Elsevier

NeuroImage

Volume 76, 1 August 2013, Pages 81-89
NeuroImage

EEG alpha activity is associated with individual differences in post-break improvement

https://doi.org/10.1016/j.neuroimage.2013.03.018Get rights and content

Highlights

  • We tested the effect of a break opportunity on sustained attention and EEG activity.

  • Individual differences in behavioral change in response to the break were observed.

  • Significant reductions in theta activity due to the break were observed.

  • Behavioral changes correlated with upper alpha (10–12 Hz) power during the break.

Abstract

Continuous EEG activity has been used increasingly as a marker of mental and cognitive states, with previous work linking particular neural patterns to conditions of arousal or fatigue. This approach is more commonly used to assess task-related, as opposed to resting-state activity. In this study, we recorded the EEG of 31 healthy individuals as they performed two sessions of a 65-minute auditory oddball task, one with, and one without a 5-minute break opportunity. Over the course of the task, reaction times, as well as EEG power in theta and lower alpha bands increased in both conditions, but did not differ significantly between conditions. Over the period of the break, delta and theta EEG activity decreased significantly in comparison with activity in the equivalent period in the no-break condition. Individual differences in response to the break were observed, with approximately half the subjects showing an improvement, and half showing a decline. These individual differences were correlated both with decreases in theta activity, as well as resting upper alpha power during the period of the break. Our results suggest that tonic EEG activity during resting periods is meaningfully related to behavioral change between individuals based on physiological or psychological factors that remain to be explored.

Introduction

Periods of extended mental workload are taxing on neural and cognitive systems, and often cause those systems to perform at increasingly suboptimal levels over time. The slope of this decline has been labeled the time-on-task (TOT) effect, and understanding its biological basis has been of interest to those seeking to optimize real-world human performance, especially in the nascent field of neuroergonomics (Parasuraman and Wilson, 2008). Under normal conditions, most individuals suffer from the effects of TOT, leaving them vulnerable to making potentially costly errors in field operations. An important question to address, therefore, is whether and how TOT deficits may be arrested or reversed through external intervention, and whether our growing understanding of the neurobiology of these declines may inform the strategies we use to do this.

Historically, fatigue and mental workload have been most commonly studied using electroencephalography (EEG), and these studies have thus far focused on changes associated with task-related activity. Broadly, it has been found that lengthening TOT leads to observable changes in both ongoing EEG activity and event-related potentials locked to task stimuli. Commonly, TOT induces increases in low-frequency theta power (Craig et al., 2012, Paus et al., 1997, Phipps-Nelson et al., 2011), particularly over frontal midline areas (Boksem et al., 2005), as well as increases in alpha power (Klimesch, 1999, Oken and Salinsky, 1992). Several studies also report a shift to greater high-frequency (beta) energy as task time increases (Craig et al., 2012, Foxe et al., 2012); this is thought to reflect compensatory efforts to maintain levels of performance as vigilance and arousal decrease. These EEG changes have been of significant interest to those hoping to find biomarkers of fatigue that may be useful in predicting on-the-job performance and preventing workplace error (Jap et al., 2009, Lal and Craig, 2001).

In comparison with the rich body of findings on the EEG correlates of TOT, changes associated with recovery from mental workload have not been extensively explored. The literature is particularly lacking in laboratory-controlled studies that test the effects of break periods in relieving fatigue and improving performance, with scattered examples showing small improvements with rest (Chen et al., 2010, Phipps-Nelson et al., 2011). It has been a matter of recent debate as to whether brief task switches may reduce TOT (Ariga and Lleras, 2011, Helton and Russell, 2012), indicating that there are aspects of the behavior itself that are still not well understood. Field studies in the ergonomics literature have also demonstrated some beneficial effects of rest breaks on productivity and safety, although the findings in these studies have also been mixed (Folkard and Tucker, 2003, Henning et al., 1997, Tucker et al., 2003). In sum, the relative paucity of data points to the need for more controlled studies relevant to this topic area.

The current work is also motivated by studies which have tested tonic EEG activity during reference or resting periods, and attempted to link this activity to behavior or cognitive function. For example, Klimesch et al. (2000) have found that greater upper alpha and lower theta power in a reference period was associated with better recall on a verbal memory task. To our knowledge, this approach has not been applied to paradigms studying sustained attention and TOT, or to intra-task periods when subjects are at rest. This is important as EEG activity shows different associations with different cognitive processes (Klimesch et al., 2007), necessitating work that elucidates the correlates of each of these functions individually.

In the current study, we administered a challenging sustained attention task to a group of young healthy participants, and investigated the effect of providing a rest opportunity on their performance and electrophysiological activity. We hypothesized that providing this rest would lead to significant improvements in performance (reaction time), and that EEG markers of attention (alpha) and fatigue (theta) would be associated with individual differences in improvement over this rest opportunity. The study was designed to augment our current knowledge of EEG and TOT by examining both decrements and improvements in performance, and to investigate the factors that contribute to effective brain recovery.

Section snippets

Material and methods

31 undergraduates (17 male; mean age = 22.8(3.0)) from the National University of Singapore were recruited for this study via word-of mouth and online advertising. Volunteers were pre-screened via a telephone interview to ensure they were right-handed using the Edinburgh Handedness inventory (Oldfield, 1971), and had no history of chronic physical or mental illnesses. Hearing ability was assessed using the Quick Hearing Check (Koike et al., 1994), and subjects were excluded if their score on this

Behavioral results

Mean accuracy for the auditory oddball task was 90.70% (6.89) for the break condition and 91.03% (9.58) for the no-break condition. The overall false alarm rate was low (1.12 (1.34) for break condition, 1.15 (2.17) for no-break condition). Reaction-time (RT) and accuracy data were averaged into 10-minute bins (Fig. 1). We analyzed the effect of time-on-task on both accuracy and RT using repeated-measures ANOVA with time (six 10-minute bins) and condition (break vs. no-break) as within subject

Discussion

The current study represents one of the first looks at the neural basis of recovery from fatigue during a mid-task break opportunity. In line with previous work, we found relative increases in theta and lower alpha from the first to last five minutes of the task. Furthermore, we found three key points of interest surrounding the break opportunity, which may be summarized as follows: 1) At the group level, behavioral changes ensuing from the five-minute break were small and non-significant, 2)

Conclusion

The current study characterizes the behavioral and electrophysiological responses of healthy adults to a break opportunity in the middle of a sustained attention test. Even in the absence of a group behavioral effect, EEG power in theta and alpha bands provided interesting information about individual differences in the behavioral change following a period of rest. These results may have implications for real-world operators who must maximize the use of break opportunities. Promising avenues of

Acknowledgments

Funding for this research was provided by NEUROEN grant R3940000059232. We acknowledge the assistance of Ong How Hwee in EEG data collection, and Tania Kong and Tse Chun-Yu for assistance in analysis and helpful comments.

Conflict of interest

The authors have no conflict of interest to declare.

References (52)

  • G. Gratton et al.

    A new method for off-line removal of ocular artifact

    Electroencephalogr. Clin. Neurophysiol.

    (1983)
  • S. Hanslmayr et al.

    Visual discrimination performance is related to decreased alpha amplitude but increased phase locking

    Neurosci. Lett.

    (2005)
  • W.S. Helton et al.

    Signal salience and the mindlessness theory of vigilance

    Acta Psychol. (Amst.)

    (2008)
  • K. Jann et al.

    Association of individual resting state EEG alpha frequency and cerebral blood flow

    NeuroImage

    (2010)
  • B.T. Jap et al.

    Using EEG spectral components to assess algorithms for detecting fatigue

    Expert Syst. Appl.

    (2009)
  • W. Klimesch

    EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis

    Brain Res. Rev.

    (1999)
  • W. Klimesch et al.

    Induced alpha band power changes in the human EEG and attention

    Neurosci. Lett.

    (1998)
  • W. Klimesch et al.

    Interindividual differences in alpha and theta power reflect memory performance

    Intelligence

    (1999)
  • W. Klimesch et al.

    EEG alpha oscillations: the inhibition-timing hypothesis

    Brain Res. Rev.

    (2007)
  • S.K. Lal et al.

    A critical review of the psychophysiology of driver fatigue

    Biol. Psychol.

    (2001)
  • J. Lim et al.

    Imaging brain fatigue from sustained mental workload: an ASL perfusion study of the time-on-task effect

    NeuroImage

    (2010)
  • S. Makeig et al.

    Tonic, phasic, and transient EEG correlates of auditory awareness in drowsiness

    Cogn. Brain Res.

    (1996)
  • R.C. Oldfield

    The assessment and analysis of handedness: the Edinburgh inventory

    Neuropsychologia

    (1971)
  • N. Pattyn et al.

    Psychophysiological investigation of vigilance decrement: boredom or cognitive fatigue?

    Physiol. Behav.

    (2008)
  • L. Torsvall et al.

    Sleepiness on the job: continuously measured EEG changes in train drivers

    Electroencephalogr. Clin. Neurophysiol.

    (1987)
  • P. Tucker et al.

    Rest breaks and accident risk

    Lancet

    (2003)
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