{ "meta_info": { "case_id": "BCIC2020-3_03.edf", "bench_subset": "NeuroBench-Core", "difficult": 1.0, "original_dataset": "BCIC2020-3" }, "agent_input": { "data_path": "data/core/BCIC2020-3_03.edf", "instruction": "Please keep only the real EEG channels from the raw signal, use the first 120 seconds, re-reference the EEG data to average reference, and save the processed EEG EDF file to the file_check directory with the filename BCIC2020-3_03_case15_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": "save_avgref_real_eeg_edf_status", "type": "file_status_check", "target_key": "file_path", "weight": 100, "params": { "match_mode": "eeg_file_match", "validator_config": { "file_name": { "value": "BCIC2020-3_03_case15_processed.edf" }, "duration_sec": { "value": 120.0, "weight": 33 }, "channel_names": { "value": [ "Fp1", "Fp2", "F7", "F3", "Fz", "F4", "F8", "FC5", "FC1", "FC2", "FC6", "T7", "C3", "Cz", "C4", "T8", "TP9", "CP5", "CP1", "CP2", "CP6", "TP10", "P7", "P3", "Pz", "P4", "P8", "PO9", "O1", "Oz", "O2", "PO10", "AF7", "AF3", "AF4", "AF8", "F5", "F1", "F2", "F6", "FT9", "FT7", "FC3", "FC4", "FT8", "FT10", "C5", "C1", "C2", "C6", "TP7", "CP3", "CPz", "CP4", "TP8", "P5", "P1", "P2", "P6", "PO7", "PO3", "POz", "PO4", "PO8" ], "weight": 34 }, "reference_mode": { "value": "average", "weight": 33 } } } } ] } }