Datasets:
| { | |
| "meta_info": { | |
| "case_id": "Q23", | |
| "bench_subset": "NeuroBench-Core", | |
| "difficult": 1.0, | |
| "original_dataset": "Core_QA", | |
| "references": "R23" | |
| }, | |
| "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 problem is surface Laplacian or CSD transformation often used to improve?\nA. Reducing reference dependence and enhancing local scalp patterns related to radial current flow.\nB. Recovering frequencies already aliased by an insufficient sampling rate.\nC. Converting scalp EEG directly into single-neuron firing.\nD. Eliminating all errors in deep source localization." | |
| }, | |
| "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" | |
| } | |
| } | |
| ] | |
| } | |
| } |