Datasets:
File size: 1,704 Bytes
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"meta_info": {
"case_id": "Mumtaz2016_04.edf",
"bench_subset": "NeuroBench-Core",
"difficult": 1.0,
"original_dataset": "Mumtaz2016"
},
"agent_input": {
"data_path": "data/core/Mumtaz2016_04.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": 0.949021,
"tolerance": 0.047451
}
}
]
}
} |