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首页> 美国卫生研究院文献>Frontiers in Human Neuroscience >The Brain Is Faster than the Hand in Split-Second Intentions to Respond to an Impending Hazard: A Simulation of Neuroadaptive Automation to Speed Recovery to Perturbation in Flight Attitude
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The Brain Is Faster than the Hand in Split-Second Intentions to Respond to an Impending Hazard: A Simulation of Neuroadaptive Automation to Speed Recovery to Perturbation in Flight Attitude

机译：在分秒必争的意图中大脑比手要快这是神经适应性自动化的仿真以加快恢复飞行姿态的速度。

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The goal of this research is to test the potential for neuroadaptive automation to improve response speed to a hazardous event by using a brain-computer interface (BCI) to decode perceptual-motor intention. Seven participants underwent four experimental sessions while measuring brain activity with magnetoencephalograpy. The first three sessions were of a simple constrained task in which the participant was to pull back on the control stick to recover from a perturbation in attitude in one condition and to passively observe the perturbation in the other condition. The fourth session consisted of having to recover from a perturbation in attitude while piloting the plane through the Grand Canyon constantly maneuvering to track over the river below. Independent component analysis was used on the first two sessions to extract artifacts and find an event related component associated with the onset of the perturbation. These two sessions were used to train a decoder to classify trials in which the participant recovered from the perturbation (motor intention) vs. just passively viewing the perturbation. The BCI-decoder was tested on the third session of the same simple task and found to be able to significantly distinguish motor intention trials from passive viewing trials (mean = 69.8%). The same BCI-decoder was then used to test the fourth session on the complex task. The BCI-decoder significantly classified perturbation from no perturbation trials (73.3%) with a significant time savings of 72.3 ms (Original response time of 425.0–352.7 ms for BCI-decoder). The BCI-decoder model of the best subject was shown to generalize for both performance and time savings to the other subjects. The results of our off-line open loop simulation demonstrate that BCI based neuroadaptive automation has the potential to decode motor intention faster than manual control in response to a hazardous perturbation in flight attitude while ignoring ongoing motor and visual induced activity related to piloting the airplane.
机译：这项研究的目的是通过使用脑机接口（BCI）来解码感知运动的意图，来测试神经自适应自动化在提高对危险事件的响应速度方面的潜力。七名参与者进行了四个实验会议，同时用磁脑电疗法测量了大脑的活动。前三节是一项简单的约束性任务，其中参与者要拉回控制杆以从一种情况下的姿势扰动中恢复过来，并在另一种情况下被动地观察扰动。第四届会议包括不得不从姿态的干扰中恢复过来，同时驾驶飞机穿越大峡谷，不断操纵以追踪下方的河流。在前两个会话中使用了独立成分分析来提取伪像，并找到与扰动发作相关的事件相关成分。这两个会话用于训练解码器以对试验进行分类，在该试验中，参与者从摄动（运动意图）中恢复了，而被动地观察了摄动。 BCI解码器在同一简单任务的第三部分进行了测试，发现能够显着区分运动意图试验和被动观察试验（平均值= 69.8％）。然后使用相同的BCI解码器测试复杂任务的第四次会话。 BCI解码器对无干扰的扰动进行了显着分类（73.3％），节省了72.3 ms的时间（BCI解码器的原始响应时间为425.0–352.7 ms）。结果表明，最佳学科的BCI解码器模型可以将性能和节省的时间都推广到其他学科。我们的离线开环仿真结果表明，基于BCI的神经自适应自动化具有比手动控制更快的解码马达意图的潜力，以应对飞行姿态中的危险扰动，而忽略了正在进行的与驾驶飞机有关的马达和视觉活动。

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