.json

{
    "meta_info": {
        "case_id": "BCIC2020-3_01.edf",
        "bench_subset": "NeuroBench-Core",
        "difficult": 1.0,
        "original_dataset": "BCIC2020-3"
    },
    "agent_input": {
        "data_path": "data/core/BCIC2020-3_01.edf",
        "instruction": "Please select only the occipital EEG channels from the raw signal first. For each selected channel, use only the first 30 seconds of signal and compute Sample Entropy (SampEn) with m=2 and r=0.2 times the standard deviation of that channel segment. Then calculate the average SampEn across all selected channels and report the final mean value clearly."
    },
    "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 the final 'Mean Sample Entropy' value from the agent report.\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 key in JSON must be EXACTLY \"sampen_mean\".\n5. The value must be a FLOAT.\n6. If missing or unrecognized, return {\"sampen_mean\": null}.\n\n### OUTPUT TEMPLATE\n{\"sampen_mean\": <float|null>}",
        "metrics": [
            {
                "metric_id": "sampen_mean_accuracy",
                "type": "numeric_check",
                "target_key": "sampen_mean",
                "weight": 100,
                "params": {
                    "gt_value": 1.923324,
                    "tolerance": 0.096168
                }
            }
        ]
    }
}