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Evaluating Mental Load During Realistic Driving Simulations by Means of Round the Ear Electrodes

机译:通过绕耳式电极评估现实驾驶模拟过程中的心理负荷

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摘要

Film based round the ear electrodes (cEEGrids) provide both, the accessibility of unobtrusive mobile EEG as well as a rapid EEG application in stationary settings when extended measurements are not possible. In a large-scale evaluation of driving abilities of older adults (N > 350) in a realistic driving simulation, we evaluated to what extent mental demands can be measured using cEEGrids in a completely unrestricted environment. For a first frequency-based analysis, the driving scenario was subdivided into different street segments with respect to their task loads (low, medium, high) that was a priori rated by an expert. Theta activity increased with task load but no change in Alpha power was found. Effects gained clarity after removing pink noise effects, that were potentially high in this data set due to motion artifacts. Theta fraction increased with task load and Alpha fraction decreased. We mapped this effect to specific street segments by applying a track-frequency analysis. Whilst participants drove with constant speed and without high steering wheel activity, Alpha was high and theta low. The reverse was the case in sections that required either high activity or increased attentional allocation to the driving context. When calculating mental demands for different street segments based on EEG, this measure is highly significant correlated with the experts’ rating of task load. Deviances can be explained by specific features within the segments. Thus, modulations in spectral power of the EEG were validly reflected in the cEEGrids data. All findings were in line with the prominent literature in the field. The results clearly demonstrate the usability of this low-density EEG method for application in real-world settings where an increase in ecological validity might outweigh the loss of certain aspects of internal validity.
机译:当无法进行扩展测量时,基于薄膜的耳电极(cEEGrids)既可提供通畅的移动EEG的可访问性,又可在固定环境中提供快速的EEG应用。在现实驾驶模拟中对老年人(N> 350)的驾驶能力进行大规模评估时,我们评估了在完全不受限制的环境中使用cEEGrids可以测量到何种程度的心理需求。对于第一个基于频率的分析,将驾驶场景根据其任务负载(低,中,高)细分为不同的街道段,这是专家事先确定的。 Theta活动随任务负载而增加,但未发现Alpha功率发生变化。消除粉红噪声效果后,效果变得更加清晰,由于运动伪影,此数据集中的潜在效果可能很高。 Theta分数随任务负荷增加而Alpha分数减少。我们通过应用跟踪频率分析将此效果映射到特定的街道段。尽管参与者以恒定的速度驾驶并且没有很高的方向盘活动性,但是Alpha却很高而θ很低。相反,在需要高强度活动或增加对驾驶环境的注意力分配的部分中,情况恰恰相反。在基于脑电图计算不同街道段的心理需求时,此度量与专家对任务负荷的评估高度相关。偏差可以通过细分中的特定功能来解释。因此,EEG频谱功率的调制有效地反映在cEEGrids数据中。所有发现均与该领域的著名文献一致。结果清楚地证明了这种低密度脑电图方法在现实环境中应用的可用性,在这种环境中,生态有效性的增加可能超过内部有效性某些方面的损失。

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