Datasets:
| { | |
| "meta_info": { | |
| "case_id": "Q33", | |
| "bench_subset": "NeuroBench-Core", | |
| "difficult": 1.0, | |
| "original_dataset": "Core_QA", | |
| "references": "R18,R19" | |
| }, | |
| "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 typical risk of setting the high-pass filter cutoff too high in ERP research?\nA. It only removes muscle activity and does not affect ERPs.\nB. It improves spatial resolution.\nC. It may distort waveforms and change the amplitude or latency of slow components.\nD. It automatically corrects the baseline." | |
| }, | |
| "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" | |
| } | |
| } | |
| ] | |
| } | |
| } |