Datasets:
File size: 2,348 Bytes
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"meta_info": {
"case_id": "BCIC2020-3_04.edf",
"bench_subset": "NeuroBench-Core",
"difficult": 1.5,
"original_dataset": "BCIC2020-3"
},
"agent_input": {
"data_path": "data/core/BCIC2020-3_04.edf",
"instruction": "Please select only the real EEG channels from the raw signal, apply a 0.5-45Hz FIR bandpass filter to rank channels by alpha relative energy ratio, identify the channel with the highest ratio, keep only that channel, apply an 8-13Hz FIR alpha bandpass filter, and save the processed signal as a .npy file to the file_check directory with the filename BCIC2020-3_04_case16_processed.npy."
},
"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 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.npy\"}",
"metrics": [
{
"metric_id": "save_alpha_top_channel_npy_status",
"type": "file_status_check",
"target_key": "file_path",
"weight": 100,
"params": {
"match_mode": "eeg_file_match",
"validator_config": {
"file_name": {
"value": "BCIC2020-3_04_case16_processed.npy"
},
"channel_count": {
"value": 1,
"weight": 30
},
"signal_rms": {
"value": 3.638633413116647e-05,
"weight": 70,
"tolerance": 3.6386334131166468e-06
}
}
}
}
]
}
}
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