{
  "meta_info": {
    "case_id": "case33_02",
    "bench_subset": "NeuroBench-Multi",
    "difficult": 1.5,
    "original_dataset": "simultaneous"
  },
  "agent_input": {
    "data_path": [
      "data/multi/simultaneous_02_run-gonogo1_EEG.edf",
      "data/multi/simultaneous_02_run-gonogo1_fNIRS.edf"
    ],
    "label_path": "data/multi/simultaneous_02_run-gonogo1_EEG.npy",
    "instruction": "Using complete `NoGo` events (event code `32`) within [44.039, 113.026) seconds, estimate the event-averaged frontal theta response from `FP1`, `AFz`, and `F1`, and the prefrontal HbO response from `S03D02`.\n\nBaseline-correct theta using `[-1, 0)` seconds and find its peak latency within `[0, 2)` seconds. Baseline-correct HbO using `[-2, 0)` seconds and find its peak latency within `[2, 10)` seconds.\n\nReport both latencies relative to event onset as `theta_peak_latency_sec` and `hbo_peak_latency_sec`. Briefly describe whether the delayed HbO peak follows the early theta peak. Treat this as an event-averaged temporal association, not proof of causal neurovascular coupling, response inhibition, or a clinical effect."
  },
  "eval_config": {
    "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for simultaneous EEG-fNIRS reports. Your sole task is to convert the agent's report into one JSON object.\n\n### TASK\nExtract the two event-averaged peak latencies relative to event onset:\n1. theta_peak_latency_sec\n2. hbo_peak_latency_sec\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY one valid JSON object.\n2. DO NOT include Markdown, explanations, units, or extra fields.\n3. The JSON keys must be EXACTLY \"theta_peak_latency_sec\" and \"hbo_peak_latency_sec\".\n4. Both values must be finite JSON numbers or null if unavailable.\n5. Do not infer a missing latency from the interpretation.\n\n### OUTPUT TEMPLATE\n{\"theta_peak_latency_sec\": <number|null>, \"hbo_peak_latency_sec\": <number|null>}",
    "metrics": [
      {
        "metric_id": "theta_peak_latency_sec",
        "type": "numeric_check",
        "target_key": "theta_peak_latency_sec",
        "weight": 35,
        "params": {
          "gt_value": 0.9500000000000001,
          "tolerance": 0.2
        }
      },
      {
        "metric_id": "hbo_peak_latency_sec",
        "type": "numeric_check",
        "target_key": "hbo_peak_latency_sec",
        "weight": 35,
        "params": {
          "gt_value": 3.8000000000000016,
          "tolerance": 0.5
        }
      },
      {
        "metric_id": "neurovascular_timing_interpretation",
        "type": "semantic_consistency",
        "target_key": "full_text",
        "weight": 30,
        "params": {
          "judge_prompt": "Judge whether the response correctly describes the event-averaged temporal ordering.\n\nGround truth:\n- theta_peak_latency_sec = 0.950000 seconds;\n- hbo_peak_latency_sec = 3.800000 seconds;\n- the HbO peak occurs later than the frontal theta peak.\n\nA correct response must:\n1. State or clearly imply that frontal theta peaks earlier;\n2. State or clearly imply that the prefrontal HbO response peaks later;\n3. Describe the result as an electrophysiological-to-hemodynamic temporal sequence or delayed hemodynamic response;\n4. Treat this as event-averaged temporal association rather than proof of causal neurovascular coupling;\n5. Avoid claiming definitive response inhibition, clinical dysfunction, or unsupported statistical significance;\n6. Not contradict the reported latency values.\n\nReturn exactly {\"status\": true} only when these requirements are met. Otherwise return {\"status\": false}. Output one JSON object only, with no Markdown or additional fields."
        }
      }
    ]
  }
}