Datasets:
File size: 2,227 Bytes
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"meta_info": {
"case_id": "Mumtaz2016_04.edf",
"bench_subset": "NeuroBench-Core",
"difficult": 1.5,
"original_dataset": "Mumtaz2016"
},
"agent_input": {
"data_path": "data/core/Mumtaz2016_04.edf",
"instruction": "Please select only the central-region EEG channels (C* and CP*). Apply a 50 Hz FIR notch filter, then a 0.5-45 Hz FIR bandpass filter, resample to 100 Hz, and re-reference using average reference. Use the first 90 seconds of each selected channel. Compute Hjorth Mobility and Hjorth Complexity for each channel, then report the channel-mean values as hjorth_mobility_mean and hjorth_complexity_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 Hjorth Mobility\n2) Mean Hjorth Complexity\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 \"hjorth_mobility_mean\" and \"hjorth_complexity_mean\".\n5. Both values must be FLOAT.\n6. If one value is missing, return it as null.\n\n### OUTPUT TEMPLATE\n{\"hjorth_mobility_mean\": <float|null>, \"hjorth_complexity_mean\": <float|null>}",
"metrics": [
{
"metric_id": "hjorth_mobility_accuracy",
"type": "numeric_check",
"target_key": "hjorth_mobility_mean",
"weight": 50,
"params": {
"gt_value": 0.530136,
"tolerance": 0.026507
}
},
{
"metric_id": "hjorth_complexity_accuracy",
"type": "numeric_check",
"target_key": "hjorth_complexity_mean",
"weight": 50,
"params": {
"gt_value": 2.620176,
"tolerance": 0.131009
}
}
]
}
}
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