Datasets:
| { | |
| "meta_info": { | |
| "case_id": "BCIC2020-3_01.edf", | |
| "bench_subset": "NeuroBench-Core", | |
| "difficult": 1.5, | |
| "original_dataset": "BCIC2020-3" | |
| }, | |
| "agent_input": { | |
| "data_path": "data/core/BCIC2020-3_01.edf", | |
| "instruction": "Please select only the central-region EEG channels (C* and CP*). Apply a 50 Hz FIR notch filter, then a 0.5-45 Hz FIR bandpass filter, resample to 100 Hz, and re-reference using average reference. Use the first 90 seconds of each selected channel. Compute Hjorth Mobility and Hjorth Complexity for each channel, then report the channel-mean values as hjorth_mobility_mean and hjorth_complexity_mean." | |
| }, | |
| "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 final values from the agent report:\n1) Mean Hjorth Mobility\n2) Mean Hjorth Complexity\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in JSON must be EXACTLY \"hjorth_mobility_mean\" and \"hjorth_complexity_mean\".\n5. Both values must be FLOAT.\n6. If one value is missing, return it as null.\n\n### OUTPUT TEMPLATE\n{\"hjorth_mobility_mean\": <float|null>, \"hjorth_complexity_mean\": <float|null>}", | |
| "metrics": [ | |
| { | |
| "metric_id": "hjorth_mobility_accuracy", | |
| "type": "numeric_check", | |
| "target_key": "hjorth_mobility_mean", | |
| "weight": 50, | |
| "params": { | |
| "gt_value": 1.400182, | |
| "tolerance": 0.070009 | |
| } | |
| }, | |
| { | |
| "metric_id": "hjorth_complexity_accuracy", | |
| "type": "numeric_check", | |
| "target_key": "hjorth_complexity_mean", | |
| "weight": 50, | |
| "params": { | |
| "gt_value": 1.223736, | |
| "tolerance": 0.061187 | |
| } | |
| } | |
| ] | |
| } | |
| } | |