| { |
| "meta_info": { |
| "case_id": "ISRUC_02.edf", |
| "bench_subset": "NeuroBench-Sleep", |
| "difficult": 1.5, |
| "original_dataset": "ISRUC" |
| }, |
| "agent_input": { |
| "data_path": "data/sleep/ISRUC_02.edf", |
| "instruction": "Please analyze the provided sleep file in the time window from minute 462.0 to minute 465.0. For this ISRUC file, X1, X3, and X4 are EMG channels. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." |
| }, |
| "eval_config": { |
| "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\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 outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": <float|null>, \"full_text\": <string|null>}", |
| "metrics": [ |
| { |
| "metric_id": "event_chin_emg_rms_accuracy", |
| "type": "numeric_check", |
| "target_key": "event_emg_rms", |
| "weight": 40, |
| "params": { |
| "gt_value": 6.807735444943595e-08, |
| "tolerance": 6.807735444943596e-09 |
| } |
| }, |
| { |
| "metric_id": "rem_like_semantic_consistency", |
| "type": "semantic_consistency", |
| "target_key": "full_text", |
| "weight": 60, |
| "params": { |
| "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The analyzed event window is 462.0-465.0 minutes. The current window is a true REM segment. The EMG channels are X1, X3, X4. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 6.80773544494e-08; the whole-night EMG RMS is 1.04680457673e-06; the event/whole-night RMS ratio is 0.065033. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." |
| } |
| } |
| ] |
| } |
| } |
|
|
