foundational_analysis/cases/case01/case01_06.json

{
    "meta_info": {
        "case_id": "BCIC2020-3_01.edf",
        "bench_subset": "NeuroBench-Core",
        "difficult": 1.0,
        "original_dataset": "BCIC2020-3"
    },
    "agent_input": {
        "data_path": "data/core/BCIC2020-3_01.edf",
        "instruction": "Please first extract only the EEG channels from the raw signal, and then apply a 0.5-40Hz FIR bandpass filter to these channels. After filtering, obtain each channel's Alpha-band power and total filtered-signal power by integrating the PSD over frequency. Calculate Alpha relative power separately for each channel, then average the channel-wise ratios and report the final percentage value clearly in your response."
    },
    "eval_config": {
        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 'Alpha Relative Power Percentage' from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks (e.g., no backticks or json tags).\n3. DO NOT include any conversational text, explanations, or units (%).\n4. The Key in the JSON must be EXACTLY \"alpha_ratio\".\n5. The Value must be a FLOAT (e.g., if the report says 25.5%, output 25.5).\n6. If the value is missing or cannot be found, return {\"alpha_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"alpha_ratio\": <float>}",
        "metrics": [
            {
                "metric_id": "alpha_ratio_accuracy",
                "type": "numeric_check",
                "target_key": "alpha_ratio",
                "weight": 100,
                "params": {
                    "gt_value": 13.560618171116563,
                    "tolerance": 1.3560624385726978
                }
            }
        ]
    }
}