Datasets:
File size: 1,926 Bytes
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"meta_info": {
"case_id": "Q09",
"bench_subset": "NeuroBench-Core",
"difficult": 1.0,
"original_dataset": "Core_QA",
"references": "R8"
},
"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: Why are multiple trials often averaged in ERP studies?\nA. To improve the signal-to-noise ratio of activity that is time-locked to the event.\nB. To convert all non-phase-locked activity into phase-locked activity.\nC. To remove all eye-movement, muscle, and reference effects.\nD. To raise the spatial resolution of EEG to the level of fMRI."
},
"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": "A"
}
}
]
}
} |