{
    "meta_info": {
        "case_id": "Q50",
        "bench_subset": "NeuroBench-Core",
        "difficult": 1.0,
        "original_dataset": "Core_QA",
        "references": "R26,R27"
    },
    "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: Why should waveform shape and filtering artifacts be treated carefully in PAC analysis?\nA. PAC is completely unaffected by preprocessing.\nB. PAC exists only in fMRI.\nC. PAC does not require statistical or surrogate testing.\nD. Non-sinusoidal waveforms, sharp transients, filter choices, and noise can create apparent cross-frequency coupling patterns."
    },
    "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": "D"
                }
            }
        ]
    }
}