探索虚拟现实飞行模拟对脑电图神经模式和任务表现的影响

IF 2.1 3区 心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Cognitive Systems Research Pub Date : 2024-09-07 DOI:10.1016/j.cogsys.2024.101282
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引用次数: 0

摘要

脑电图(EEG)等神经生理学测量可用于深入了解飞行员在飞行训练期间的心理状态,并跟踪学习进度,从而优化每个人的训练体验。先前的研究表明,飞行模拟的保真度(2D 桌面与 3D VR)与任务需求相关的不同皮层活动有关。然而,模拟逼真度是否会影响飞行表现,以及这种影响是否能在与工作量相关的脑电图神经生理反应中观察到,目前仍是未知数。因此,本研究评估了基于脑电图的工作量和任务参与指数是否能预测不同模拟环境下飞行训练的成绩。我们对 53 名新手学员进行了受试者内设计实验,他们在两种条件(桌面与 VR)下执行了两项飞行任务(速度变化、中等转弯)。我们在整个实验过程中收集了脑电信号,并使用β-比率(βα+θ)对心理工作量进行量化。与桌面模拟相比,VR 条件下包括额叶和顶叶在内的所有脑叶的β-比率都有所增加。此外,我们还观察到模拟器环境对成绩的影响,因为 VR 与飞行成绩的提高有关。但是,我们没有发现贝塔比率与成绩之间存在关系的证据。我们的研究结果表明,在不同逼真度的训练环境中,大脑对任务的反应是不同的。然而,还需要对飞行训练的神经计量学进行更多的研究,以便开发脑机接口,通过实时提供个性化反馈来改进当前的飞行员训练方法。
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Exploring the impact of virtual reality flight simulations on EEG neural patterns and task performance

Neurophysiological measurements, such as electroencephalography (EEG), can be used to derive insight into pilots’ mental states during flight training and to track learning progress in order to optimize the training experience for each individual. Prior work has demonstrated that the level of fidelity of a flight simulation (2D Desktop vs. 3D VR) is associated with different cortical activity in relation to task demands. However, it remains unknown whether simulation fidelity affects flight performance, and whether this effect can be observed in EEG neurophysiological responses associated with workload. The current study therefore assessed whether an EEG-based index of workload and task engagement is predictive of performance during flight training in different simulation environments. We conducted a within-subject designed experiment with 53 novice participants who performed two flight tasks (speed change, medium turn) under two conditions (Desktop vs. VR). EEG signals were collected throughout the experiment to quantify mental workload using the beta-ratio (βα+θ). The VR condition showed increased beta-ratios in all lobes, including frontal and parietal areas, compared to the Desktop simulation. Additionally, we observed an effect of simulator environment on performance, as VR was associated with improved flight performance. However, we found no evidence of a relationship between the beta-ratio and performance. Our findings demonstrate that the brain responds differently to tasks in training environments of various levels of fidelity. However, more research into the neurometrics of flight training is needed in order to develop brain-computer interfaces that can enhance current pilot training methods by providing personalized feedback in real-time.

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来源期刊
Cognitive Systems Research
Cognitive Systems Research 工程技术-计算机:人工智能
CiteScore
9.40
自引率
5.10%
发文量
40
审稿时长
>12 weeks
期刊介绍: Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial. The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition. Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.
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