Datasets:
File size: 2,273 Bytes
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"meta_info": {
"case_id": "Mumtaz2016_03.edf",
"bench_subset": "NeuroBench-Core",
"difficult": 1.5,
"original_dataset": "Mumtaz2016"
},
"agent_input": {
"data_path": "data/core/Mumtaz2016_03.edf",
"instruction": "Please select the specified central EEG channel C3-LE, compute the 49-51Hz narrowband power before notch filtering, apply a 50Hz FIR notch filter, compute the 49-51Hz narrowband power after notch filtering, calculate the ratio post_notch_power / pre_notch_power, and report both the ratio and whether line-noise interference was successfully suppressed. Define successful suppression as a ratio strictly below 0.5."
},
"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 outputs from the agent report:\n1) suppression ratio defined as post_notch_power / pre_notch_power\n2) whether line-noise interference was successfully suppressed\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 \"suppression_ratio\" and \"suppressed\".\n5. \"suppression_ratio\" must be a FLOAT or null.\n6. \"suppressed\" must be either true, false, or null.\n\n### OUTPUT TEMPLATE\n{\"suppression_ratio\": <float|null>, \"suppressed\": <bool|null>}",
"metrics": [
{
"metric_id": "suppression_ratio_accuracy",
"type": "numeric_check",
"target_key": "suppression_ratio",
"weight": 70,
"params": {
"gt_value": 0.5023783192085678,
"tolerance": 0.05023783192085678
}
},
{
"metric_id": "suppression_success_accuracy",
"type": "categorical_check",
"target_key": "suppressed",
"weight": 30,
"params": {
"gt_value": false
}
}
]
}
}
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