Datasets:
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"meta_info": {
"case_id": "case13_02",
"bench_subset": "NeuroBench-Multi",
"difficult": 1.5,
"original_dataset": "DEAP"
},
"agent_input": {
"data_path": "data/multi/DEAP_02.bdf",
"instruction": "Analyze EEG channel `Fz` and GSR channel `GSR1` over [300, 600).\n\nDivide both streams into aligned, non-overlapping 30-second windows and pair the windows by index.\n\nFor each window, compute `beta_power` from the EEG signal as [13, 30) Hz band power. Compute `phasic_auc` from the GSR signal as positive phasic skin-conductance area.\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\": \"<positive|negative|null>\"}",
"metrics": [
{
"metric_id": "correlation_sign",
"type": "categorical_check",
"target_key": "correlation_sign",
"weight": 100,
"params": {
"gt_value": "negative"
}
}
]
}
}
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