发表机构
Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用外周生理信号的多模态深度学习解码器，在飞行任务中实现更高精度的唤醒解码，并揭示个体化唤醒动态，为闭环神经反馈提供个性化调节规范。
AI 中文摘要
大多数基于唤醒的脑机接口（BCIs）使用脑电图（EEG）来解码认知状态。但唤醒是一个自主神经过程。我们研究了外周生理信号是否能更好地解码任务相关唤醒。我们使用了来自一项困难边界回避飞行任务的公开数据集。在该任务中，参与者接受了基于EEG的BCI神经反馈、假反馈或无反馈。我们在心率、心率变异性（HRV）、呼吸、皮肤电活动和瞳孔直径上训练了一个多模态深度学习解码器。我们将其与仅使用EEG的解码器以及原始的滤波器组共空间模式（FBCSP）解码器进行了比较。所有分析均为离线分析。外周解码器的受试者内AUC为93.0%。仅使用EEG的解码器为85.2%，FBCSP解码器为79.8%。只有外周解码器显示出解码唤醒与表现之间的耶克斯-多德森倒U型关系。反馈条件并未改变唤醒轨迹。因此，BCI的益处可能来自于任务关键时刻的调节，而非持续的转变。我们从对照试验中计算了时变的最优唤醒轨迹。每次试验与该轨迹的偏差预测了表现（r = -0.28至-0.24，p < 0.01）。基线HRV和伽马功率改变每个受试者的唤醒-表现曲线。我们使用这些值将受试者分为唤醒敏感型和唤醒耐受型组。针对每组特定的控制带增加了试验分离度（Cohen's d从1.21到1.32，以及从0.85到1.10）。这些结果表明，外周信号在连续唤醒解码方面优于EEG。它们还为个体化闭环唤醒调节提供了规范。一项前瞻性研究必须测试该规范，例如使用个性化经皮迷走神经刺激。
英文摘要
Most arousal-based brain-computer interfaces (BCIs) use EEG to decode cognitive state. But arousal is an autonomic process. We examined if peripheral physiological signals give a better decode of task-related arousal. We used a public dataset from a difficult boundary-avoidance flight task. In this task, participants received EEG-based BCI neurofeedback, sham feedback, or no feedback. We trained a multimodal deep learning decoder on heart rate, heart rate variability (HRV), respiration, electrodermal activity, and pupil diameter. We compared it with an EEG-only decoder and with the original filter-bank common spatial pattern (FBCSP) decoder. All analyses were offline. The peripheral decoder had a within-subject AUC of 93.0%. The EEG-only decoder had 85.2% and the FBCSP decoder had 79.8%. Only the peripheral decoder showed the Yerkes-Dodson inverted-U relation between decoded arousal and performance. The feedback conditions did not change the arousal trajectories. Thus, the BCI benefit possibly comes from regulation at critical moments in the task, not from a continuous shift. We calculated a time-varying optimal arousal trajectory from control trials. Per-trial deviations from this trajectory predicted performance (r = -0.28 to -0.24, p < 0.01). Baseline HRV and gamma power changed the arousal-performance curve of each subject. We used these values to divide subjects into arousal-sensitive and arousal-tolerant groups. Control bands specific to each group increased trial separation (Cohen's d from 1.21 to 1.32 and from 0.85 to 1.10). These results show that peripheral signals are better than EEG for continuous arousal decoding. They also give a specification for individualized closed-loop arousal regulation. A prospective study must test this specification, for example with personalized transcutaneous vagus nerve stimulation.
