Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Neuroimage. 2018 Aug 1;176:138-151. doi: 10.1016/j.neuroimage.2018.04.046. Epub 2018 Apr 23.
A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments under eyes-closed settings. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method (micro-measures algorithm) based on frequency and sleep graphoelements, which are capable of detecting micro variations in alertness. The validity of the micro-measures algorithm was verified by training and testing using a dataset where participants are known to fall asleep. In addition, we tested generalisability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience under eyes-closed settings.
在心理学和生理学实验中，一个主要问题是困倦：尽管被指示保持警觉，但大约三分之一的参与者表现出警觉性降低。在一些非视觉实验中，参与者在整个任务过程中保持闭眼，从而促进了这种警觉性变化的发生。这些觉醒变化导致数据和感兴趣的测量值中存在系统性噪声。为了解决数据采集过程中普遍存在的这个问题，我们定义了标准和代码，允许研究人员在闭眼设置下检测和控制脑电图（EEG）实验中的警觉性变化。我们首先修订了一种用于检测和描述睡眠起始过程的视觉评分方法，并将其适应于检测警觉水平。此外，我们展示了阻止该方法实际应用的主要问题，并通过开发基于频率和睡眠图形元素的自动方法（微测度算法）克服了这些问题，该方法能够检测警觉性的微小变化。微测度算法的有效性通过使用已知入睡的参与者的数据集进行训练和测试进行了验证。此外，我们还通过在另一个数据集上进行独立验证来测试了泛化性。所开发的方法构成了一种独特的工具，可以评估在闭眼设置下进行认知神经科学中每一个使用 EEG 进行的实验中警觉水平的微小变化，并可回顾性或前瞻性地逐试验进行控制。
