Datasets:
File size: 1,934 Bytes
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"meta_info": {
"case_id": "Q02",
"bench_subset": "NeuroBench-Core",
"difficult": 1.0,
"original_dataset": "Core_QA",
"references": "R3"
},
"agent_input": {
"data_path": "",
"instruction": "This is a standalone EEG/BCI knowledge multiple-choice question. No EEG data file is needed. Please answer by selecting exactly one option: A, B, C, or D.\n\nQuestion: Compared with fMRI, what is the most characteristic methodological advantage of EEG?\nA. It more easily provides millimeter-level cortical source localization.\nB. It is more sensitive to deep structures than to cortical activity.\nC. It has millisecond-level temporal resolution.\nD. It is inherently unaffected by head models and volume conduction."
},
"eval_config": {
"parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for multiple-choice EEG/BCI QA responses. Your sole task is to extract the final selected answer option.\n\n### TASK\nExtract the final selected option 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, explanations, or units.\n4. The key must be EXACTLY \"selected_option\".\n5. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n6. If multiple options are mentioned, extract only the final answer option.\n7. If the selected option is missing or unclear, return {\"selected_option\": null}.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": <string|null>}",
"metrics": [
{
"metric_id": "qa_option_accuracy",
"type": "categorical_check",
"target_key": "selected_option",
"weight": 100,
"params": {
"gt_value": "C"
}
}
]
}
} |