Datasets:
File size: 1,910 Bytes
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"meta_info": {
"case_id": "Q32",
"bench_subset": "NeuroBench-Core",
"difficult": 1.0,
"original_dataset": "Core_QA",
"references": "R18"
},
"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: What is a potential cost of using a notch filter to remove 50 Hz line noise?\nA. It automatically improves the signal-to-noise ratio of all bands without side effects.\nB. It may affect nearby real signals and phase or waveform characteristics.\nC. It converts EEG into a bipolar montage.\nD. It restores bad-channel signals."
},
"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": "B"
}
}
]
}
} |