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在对即将发生的危险做出瞬间反应的意图中,大脑比手部动作更快:神经自适应自动化模拟以加速飞行姿态扰动后的恢复。

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.

作者信息

Callan Daniel E, Terzibas Cengiz, Cassel Daniel B, Sato Masa-Aki, Parasuraman Raja

机构信息

Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka UniversityOsaka, Japan; Multisensory Cognition and Computation Laboratory, Universal Communication Research Institute, National Institute of Information and Communications TechnologyKyoto, Japan.

Multisensory Cognition and Computation Laboratory, Universal Communication Research Institute, National Institute of Information and Communications Technology Kyoto, Japan.

出版信息

Front Hum Neurosci. 2016 Apr 27;10:187. doi: 10.3389/fnhum.2016.00187. eCollection 2016.

Abstract

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.

摘要

本研究的目标是通过使用脑机接口(BCI)解码感知运动意图,来测试神经自适应自动化在提高对危险事件反应速度方面的潜力。七名参与者进行了四次实验,同时用脑磁图测量大脑活动。前三场实验是简单的受限任务,在一种情况下,参与者要拉回控制杆以从姿态扰动中恢复,在另一种情况下则是被动观察扰动。第四场实验要求参与者在驾驶飞机穿越大峡谷并不断操纵以追踪下方河流的同时,从姿态扰动中恢复。对前两场实验使用独立成分分析来提取伪迹,并找到与扰动开始相关的事件相关成分。这两场实验用于训练一个解码器,以对参与者从扰动中恢复(运动意图)与只是被动观看扰动的试验进行分类。BCI解码器在同一项简单任务的第三场实验中进行了测试,结果发现它能够显著区分运动意图试验和被动观看试验(平均准确率为69.8%)。然后,同样的BCI解码器被用于测试复杂任务的第四场实验。BCI解码器能够显著区分扰动试验和无扰动试验(准确率为73.3%),并且显著节省了72.3毫秒的时间(BCI解码器的原始反应时间为425.0 - 352.7毫秒)。最佳受试者的BCI解码器模型在性能和时间节省方面都被证明可以推广到其他受试者。我们离线开环模拟的结果表明,基于BCI的神经自适应自动化有潜力在对飞行姿态的危险扰动做出反应时,比手动控制更快地解码运动意图,同时忽略与驾驶飞机相关的持续运动和视觉诱导活动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b86c/4846799/c758fd75df41/fnhum-10-00187-g0001.jpg

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