Datasets:
File size: 3,066 Bytes
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"meta_info": {
"case_id": "Mumtaz2016_03.edf",
"bench_subset": "NeuroBench-Core",
"difficult": 1.0,
"original_dataset": "Mumtaz2016"
},
"agent_input": {
"data_path": "data/core/Mumtaz2016_03.edf",
"instruction": "Please keep only the real EEG channels from the raw signal, use the first 120 seconds, re-reference the EEG data to average reference, and save the processed EEG EDF file to the file_check directory with the filename Mumtaz2016_03_case15_processed.edf."
},
"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 saved EEG file path from the agent report.\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 or explanations.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed.edf\"}",
"metrics": [
{
"metric_id": "save_avgref_real_eeg_edf_status",
"type": "file_status_check",
"target_key": "file_path",
"weight": 100,
"params": {
"match_mode": "eeg_file_match",
"validator_config": {
"file_name": {
"value": "Mumtaz2016_03_case15_processed.edf"
},
"duration_sec": {
"value": 120.0,
"weight": 33
},
"channel_names": {
"value": [
"Fp1-LE",
"F3-LE",
"C3-LE",
"P3-LE",
"O1-LE",
"F7-LE",
"T3-LE",
"T5-LE",
"Fz-LE",
"Fp2-LE",
"F4-LE",
"C4-LE",
"P4-LE",
"O2-LE",
"F8-LE",
"T4-LE",
"T6-LE",
"Cz-LE",
"Pz-LE"
],
"weight": 34
},
"reference_mode": {
"value": "average",
"weight": 33
}
}
}
}
]
}
} |