{ "meta_info": { "case_id": "case13_28", "bench_subset": "NeuroBench-Multi", "difficult": 1.5, "original_dataset": "SEED-VII" }, "agent_input": { "data_path": [ "data/multi/SEEDVII_04_session-20221030_1_EEG.cnt", "data/multi/SEEDVII_04_session-20221030_1_EyeTracking.edf" ], "instruction": "Analyze EEG channel `CZ` and eye tracking channels `Gaze point X`, `Gaze point Y` over [240, 320).\n\nDivide both streams into aligned, non-overlapping 8-second windows and pair the windows by index.\n\nFor each window, compute `theta_alpha_ratio` from the EEG signal as theta-to-alpha power ratio using [4, 8) and [8, 13) Hz. Compute `gaze_path_length` from the eye tracking signal as total Euclidean path length across consecutive valid gaze samples.\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" } } ] } }