Datasets:
| { | |
| "meta_info": { | |
| "case_id": "Q07", | |
| "bench_subset": "NeuroBench-Core", | |
| "difficult": 1.0, | |
| "original_dataset": "Core_QA", | |
| "references": "R6" | |
| }, | |
| "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: Which statement most accurately describes the role of ICA in EEG artifact processing?\nA. ICA can automatically determine the neural origin of every independent component.\nB. ICA can help separate eye-movement or muscle-related components, but the components still need to be identified and validated.\nC. ICA is essentially a notch filter and can only remove 50/60 Hz noise.\nD. ICA preserves all brain components and removes artifacts without any risk." | |
| }, | |
| "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" | |
| } | |
| } | |
| ] | |
| } | |
| } |