Datasets:
File size: 2,690 Bytes
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"meta_info": {
"case_id": "MentalArithmetic_02.edf",
"bench_subset": "NeuroBench-Core",
"difficult": 1.0,
"original_dataset": "MentalArithmetic"
},
"agent_input": {
"data_path": "data/core/MentalArithmetic_02.edf",
"instruction": "Please select the first 30 seconds of prefrontal EEG channels from the raw signal, apply a 0.5-30Hz FIR bandpass filter, downsample to 100Hz, and save the processed EEG EDF file to the file_check directory with the filename MentalArithmetic_02_case14_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": "processed_eeg_file_status",
"type": "file_status_check",
"target_key": "file_path",
"weight": 100,
"params": {
"match_mode": "eeg_file_match",
"validator_config": {
"file_name": {
"value": "MentalArithmetic_02_case14_processed.edf"
},
"duration_sec": {
"value": 30.0,
"weight": 25
},
"sfreq_hz": {
"value": 100.0,
"weight": 25
},
"channel_names": {
"value": [
"Fp1",
"Fp2"
],
"weight": 25
},
"bandpass_hz": {
"value": [
0.5,
30.0
],
"weight": 25
}
}
}
}
]
}
}
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