| { |
| "meta_info": { |
| "case_id": "case05_02", |
| "bench_subset": "NeuroBench-Multi", |
| "difficult": 1.5, |
| "original_dataset": "DEAP" |
| }, |
| "agent_input": { |
| "data_path": "data/multi/DEAP_02.bdf", |
| "instruction": "Extract Resp (Respiration) and Plet (PPG/BVP) from the same declared real-time interval [5, 7) seconds. Use each channel's native sampling rate and convert both time boundaries independently; never reuse sample indices between modalities. Apply the public annotation alignment rule and save each one-dimensional segment as a one-channel EDF: the first at file_check/case05_02_signal_1.edf and the second at file_check/case05_02_signal_2.edf. Return only signal_1_file and signal_2_file.", |
| "label_path": "data/multi/DEAP_02.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 following final answer fields from the agent's report into one JSON object:\n- signal_1_file: Signal 1 file.\n- signal_2_file: Signal 2 file.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON keys must be EXACTLY the keys listed in the OUTPUT TEMPLATE.\n5. Preserve the declared value type for every key.\n6. If a requested value is missing or cannot be found, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"signal_1_file\": null, \"signal_2_file\": null}", |
| "metrics": [ |
| { |
| "metric_id": "signal_1_file_status", |
| "type": "file_status_check", |
| "target_key": "signal_1_file", |
| "weight": 50, |
| "params": { |
| "match_mode": "eeg_file_match", |
| "validator_config": { |
| "file_name": { |
| "value": "case05_02_signal_1.edf" |
| }, |
| "duration_sec": { |
| "value": 2.0, |
| "weight": 25, |
| "tolerance": 1e-06 |
| }, |
| "sfreq_hz": { |
| "value": 512.0, |
| "weight": 25, |
| "tolerance": 1e-06 |
| }, |
| "channel_names": { |
| "value": [ |
| "Resp" |
| ], |
| "weight": 25 |
| }, |
| "signal_rms": { |
| "value": 0.04285457990540631, |
| "weight": 25, |
| "tolerance": 0.004285457990540631 |
| } |
| } |
| } |
| }, |
| { |
| "metric_id": "signal_2_file_status", |
| "type": "file_status_check", |
| "target_key": "signal_2_file", |
| "weight": 50, |
| "params": { |
| "match_mode": "eeg_file_match", |
| "validator_config": { |
| "file_name": { |
| "value": "case05_02_signal_2.edf" |
| }, |
| "duration_sec": { |
| "value": 2.0, |
| "weight": 25, |
| "tolerance": 1e-06 |
| }, |
| "sfreq_hz": { |
| "value": 512.0, |
| "weight": 25, |
| "tolerance": 1e-06 |
| }, |
| "channel_names": { |
| "value": [ |
| "Plet" |
| ], |
| "weight": 25 |
| }, |
| "signal_rms": { |
| "value": 0.01671554772251551, |
| "weight": 25, |
| "tolerance": 0.0016715547722515511 |
| } |
| } |
| } |
| } |
| ] |
| } |
| } |
|
|
