Datasets:
File size: 2,251 Bytes
752e201 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"meta_info": {
"case_id": "case08_02",
"bench_subset": "NeuroBench-Multi",
"difficult": 1,
"original_dataset": "DEAP"
},
"agent_input": {
"data_path": "data/multi/DEAP_04.bdf",
"instruction": "From GSR/EDA channel `GSR1`, analyze the half-open interval `[60, 120)` seconds at its native sampling rate.\n\nApply the declared fourth-order 5 Hz low-pass filter.\nEstimate the tonic component using a centered 5120-sample moving average with reflect padding, and define the phasic component as the filtered signal minus the tonic component.\nSet `feature_value` to the linear slope per second of the tonic component over the interval."
},
"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 5 Hz low-pass filter to channel `GSR1` over `[60, 120)`. Estimate the tonic component with a centered 5120-sample moving average using reflect padding, and define the phasic component as filtered signal minus tonic component. Report the final requested scalar: the linear slope per second of the tonic component.\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": 0.310323,
"tolerance": 0.031032300000000002
}
}
]
}
}
|