Datasets:
| { | |
| "meta_info": { | |
| "case_id": "SEED-V-03.cnt", | |
| "bench_subset": "NeuroBench-Core", | |
| "difficult": 1.5, | |
| "original_dataset": "SEED-V" | |
| }, | |
| "agent_input": { | |
| "data_path": "data/core/SEED-V-03.cnt", | |
| "instruction": "Please select only the real EEG channels from the raw signal, apply a 0.5-45Hz FIR bandpass filter to rank channels by alpha relative energy ratio, identify the channel with the highest ratio, keep only that channel, apply an 8-13Hz FIR alpha bandpass filter, and save the processed signal as a .npy file to the file_check directory with the filename SEED-V_03_case16_processed.npy." | |
| }, | |
| "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 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.npy\"}", | |
| "metrics": [ | |
| { | |
| "metric_id": "save_alpha_top_channel_npy_status", | |
| "type": "file_status_check", | |
| "target_key": "file_path", | |
| "weight": 100, | |
| "params": { | |
| "match_mode": "eeg_file_match", | |
| "validator_config": { | |
| "file_name": { | |
| "value": "SEED-V_03_case16_processed.npy" | |
| }, | |
| "channel_count": { | |
| "value": 1, | |
| "weight": 30 | |
| }, | |
| "signal_rms": { | |
| "value": 0.0007587429000632526, | |
| "weight": 70, | |
| "tolerance": 7.587429000632527e-05 | |
| } | |
| } | |
| } | |
| } | |
| ] | |
| } | |
| } | |