Datasets:
File size: 2,773 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 determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
},
"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 two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\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. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
"metrics": [
{
"metric_id": "dominant_band_accuracy",
"type": "categorical_check",
"target_key": "dominant_band",
"weight": 70,
"params": {
"gt_value": "delta"
}
},
{
"metric_id": "secondary_band_accuracy",
"type": "categorical_check",
"target_key": "secondary_band",
"weight": 30,
"params": {
"gt_value": "alpha"
}
}
]
}
}
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