Datasets:
File size: 2,107 Bytes
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"meta_info": {
"case_id": "BCIC2020-3_05.edf",
"bench_subset": "NeuroBench-Core",
"difficult": 1.5,
"original_dataset": "BCIC2020-3"
},
"agent_input": {
"data_path": "data/core/BCIC2020-3_05.edf",
"instruction": "Please select only the parietal (P*) 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_p_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 parietal 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_p_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_p_mean\": <float|null>, \"sef95_o_mean\": <float|null>}",
"metrics": [
{
"metric_id": "sef95_p_accuracy",
"type": "numeric_check",
"target_key": "sef95_p_mean",
"weight": 50,
"params": {
"gt_value": 37.638889,
"tolerance": 1.881944
}
},
{
"metric_id": "sef95_o_accuracy",
"type": "numeric_check",
"target_key": "sef95_o_mean",
"weight": 50,
"params": {
"gt_value": 37.75,
"tolerance": 1.8875
}
}
]
}
}
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