Datasets:
File size: 2,142 Bytes
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"meta_info": {
"case_id": "case08_07",
"bench_subset": "NeuroBench-Multi",
"difficult": 1,
"original_dataset": "DEAP"
},
"agent_input": {
"data_path": "data/multi/DEAP_02.bdf",
"instruction": "From PPG/BVP channel `Plet`, analyze the half-open interval `[0, 60)` seconds at its native sampling rate.\n\nApply the declared fourth-order 0.5–8 Hz band-pass filter.\nRobust-z-score the filtered signal to obtain the normalized signal.\nDetect peaks using a minimum distance of 205 samples and prominence 0.5.\nSet `feature_value` to the mean peak-interval rate per minute of the normalized signal."
},
"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 scalar feature produced by the complete declared signal-analysis procedure.\nApply the declared fourth-order 0.5–8 Hz band-pass filter to channel `Plet` over `[0, 60)`, robust-z-score the filtered signal, and detect peaks with a minimum distance of 205 samples and prominence 0.5. Report the final requested scalar: the mean peak-interval rate per minute.\nExtract the final requested aggregate only, not intermediate filtered, tonic, phasic, normalized, peak, or smoothed values.\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 JSON key must be EXACTLY `feature_value`.\n5. The value must be one finite numeric scalar; integer counts are allowed when the requested feature is a count.\n6. If the final feature value is missing or cannot be identified, return {\"feature_value\": null}.\n\n### OUTPUT TEMPLATE\n{\"feature_value\": <number|null>}",
"metrics": [
{
"metric_id": "feature_value_accuracy",
"type": "numeric_check",
"target_key": "feature_value",
"weight": 100.0,
"params": {
"gt_value": 68.982596,
"tolerance": 6.8982596
}
}
]
}
}
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