Tracking wakefulness as it fades: Micro-measures of alertness

Abstract

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.

Alertness fluctuations during task performance modulate cortical evoked responses to transcranial magnetic stimulation

“…Arguably, concurrent EEG recording should be the gold standard for assessment of single-trial alertness, as it provides quantifiable and reliable signatures of instantaneous brain-states, including alpha-and theta-derived Hori stages of sleep onset (Hori et al, 1994). As an alternative to the tedious manual scoring of Hori stages (Alertness Levels), an automated EEG method based on wakefulness and sleep grapho-elements is available for the detection of drowsiness from EEG data (Jagannathan et al, 2018). While methods that weight the dominance of EEG theta and alpha oscillations are suitable for eyes-closed paradigms (Hori et al, 1994;Jagannathan et al, 2018), such as resting state or phosphene studies (Bonnard et al, 2016;De Graaf et al, 2017), the power of higher EEG frequencies should be considered when assessing alertness during active eyesopen experiments (Eoh et al, 2005;Kaida et al, 2006;Zhao et al, 2012).…”

“…As an alternative to the tedious manual scoring of Hori stages (Alertness Levels), an automated EEG method based on wakefulness and sleep grapho-elements is available for the detection of drowsiness from EEG data (Jagannathan et al, 2018). While methods that weight the dominance of EEG theta and alpha oscillations are suitable for eyes-closed paradigms (Hori et al, 1994;Jagannathan et al, 2018), such as resting state or phosphene studies (Bonnard et al, 2016;De Graaf et al, 2017), the power of higher EEG frequencies should be considered when assessing alertness during active eyesopen experiments (Eoh et al, 2005;Kaida et al, 2006;Zhao et al, 2012). Finally, when EEG measurements are not available or feasible, TMS experiments could be carried out in short blocks of just a few minutes each (e.g., 3-5 min) and inter-block intervals could be used to assess instantaneous subjective sleepiness, for example by asking participants to undertake the 9-graded Karolinska Sleepiness Scale (Åkerstedt & Gillberg, 1990;Kaida et al, 2006).…”

Predicting lapses of attention with sleep-like slow waves

“…However, alpha oscillations are not an unambiguous marker of sleepiness, as they only transiently increase with sleepiness. Alpha power is low when participants are both fully alert or, on the contrary, approaching sleep onset 51,52 . Thus, the divergent results obtained regarding mind wandering and alpha oscillations could be explained by a shift in participants' baseline level of fatigue.…”

Connectivity dynamics from wakefulness to sleep

“…This suggests that dFNC windows exhibit larger variability during the N1 sleep stage compared to other sleep stages as observed in Supplemental Figure S2 leading to misclassification. This highlights the need to further identify additional sub-clusters in the N1 sleep stage and investigate if a fine grained EEG classification of this stage as proposed by Hori and colleagues (Hori et al, 1994) and recently demonstrated using EEG data (Jagannathan et al, 2018) can provide additional insights into large scale connectivity changes during the transition to sleep (Goupil and Bekinschtein, 2012).…”