Datasets:
File size: 1,884 Bytes
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"meta_info": {
"case_id": "MentalArithmetic_01.edf",
"bench_subset": "NeuroBench-Core",
"difficult": 1.0,
"original_dataset": "MentalArithmetic"
},
"agent_input": {
"data_path": "data/core/MentalArithmetic_01.edf",
"instruction": "Please first extract only the EEG channels from the raw signal, and then apply a 0.5-40Hz FIR bandpass filter to these channels. After filtering, obtain each channel's Alpha-band power and total filtered-signal power by integrating the PSD over frequency. Calculate Alpha relative power separately for each channel, then average the channel-wise ratios and report the final percentage value clearly in your response."
},
"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 'Alpha Relative Power Percentage' from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks (e.g., no backticks or json tags).\n3. DO NOT include any conversational text, explanations, or units (%).\n4. The Key in the JSON must be EXACTLY \"alpha_ratio\".\n5. The Value must be a FLOAT (e.g., if the report says 25.5%, output 25.5).\n6. If the value is missing or cannot be found, return {\"alpha_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"alpha_ratio\": <float>}",
"metrics": [
{
"metric_id": "alpha_ratio_accuracy",
"type": "numeric_check",
"target_key": "alpha_ratio",
"weight": 100,
"params": {
"gt_value": 41.33018074506845,
"tolerance": 4.133018074506846
}
}
]
}
}
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