| {
|
| "meta_info": {
|
| "case_id": "MentalArithmetic_04.edf",
|
| "bench_subset": "NeuroBench-Core",
|
| "difficult": 1.5,
|
| "original_dataset": "MentalArithmetic"
|
| },
|
| "agent_input": {
|
| "data_path": "data/core/MentalArithmetic_04.edf",
|
| "instruction": "Please select EEG channels, downsample to 200Hz, apply a 0.5-45Hz FIR bandpass filter, compute channel-wise kurtosis and skewness, then list four channel names in descending order by kurtosis and four channel names in descending order by skewness."
|
| },
|
| "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 ranked channel sequences from the agent report:\n1) Top-4 channels by kurtosis (descending)\n2) Top-4 channels by skewness (descending)\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 in JSON must be EXACTLY \"kurtosis_top4_channels\" and \"skewness_top4_channels\".\n5. Extract the two final ranked lists, not channel inventories or intermediate values. Each value must be an array of exactly 4 strings.\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 output label must contain only uppercase A-Z letters and digits. Examples: O2-A1 -> O2; EEG Fp1-LE -> FP1; EEG T4 -> T8; Cz -> CZ.\n10. Preserve the descending rank order within each list. Do not sort either array alphabetically and do not substitute different channels.\n11. If one final list does not contain exactly 4 identifiable channel labels, return null only for that value; do not guess.\n\n### OUTPUT TEMPLATE\n{\"kurtosis_top4_channels\": [\"CH1\",\"CH2\",\"CH3\",\"CH4\"], \"skewness_top4_channels\": [\"CH1\",\"CH2\",\"CH3\",\"CH4\"]}",
|
| "metrics": [
|
| {
|
| "metric_id": "kurtosis_top4_sequence",
|
| "type": "sequence_match_check",
|
| "target_key": "kurtosis_top4_channels",
|
| "weight": 50,
|
| "params": {
|
| "gt_value": [
|
| "F8",
|
| "F7",
|
| "F4",
|
| "P3"
|
| ],
|
| "match_mode": "weighted_partial_order",
|
| "top_k": 4,
|
| "position_weights": [
|
| 1.0,
|
| 0.8,
|
| 0.6,
|
| 0.4
|
| ],
|
| "min_overlap": 2,
|
| "allow_order_slip": 1
|
| }
|
| },
|
| {
|
| "metric_id": "skewness_top4_sequence",
|
| "type": "sequence_match_check",
|
| "target_key": "skewness_top4_channels",
|
| "weight": 50,
|
| "params": {
|
| "gt_value": [
|
| "O1",
|
| "PZ",
|
| "F4",
|
| "P3"
|
| ],
|
| "match_mode": "weighted_partial_order",
|
| "top_k": 4,
|
| "position_weights": [
|
| 1.0,
|
| 0.8,
|
| 0.6,
|
| 0.4
|
| ],
|
| "min_overlap": 2,
|
| "allow_order_slip": 1
|
| }
|
| }
|
| ]
|
| }
|
| }
|
|
|
