Datasets:
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"meta_info": {
"case_id": "case09_02",
"bench_subset": "NeuroBench-Multi",
"difficult": 1,
"original_dataset": "SEED-VIG"
},
"agent_input": {
"data_path": "data/multi/SEEDVIG_02_session-20151106_noon.edf",
"instruction": "From EOG channel `eog_v`, compute the EOG RMS over the half-open interval `[80, 88)` seconds.\n\nUsing the channel’s native sampling rate and physical values decoded from the EDF file, convert them to microvolts (uV). Apply a fourth-order zero-phase Butterworth band-pass filter from `0.1` to `15` Hz to the full available channel. Then extract `[80, 88)` seconds from the filtered signal and compute its root-mean-square value in uV.\n\nReport only `feature_value`."
},
"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 following final answer fields from the agent's report into one JSON object:\n- feature_value: Feature value.\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 extra fields.\n4. The JSON keys must be EXACTLY the keys listed in the OUTPUT TEMPLATE.\n5. Preserve the declared value type for every key.\n6. If a requested value is missing or cannot be found, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"feature_value\": <float>}",
"metrics": [
{
"metric_id": "feature_value_accuracy",
"type": "numeric_check",
"target_key": "feature_value",
"weight": 100,
"params": {
"gt_value": 61.05082239310536,
"tolerance": 6.105082239310537
}
}
]
}
}
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