{
    "meta_info": {
        "case_id": "BCIC2020-3_02.edf",
        "bench_subset": "NeuroBench-Core",
        "difficult": 1.5,
        "original_dataset": "BCIC2020-3"
    },
    "agent_input": {
        "data_path": "data/core/BCIC2020-3_02.edf",
        "instruction": "Please select only the real EEG channels from the raw signal, apply a 0.5-45Hz FIR bandpass filter to rank channels by alpha relative energy ratio, identify the channel with the highest ratio, keep only that channel, apply an 8-13Hz FIR alpha bandpass filter, and save the processed signal as a .npy file to the file_check directory with the filename BCIC2020-3_02_case16_processed.npy."
    },
    "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 saved file path 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 or explanations.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed.npy\"}",
        "metrics": [
            {
                "metric_id": "save_alpha_top_channel_npy_status",
                "type": "file_status_check",
                "target_key": "file_path",
                "weight": 100,
                "params": {
                    "match_mode": "eeg_file_match",
                    "validator_config": {
                        "file_name": {
                            "value": "BCIC2020-3_02_case16_processed.npy"
                        },
                        "channel_count": {
                            "value": 1,
                            "weight": 30
                        },
                        "signal_rms": {
                            "value": 3.6918619051987136e-05,
                            "weight": 70,
                            "tolerance": 3.691861905198714e-06
                        }
                    }
                }
            }
        ]
    }
}