Datasets:
| { | |
| "meta_info": { | |
| "case_id": "Q14", | |
| "bench_subset": "NeuroBench-Core", | |
| "difficult": 1.0, | |
| "original_dataset": "Core_QA", | |
| "references": "R12" | |
| }, | |
| "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 stimulus-response relationship does an SSVEP-based EEG-BCI usually rely on?\nA. Semantic violations eliciting a central N400.\nB. Hand movement eliciting frontal eye-blink artifacts.\nC. A visual stimulus flickering at a fixed frequency eliciting a steady-state visual response at the corresponding frequency.\nD. Cardiac rhythm eliciting changes in 10-20 electrode nomenclature." | |
| }, | |
| "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" | |
| } | |
| } | |
| ] | |
| } | |
| } |