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Time perception at different EEG-vigilance levels

机译:不同EEG警戒级别的时间感知

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Background Human time perception is influenced by various factors such as attention and drowsiness. Nevertheless, the impact of cerebral vigilance fluctuations on temporal perception has not been sufficiently explored. We assumed that the state of vigilance ascertained by electroencephalography (EEG) during the perception of a given auditory rhythm would influence its reproduction. Thus, we hypothesised that the re-tapping interval length and the accuracy of reproduction performance would vary depending on the state of vigilance determined by EEG. Methods 12 female and 9 male subjects ranging from 21 to 38 years (M = 25.52, SD = 3.75) participated in a test paradigm comprising a) a resting EEG for the determination of vigilance while an auditory rhythm was presented, b) a short activity of the proband to be sure of sufficient alertness, and c) a tapping task to reproduce the presented rhythm. Vigilance states of three consecutive 1-sec-EEG-segments of the resting EEG before the reproduction phase were classified using the Vigilance Algorithm Leipzig (VIGALL). Results and discussion Reproduction accuracy was more precise after high EEG-vigilance stages. Thus, the subjects’ mean deviation from the given rhythm was lower (t(17) = ?2.733, p Conclusion These findings support the hypothesis of a varying time perception and of speed alterations of the internal clock after different states of EEG-vigilance, which were automatically classified by VIGALL. Thus, alterations of cognitive processing may be assessable by specific EEG-patterns.
机译:背景技术人类时间知觉受诸如注意力和嗜睡之类的多种因素影响。然而,尚未充分探讨大脑警觉性波动对时间知觉的影响。我们假设在给定听觉节律的感知过程中通过脑电图(EEG)确定的警觉状态会影响其复制。因此,我们假设重新敲击间隔的长度和复制性能的准确性将取决于由EEG确定的警惕状态。方法12名女性和9名男性受试者,年龄在21至38岁之间(M = 25.52,SD = 3.75)参加了一个测试范例,其中包括:a)静息的脑电图,用于确定警觉性,同时呈现听觉节律; b)短暂活动先证者确保足够的警觉性; c)敲击任务以重现所呈现的节奏。使用莱比锡警戒算法(VIGALL)对繁殖阶段之前静息EEG的三个连续的1秒EEG段的警戒状态进行分类。结果与讨论高脑电图监测阶段后,生殖准确性更加精确。因此,受试者与给定节律的平均偏差较低(t(17)=?2.733,p)结论这些发现支持以下假设:在不同的脑电图警觉状态下,时间感知和内部时钟的速度会发生变化,这些都是由VIGALL自动分类的,因此,认知过程的改变可以通过特定的EEG模式进行评估。

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