{ "meta_info": { "case_id": "case13_30", "bench_subset": "NeuroBench-Multi", "difficult": 1.5, "original_dataset": "SEED-VII" }, "agent_input": { "data_path": [ "data/multi/SEEDVII_01_session-20221001_1_EEG.cnt", "data/multi/SEEDVII_01_session-20221001_1_EyeTracking.edf" ], "instruction": "Analyze EEG channel `FZ` and eye tracking channel `Pupil diameter left` over [400, 480).\n\nDivide both streams into aligned, non-overlapping 8-second windows and pair the windows by index.\n\nFor each window, compute `beta_alpha_ratio` from the EEG signal as beta-to-alpha power ratio using [13, 30) and [8, 13) Hz. Compute `pupil_slope` from the eye tracking signal as left-pupil diameter slope against time.\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": "positive" } } ] } }