{ "meta_info": { "case_id": "case13_20", "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" ], "instruction": "Analyze EEG channel `O2` over [20, 220) and fNIRS channels `S01D01_760`, `S01D01_850` over [18.105, 218.105).\n\nDivide both streams into aligned, non-overlapping 20-second windows and pair the windows by index.\n\nFor each window, compute `alpha_power` from the EEG signal as [8, 13) Hz band power. Compute `hbr_mean` from the fNIRS signal as mean HbR concentration change.\n\nKeep windows with finite values for both features. If at least 6 valid pairs remain and both feature sequences have non-zero variance, compute their Pearson correlation. Otherwise, the sign cannot be determined.\n\nReport correlation_sign as `positive` when the correlation is greater than zero and `negative` when it is less than zero. Report null when the correlation cannot be determined or is exactly zero.\n\nReport only correlation_sign." }, "eval_config": { "parser_prompt": "### ROLE\nYou are a precise JSON extraction engine for multimodal physiological correlation reports.\n\n### TASK\nExtract `correlation_sign`, the reported sign of the correlation between the two\nwindow-level feature sequences.\n\n### STRICT CONSTRAINTS\n1. Output ONLY one valid JSON object.\n2. Do not output Markdown, explanations, units, numeric correlation values, or extra fields.\n3. The only key must be `correlation_sign`.\n4. The value must be exactly `positive` or `negative`, or null if the sign is\n missing, zero, or unclear.\n\n### OUTPUT TEMPLATE\n{\"correlation_sign\": \"\"}", "metrics": [ { "metric_id": "correlation_sign", "type": "categorical_check", "target_key": "correlation_sign", "weight": 100, "params": { "gt_value": "negative" } } ] } }