Datasets:
File size: 3,486 Bytes
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"meta_info": {
"case_id": "case06_02",
"bench_subset": "NeuroBench-Multi",
"difficult": 1.5,
"original_dataset": "SEED-VIG"
},
"agent_input": {
"data_path": "data/multi/SEEDVIG_02_session-20151106_noon.edf",
"instruction": "Using EEG channels `FT7, FT8, T7, T8, TP7, TP8` and EOG channels `EOG-H, EOG-V`, analyze the target interval `[456, 460)` seconds.\n\nFor the target interval and each of the following same-record reference intervals—`[1032, 1036)`, `[1264, 1268)`, `[1496, 1500)`, `[1728, 1732)`, `[1960, 1964)`, `[2192, 2196)`, `[2424, 2428)`, `[2656, 2660)`, `[2888, 2892)`, `[3120, 3124)`, `[3352, 3356)`, `[3584, 3588)` seconds—compute the RMS of each listed channel using its finite samples, then take the median across channels separately for EEG and EOG. Do not apply a logarithmic transform.\n\nFor each modality, standardize the target median-RMS value using the mean and population standard deviation (ddof=0) of the corresponding 12 reference median-RMS values. The reference standard deviation is guaranteed to be positive.\n\nSet `artifact_present` to `true` only if both `eeg_z_score` and `eog_z_score` are at least 1.0; otherwise, set it to `false`.\n\nReturn only one valid JSON object containing exactly:\n\n{\n\"eeg_z_score\": <finite number>, \n\"eog_z_score\": <finite number>,\n\"artifact_present\": <true or false>\n}"
},
"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- eeg_z_score: Eeg z score. A finite z-score for the EEG target median-RMS value, standardized with the mean and population standard deviation (ddof=0) of the 12 EEG reference median-RMS values.\n- eog_z_score: Eog z score. A finite z-score for the EOG target median-RMS value, standardized with the mean and population standard deviation (ddof=0) of the 12 EOG reference median-RMS values.\n- artifact_present: Artifact present. true only when both eeg_z_score and eog_z_score are at least 1.0; otherwise false.\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{\"eeg_z_score\": <float>, \"eog_z_score\": <float>, \"artifact_present\": <true or false>}",
"metrics": [
{
"metric_id": "eeg_z_score",
"type": "numeric_check",
"target_key": "eeg_z_score",
"weight": 40,
"params": {
"gt_value": -0.34210346404523095,
"tolerance": 0.0342103464045231
}
},
{
"metric_id": "eog_z_score",
"type": "numeric_check",
"target_key": "eog_z_score",
"weight": 40,
"params": {
"gt_value": -0.5779596343074518,
"tolerance": 0.05779596343074518
}
},
{
"metric_id": "artifact_present",
"type": "categorical_check",
"target_key": "artifact_present",
"weight": 20,
"params": {
"gt_value": false
}
}
]
}
}
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