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Restore naturally ordered Foundational Analysis case paths
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{
"meta_info": {
"case_id": "SEED-V-03.cnt",
"bench_subset": "NeuroBench-Core",
"difficult": 2.0,
"original_dataset": "SEED-V"
},
"agent_input": {
"data_path": "data/core/SEED-V-03.cnt",
"instruction": "Please select standard EEG channels, compute alpha power for each channel, and rank channels by alpha power in descending order. Output the top-5 channel names. Then determine the dominant brain region based on the number of top-5 channels in each region. If multiple regions tie for the highest count, output all tied regions. Use this region mapping: frontal=[FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8]; central=[FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, CP3, CPZ, CP4]; parietal=[TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8]; occipital=[PO7, PO5, PO3, POZ, PO4, PO6, PO8, O1, OZ, O2, CB1, CB2]."
},
"eval_config": {
"parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) top-5 channels ranked by alpha power\n2) dominant brain region or tied dominant brain regions\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys must be EXACTLY \"top5_alpha_power_channels\" and \"dominant_regions\".\n5. Extract the final top-5 ranked list, not a channel inventory or an intermediate table. top5_alpha_power_channels must contain exactly 5 strings or be null.\n6. Normalize EACH reported channel independently: trim whitespace, remove one leading acquisition prefix EEG followed by a space, hyphen, or underscore, then uppercase the label.\n7. Remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG.\n8. Convert legacy aliases after removing the prefix and suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Each channel label must contain only uppercase A-Z letters and digits. Examples: O2-A1 -> O2; EEG Fp2-LE -> FP2; EEG T6 -> P8; Pz -> PZ. Preserve descending alpha-power order and do not sort alphabetically.\n10. For dominant_regions, lowercase the explicitly reported final dominant or tied regions and keep only exact values from: frontal, central, parietal, occipital. Ignore unsupported region labels and do not map or guess them.\n11. If the top-5 list is missing or does not contain exactly 5 identifiable labels, set top5_alpha_power_channels to null. If no supported dominant region is explicit, set dominant_regions to null.\n\n### OUTPUT TEMPLATE\n{\"top5_alpha_power_channels\": [\"CH1\", \"CH2\", \"CH3\", \"CH4\", \"CH5\"], \"dominant_regions\": [\"region\"]}",
"metrics": [
{
"metric_id": "top5_alpha_power_sequence",
"type": "sequence_match_check",
"target_key": "top5_alpha_power_channels",
"weight": 60,
"params": {
"gt_value": [
"F2",
"F4",
"C2",
"CB1",
"CB2"
],
"match_mode": "weighted_partial_order",
"top_k": 5,
"position_weights": [
1.0,
0.8,
0.6,
0.4,
0.2
],
"min_overlap": 3,
"allow_order_slip": 1
}
},
{
"metric_id": "dominant_region_set",
"type": "set_match_check",
"target_key": "dominant_regions",
"weight": 40,
"params": {
"gt_value": [
"frontal",
"occipital"
],
"match_mode": "exact"
}
}
]
}
}