{
    "meta_info": {
        "case_id": "MentalArithmetic_03.edf",
        "bench_subset": "NeuroBench-Core",
        "difficult": 1.0,
        "original_dataset": "MentalArithmetic"
    },
    "agent_input": {
        "data_path": "data/core/MentalArithmetic_03.edf",
        "instruction": "Please select the first 30 seconds of prefrontal EEG channels from the raw signal, apply a 0.5-30Hz FIR bandpass filter, downsample to 100Hz, and save the processed EEG EDF file to the file_check directory with the filename MentalArithmetic_03_case14_processed.edf."
    },
    "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 EEG 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.edf\"}",
        "metrics": [
            {
                "metric_id": "processed_eeg_file_status",
                "type": "file_status_check",
                "target_key": "file_path",
                "weight": 100,
                "params": {
                    "match_mode": "eeg_file_match",
                    "validator_config": {
                        "file_name": {
                            "value": "MentalArithmetic_03_case14_processed.edf"
                        },
                        "duration_sec": {
                            "value": 30.0,
                            "weight": 25
                        },
                        "sfreq_hz": {
                            "value": 100.0,
                            "weight": 25
                        },
                        "channel_names": {
                            "value": [
                                "Fp1",
                                "Fp2"
                            ],
                            "weight": 25
                        },
                        "bandpass_hz": {
                            "value": [
                                0.5,
                                30.0
                            ],
                            "weight": 25
                        }
                    }
                }
            }
        ]
    }
}