Datasets:
| { | |
| "meta_info": { | |
| "case_id": "MentalArithmetic_01.edf", | |
| "bench_subset": "NeuroBench-Core", | |
| "difficult": 1.0, | |
| "original_dataset": "MentalArithmetic" | |
| }, | |
| "agent_input": { | |
| "data_path": "data/core/MentalArithmetic_01.edf", | |
| "instruction": "Please select the first 30 seconds of prefrontal EEG channels from the raw signal, apply a 0.5-30Hz FIR bandpass filter, downsample to 100Hz, and save the processed EEG EDF file to the file_check directory with the filename MentalArithmetic_01_case14_processed.edf." | |
| }, | |
| "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 the saved EEG file path from the agent report.\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. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed.edf\"}", | |
| "metrics": [ | |
| { | |
| "metric_id": "processed_eeg_file_status", | |
| "type": "file_status_check", | |
| "target_key": "file_path", | |
| "weight": 100, | |
| "params": { | |
| "match_mode": "eeg_file_match", | |
| "validator_config": { | |
| "file_name": { | |
| "value": "MentalArithmetic_01_case14_processed.edf" | |
| }, | |
| "duration_sec": { | |
| "value": 30.0, | |
| "weight": 25 | |
| }, | |
| "sfreq_hz": { | |
| "value": 100.0, | |
| "weight": 25 | |
| }, | |
| "channel_names": { | |
| "value": [ | |
| "Fp1", | |
| "Fp2" | |
| ], | |
| "weight": 25 | |
| }, | |
| "bandpass_hz": { | |
| "value": [ | |
| 0.5, | |
| 30.0 | |
| ], | |
| "weight": 25 | |
| } | |
| } | |
| } | |
| } | |
| ] | |
| } | |
| } | |