| { |
| "meta_info": { |
| "case_id": "case35_02", |
| "bench_subset": "NeuroBench-Multi", |
| "difficult": 1.5, |
| "original_dataset": "simultaneous" |
| }, |
| "agent_input": { |
| "data_path": [ |
| "data/multi/simultaneous_02_run-gonogo1_EEG.edf", |
| "data/multi/simultaneous_02_run-gonogo1_fNIRS.edf" |
| ], |
| "label_path": "data/multi/simultaneous_02_run-gonogo1_EEG.npy", |
| "instruction": "Use all complete NoGo events (`event_code = 32`) whose onset falls within `[78.629, 143.184)` seconds.\n\nFor each event, compute theta power `[4, 8)` Hz over `FP1`, `AFz`, and `F1` for baseline `[-1, 0)` and response `[0, 1)` using a 1-second Hann-window Welch spectrum. Average the three channel powers and retain the event when response theta is higher than baseline theta.\n\nFor each retained event, use fNIRS channels `S03D02_760` and `S03D02_850`, with `[-2, 0)` as baseline and `[4, 8)` as response. Convert the `760`- and `850`-nm signals to HbO and HbR changes using the modified Beer–Lambert law with extinction coefficients `(1.4666, 3.8437)` and `(2.5264, 1.7986)`, a 3-cm source–detector distance, and DPF `6`. Use the mean baseline intensity as the optical-density reference and the natural logarithm for the intensity-to-optical-density conversion. Apply no additional fNIRS filtering and keep the resulting concentrations in mM.\n\nFor HbO and HbR separately, compute the response mean minus the baseline mean and average the changes across retained events.\n\nReport only `mean_hbo_change` and `mean_hbr_change`." |
| }, |
| "eval_config": { |
| "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for simultaneous EEG-fNIRS reports. Your sole task is to convert the agent's report into one JSON object.\n\n### TASK\nExtract the mean baseline-corrected responses across the theta-positive NoGo events:\n1. mean_hbo_change\n2. mean_hbr_change\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY one valid JSON object.\n2. DO NOT include Markdown code blocks, explanations, units, event counts, or extra fields.\n3. The JSON keys must be EXACTLY \"mean_hbo_change\" and \"mean_hbr_change\".\n4. Both values must be finite JSON numbers, or null when the requested value is missing or unclear.\n5. Do not return values from an individual event or an intermediate calculation.\n\n### OUTPUT TEMPLATE\n{\"mean_hbo_change\": <number|null>, \"mean_hbr_change\": <number|null>}", |
| "metrics": [ |
| { |
| "metric_id": "mean_hbo_change", |
| "type": "numeric_check", |
| "target_key": "mean_hbo_change", |
| "weight": 50, |
| "params": { |
| "gt_value": 0.00014810572729792863, |
| "tolerance": 2.9621145459585728e-05 |
| } |
| }, |
| { |
| "metric_id": "mean_hbr_change", |
| "type": "numeric_check", |
| "target_key": "mean_hbr_change", |
| "weight": 50, |
| "params": { |
| "gt_value": -4.00234087006518e-05, |
| "tolerance": 8.00468174013036e-06 |
| } |
| } |
| ] |
| } |
| } |
|
|
