Datasets:
File size: 2,631 Bytes
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"meta_info": {
"case_id": "MentalArithmetic_02.edf",
"bench_subset": "NeuroBench-Core",
"difficult": 1.5,
"original_dataset": "MentalArithmetic"
},
"agent_input": {
"data_path": "data/core/MentalArithmetic_02.edf",
"instruction": "Please select standard EEG channels, apply common average reference, resample to 100Hz, apply a 0.5-40Hz FIR bandpass filter, and split the 30-60s segment into three 10-second windows. Compute Global Field Power (GFP) in each window and report the time point of the maximum GFP peak in each window."
},
"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 three GFP peak time points from the agent report:\n1) peak time in window 1\n2) peak time in window 2\n3) peak time in window 3\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, explanations, or units.\n4. Keys must be EXACTLY \"gfp_peak_time_window_1\", \"gfp_peak_time_window_2\", and \"gfp_peak_time_window_3\".\n5. Values must be float seconds or null.\n6. If a value is missing or cannot be found, use null for that key.\n\n### OUTPUT TEMPLATE\n{\"gfp_peak_time_window_1\": <float|null>, \"gfp_peak_time_window_2\": <float|null>, \"gfp_peak_time_window_3\": <float|null>}",
"metrics": [
{
"metric_id": "gfp_peak_time_window_1_accuracy",
"type": "numeric_check",
"target_key": "gfp_peak_time_window_1",
"weight": 34,
"params": {
"gt_value": 39.2,
"tolerance": 0.1
}
},
{
"metric_id": "gfp_peak_time_window_2_accuracy",
"type": "numeric_check",
"target_key": "gfp_peak_time_window_2",
"weight": 33,
"params": {
"gt_value": 40.02,
"tolerance": 0.1
}
},
{
"metric_id": "gfp_peak_time_window_3_accuracy",
"type": "numeric_check",
"target_key": "gfp_peak_time_window_3",
"weight": 33,
"params": {
"gt_value": 59.1,
"tolerance": 0.1
}
}
]
}
}
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