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

NeuroImage 76 (2013) 81–89

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EEG alpha activity is associated with individual differences in post-break improvement Julian Lim a, b, c,⁎, Frances-Catherine Quevenco a, b, Kenneth Kwok a, b a b c

Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore Cognitive Science Lab, Temasek Laboratories, National University of Singapore, Singapore Department of Psychology, National University of Singapore, Singapore

a r t i c l e

i n f o

Article history: Accepted 9 March 2013 Available online 21 March 2013 Keywords: Sustained attention Time-on-task Alpha power EEG Rest break

a b s t r a c t 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. © 2013 Elsevier Inc. All rights reserved.

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 ⁎ Corresponding author at: 5A Engineering Drive 1, #09-02, 117411 Singapore. Fax: + 65 68726840. E-mail address: [email protected] (J. Lim). 1053-8119/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neuroimage.2013.03.018

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.

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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. 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 scale was 9 or greater (90th percentile). Subjects were also rejected if they could not reach criterion (at least 80% accuracy) in the practice sessions. Participants recruited into the study came into the laboratory for two separate experimental sessions, separated by approximately one week. Volunteers were required to obtain a minimum of 7 h of sleep for the 2 nights prior to the study, and were asked to refrain from using caffeine or alcohol for 6 h prior to coming into the lab. Study sessions took place in the afternoon between 1:30 and 5:30 pm to control for possible circadian confounds. All testing took place in the Cognitive Science Laboratory of Temasek Laboratories in Singapore. In each study session, participants performed a 65-minute auditory oddball task (see task characteristics below) while their brain activity was monitored using electroencephalographic (EEG) recording. All subjects went through a “break” and “no-break” condition, in counterbalanced order across the sessions. Subjects were given up to three one-minute practice sessions of the oddball task (with the proportion of targets increased in this practice to 50%) to ensure they were able to perform at a high level of accuracy. Subjects who were unable to reach criterion in this time were excluded from further testing. Participants were compensated S$40 for their time, or a pro-rated amount if they did not complete the entire experiment.

inter-stimulus interval was kept constant at 2 s. Non-targets were 996 Hz tones presented for 1 s. Targets were identical to non-targets except for a 50% drop in amplitude in the final 50 ms of the stimulus. Subjects were required to respond with a button press to targets, and to ignore non-target stimuli. We emphasized that both accuracy and speed were important to good task performance. In the “break” version of the task, subjects were given a 5-minute rest opportunity at the 30-minute mark of the task, where they were instructed to maintain their gaze at a fixation cross in the middle of the screen and remain awake but relaxed. At the start of this break period, subjects viewed a brief instruction screen informing them that a break period had commenced, and that they should relax with their eyes maintained on the fixation cross. In the “no-break” version, subjects performed the auditory oddball task for 65 min continuously. Regardless of condition, participants were informed at the beginning of each block that they “may or may not” receive a 5-minute break in the middle of the task, so as to minimize any possible confounds of expectation.

EEG recording and preprocessing EEG was recorded with Ag/AgCl electrodes at 64 scalp locations based on the standard 10-20 system using a high-density DC amplifier (ASA-Lab, Advanced Neuro Technology, The Netherlands). Active electrodes were referenced online to an electrode over the left mastoid. An average mastoid reference was computed offline and subtracted from all scalp channels. To obtain bipolar vertical and horizontal EOG recordings, electrodes were attached vertically above and below the left eye, and at the outer canthi of each eye. Electrode impedance was brought to less than 10 kΩ before data collection commenced. EEG and EOG data were recorded from DC to 70 Hz and sampled online at 250 Hz. Offline analysis of the EEG data was performed to test our experimental hypotheses. EEG data were bandpass filtered between 0.1 and 30 Hz, and segmented into 50%-overlapping 7.5 s epochs within each period of interest. Ocular artifacts were corrected based on the method of Gratton et al. (1983), and epochs with voltage changes greater than 200 μv across the recording window were rejected. The remaining data were submitted to a fast Fourier transform, and power spectra were computed from 1 to 30 Hz during five five-minute periods in the break condition (in the first and last five minutes of the task, and during, directly before, and directly after the rest period), and four five-minute periods in the no-break condition (first and last five minutes of the task, minutes 25–30 and 35–40 of the task). Spectral power was calculated in five EEG bands: delta (1–4 Hz), theta (4– 8 Hz), lower alpha (8–10 Hz), upper alpha (10–12 Hz) and beta (12– 30 Hz), and average power in these bands was obtained over four bilateral clusters (frontal, central, parietal, occipital). Spectral values during the break period were taken from average power in the three electrodes with the highest values in each respective band. Electrodes used during task and rest periods are reported in Tables 1 and 2. Neurophysiological data were analyzed in EEGLab (Delorme and Makeig, 2004) and using in-house scripts written in MATLAB R2011B, and statistical analysis was performed using SPSS for Windows, Version 17.0.

Task characteristics The auditory oddball paradigm used in the current experiment was modeled on a design reported in Paus et al. (1997). Stimuli were prepared using Audacity (audacity.sourceforge.net) and presented using E-Prime version 1.0 through Etymotic ER4 microPro earphones (Etymotic Resrac Inc., IL, USA). These consisted of common (95%) non-targets and rare (5%) targets arranged in a pseudorandom order such that at least one target occurred during each minute of the paradigm. Targets were never presented consecutively, and the

Table 1 Electrode clusters used for analysis of EEG during task performance. Frequency

Peak electrodes

Frontal Central Parietal Occipital

F1-6, AF3-4, FP1-2, Fz, FPz FC1-6, C1-6, CP1-6, FCz, Cz P1-6, PO3-6, Pz, POz O1-2, Oz

J. Lim et al. / NeuroImage 76 (2013) 81–89 Table 2 Peak electrodes at the mid-frequency of each spectral band during the break period. Frequency

Peak electrodes

Delta (2.5 Hz) Theta (6 Hz) Lower alpha (9 Hz) Upper alpha (11 Hz) Beta (20 Hz)

AF8, F8, Fp2 F2, Fz, FCz PO4, PO7, PO8 PO6, POz, PO8 T7, FC5, C5

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and electrode cluster. We corrected for multiple comparisons using false discovery rate (FDR) (Benjamini and Hochberg, 1995) and considered results significant at a threshold of q (FDR corrected) b .05. Results are reported in Table 3. No significant effects of condition, or time by condition interactions were found. However, the main effect of time was significant in frontal and central theta, and frontal and central low alpha (all increases). No differences were found in parietal and occipital electrode clusters. Spectral changes from pre- to post-break

Results 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 factors. We found a significant main effect of time on both reaction times (F5,25 = 9.92, p b .001) and accuracy (F5,25 = 4.73, p b .01), but no effect of condition (RT: F1,29 = 0.63, p = n.s.; accuracy F1,29 = 1.44, p = n.s.) and no time by condition interaction (RT: F5,25 = 0.51, p = n.s.; accuracy: F5,25 = 0.81, p = n.s.). Inspection of the data indicated that reaction times improved from before to after the break (mean change = 1.5%); however, we observed that almost half our study sample actually showed slower reaction times in the period directly following the break. Finally, we performed a repeated-measures ANOVA to assess the direct change across the break with time (minutes 20–30 vs. minutes 35–45) and condition as factors and, contrary to our hypothesis, found no significant effects in this analysis either. Reaction time measures have previously been found to have robust associations with genetic and physiological markers of TOT (Lim et al., 2010, 2012). Furthermore, inspection of Fig. 1 indicates that, unlike reaction times, accuracy did not decline in a linear fashion from the beginning to the end of the task. As a result, we chose to use RT measures in the current analysis as our primary behavioral outcome. Spectral changes from beginning to end of task EEG data from the first and final five minutes of each condition were subjected to a 2 × 2 repeated-measures ANOVA in each band

We tested for differences in pre- to post-break EEG power through a two-step approach. First, we conducted repeated-measures ANOVA in each power band and electrode cluster using time (minutes 25–30 vs. minutes 35–40) and condition (break vs. no break) as factors. We considered the effect of the break to be significant if we found a time × condition interaction at the level of q (FDR corrected) = .05 (Table 4). Eight band/cluster combinations survived this first stage analysis. Post-hoc paired-sample t-tests were then conducted in this subsample between pre- and post-break EEG power to test the direct effect of the break. EEG power decreased significantly in five of these eight clusters; no significant changes were observed in delta, upper alpha and beta in frontal areas (Table 5). At a more liberal threshold (performing all comparisons regardless of whether a difference was found in the first-stage analysis), we also found decreases in theta power in parietal (t29 = − 2.15, p b .05) and occipital clusters (t29 = − 2.20, p b .05) over the course of the break (Fig. 2). As frontal theta is a known marker of fatigue and showed the largest change over this period, we calculated the percent change in theta power for each subject and correlated this change score with percent change in RT from the pre- to post-break period. We found a significant association between these two variables (r = .52, p b .01; Fig. 3). We repeated this analysis with global theta power reduction (averaged over all channels) and found that this was also positively correlated with reaction time change (r = .47, p b .01). Correlations between resting EEG power and reaction time changes EEG spectral analysis was conducted for the five-minute period of the break. To visualize the differences between responders and non-responders to the break, we divided our sample into two groups, those with RT changes of less than zero (N = 18; responders), and greater than zero (N = 13; non-responders) over the break. Scalp maps of activity were plotted for the midpoint frequency values of each band. This analysis revealed a slightly different pattern of scalp

Fig. 1. (a) Accuracy, and (b) reaction time change across the 65-minute oddball task. Average accuracy, reaction times and standard errors are plotted for each 10-minute bin of the task. A significant effect of time-on-task but no significant effect of condition was observed in both sets of data; however, only reaction times show a classic, linear pattern of decline.

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Table 3 F-and q-values for repeated-measures ANOVA with time (first vs last five minutes) and condition (break vs. no break) as factors. Results reported are corrected using false discovery rate. Delta

Theta

F1,30 Frontal

Time Condition Time × condition Time Condition Time × condition Time Condition Time × condition Time Condition Time × condition

Central

Parietal

Occipital

q

4.87 0.08 1.68 3.94 7.21 7.47 0.16 4.75 0.83 0.93 3.38 1.35

.12 .78 .20 .16 .12 .10 .69 .16 .98 .40 .16 .46

Lower alpha

F1,30

q

9.69 0.24 0.99 11.26 3.66 2.68 2.71 2.52 0.00 0.86 2.36 0.75

.02⁎ .63 .33 .02⁎ .18 .25 .22 .25 .98 .40 .25 .55

Upper alpha

Beta

F1,30

q

F1,30

q

F1,30

q

10.6 0.88 8.26 11.27 1.93 1.39 0.86 3.69 0.58 0.97 4.14 4.68

.02⁎

1.81 1.26 4.95 1.52 1.23 1.50 1.06 9.55 0.19 2.83 5.46 0.54

.19 .39 .20 .23 .28 .23 .40 .08 .73 .22 .16 .55

1.82 .74 .68 6.13 2.60 1.54 0.37 0.53 0.16 4.52 0.17 1.44

.32 .40 .55 .08 .25 .46 .58 .56 .73 .07 .72 .46

.47 .10 .02⁎ .29 .46 .40 .16 .55 .40 .16 .20

⁎ Significant at q b .05.

activity in these two groups (Fig. 4). Notably, upper alpha had a more posterior concentration in the non-responders than the responders. Beta activity also presented more strongly in both anterior and posterior regions in this group. We conducted further analysis to determine if EEG power during the break was associated with individual differences in reaction time change. To avoid the problem of multiple comparisons, we extracted average power in the peak electrodes of each band, and correlated just this variable with percent change in RT to determine if it was associated with recovery. One data point (in upper alpha) was removed from this analysis as it was greater than 3 SD from the mean; we note, however, that including this data point actually strengthens the reported association. We found a significant correlation between reaction time changes and upper alpha (r = .53, p b .01; Fig. 5), but no associations with delta (r = .21, p = N.S.), theta (r = .26, p = N.S.), lower alpha (r = .29, p = N.S.) or beta power (r = .22, p = N.S.). We next investigated whether upper alpha power during the break was associated with the reductions in theta power observed across it. Average theta power reduction was significantly correlated with upper alpha power (r = .44, p b .05). Furthermore, upper alpha acted as a mediator in the association between theta power reduction and reaction time change over the break (i.e. the partial correlation between these variables was non-significant after controlling for upper alpha power).

Tests for order effects We tested for order effects to rule out the possibility that differences may have been due to subjects anticipating (or not anticipating) a break period. No differences were found in time-on-task variables in either condition, for EEG power in any band over the break, or for EEG power changes in any band over the break. Table 4 F- and q-values for 2 × 2 repeated-measures ANOVA using time (minutes 20–30 and 35-of the task) and condition (break vs no-break) as factors. Results reported are corrected using false discovery rate. Delta

Frontal Central Parietal Occipital

Theta

F1,30

q

12.70 16.81 6.64 9.61

.005⁎ .003⁎ .02⁎ .02⁎

⁎ Significant at q b .05.

F1,30 20.77 14.67 2.30 1.07

Lower alpha q

F1,30 .002⁎ .005⁎

0.25 0.43

3.4 4.42 .002 .31

Upper alpha q .13 .09 .96 .64

Beta

F1,30

q

F1,30

q

9.12 0.84 0.84 0.38

.02⁎

6.66 5.12 .66 2.04

.04⁎ .07 .52 .26

.81 .48 .64

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) Decreases in EEG power were nevertheless observed from pre- to post-break spectra in theta and delta bands over this period. 3) Upper alpha power during the break correlated with both individual differences in reaction time change and theta power change, and mediated the relationship between these variables. These findings are discussed in more detail below. Behavioral effects of the break opportunity Providing subjects with a break opportunity midway through the oddball task produced neither a consistent, nor a long-lasting behavioral effect. Over the break, only 18/31 of our subjects showed an improvement in reaction times, and the average change in the sample was not significant from the pre- to post-break period. Furthermore, there was no significant difference in overall TOT slope between the break and no-break conditions. This result is consistent with recent findings suggesting that changing the goal state during a vigilance task may not be sufficient to slow the vigilance decrement (Helton and Russell, 2012), and that substantial recovery may be needed to replenish the neural resources expended during demanding mental activity. In a study of driver fatigue and TOT, Phipps-Nelson et al. (2011) found that a one-hour break (between two-hour driving periods) was sufficient to produce a significant behavioral improvement. Even then however, the changes observed were transient, and

Table 5 t-Tests comparing pre- and post-break EEG power for band/cluster combinations that survived first-round analysis as reported in Table 4. Band/cluster

t-Value (df = 30)

p-Value

Frontal delta Central delta Parietal delta Occipital delta Frontal theta Central theta Frontal upper alpha Frontal beta

−2.02 −2.25 −3.04 −2.95 −3.26 −2.74 −1.97 −1.14

.052 b.05⁎ b.01⁎ b.01⁎ b.01⁎ b.01⁎

⁎ Significant at p b .05.

.06 .26

J. Lim et al. / NeuroImage 76 (2013) 81–89

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Fig. 2. Means (±corrected SEM) for power in each band and electrode cluster before (black bars) and after (gray bars) the 5-minute break. D = delta, T = theta, LA = low alpha, UA = upper alpha, B = beta. Error bars are corrected for within-subjects comparisons by the method of Cousineau (2005). *Represents p b .05; **represents p b .01.

performance continued to decline both between and within driving sessions. Our data also correspond well to real-world ergonomics results; for example, Tucker et al. (2003) reported that rest breaks given to car assembly workers reduce the risk of an accident, but that this reduction is restricted to a short period directly after the break opportunity. Two competing theories have been proposed to explain TOT effects: resource theory (Helton and Russell, 2011; Helton and Warm, 2008), which posits that TOT declines are due to overload, and a putative depletion of neural resources, and motivation theory (Smallwood and Schooler, 2006; Pattyn et al., 2008) which attributes such declines to underload, boredom, or lack of motivation). While

we cannot completely rule out boredom and motivation as factors inducing TOT in these data, there are clearer indications that resource depletion and replenishment are taking place. The relatively long duration of recovery needed to ameliorate TOT declines in studies involving mental breaks points to the depletion of a physical substrate that cannot be rapidly replenished, perhaps due to overtaxation of a metabolic or neurotransmitter system (Lim et al., 2012). In contrast, motivation levels and task-unrelated thoughts tend to fluctuate on a finer time scale. Thus, in an underload situation, we would expect performance to return (at least for a period) to baseline levels following a break (Ariga and Lleras, 2011), a pattern we do not observe in the current data.

Fig. 3. Change in frontal theta power correlates with percent reaction time change over the break.

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Fig. 4. 64-Channel continuous EEG data and corresponding scalp maps for responders and non-responders to the break. Scalp maps are plotted for the midpoint of each of the five frequency bands of interest (delta, theta, lower alpha, upper alpha, beta). Non-responders have a more posterior upper alpha concentration, and a different distribution of beta power across the scalp.

It is conceivable that controlling and constraining the kind of mental activity a subject engages in may moderate this difference; this possibility is discussed in the section “Upper alpha power during the break correlates with reaction time change”.

EEG changes over the task period Increases in theta and lower alpha power over frontal and central electrodes were found when comparing EEG spectra at the beginning

Fig. 5. Upper alpha power during the break correlates with reaction time change across this period.

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and the end of the task. We failed to find an interaction effect between time and condition during this period, indicating that the EEG changes occurring over the period of the break (see section “EEG changes over the break period”) did not have an enduring difference on neural activity. This finding is consistent with the absence of an effect in the behavioral data. Theta activity is generally found to increase in tandem with time-on-task (Bonnefond et al., 2010) and mental fatigue (Craig et al., 2012; Tanaka et al., 2012), although numerous studies have noted that power in this band also increases as a function of time awake, and may be under homeostatic control (Cajochen et al., 1995; Finelli et al., 2000). This increase is often accompanied by behavioral signs of lower cognitive capacity and control, such as slowed reaction times and attentional lapses (Huang et al., 2001; Makeig and Jung, 1996; Peiris et al., 2006). Similarly, alpha activity in the 8–10 Hz range is generally related to attentional demands (Klimesch et al., 1998), and in this dataset may reflect greater effort to sustain attention as task length increases. Our data thus replicate this ubiquitous and robust finding in the literature. EEG changes over the break period Comparing the five minute period pre- to post-break, we observed significant decreases in theta across all electrode clusters, with the strongest changes in frontal electrodes. We infer from these reductions in theta that at least some subjects experienced a measure of recovery from their fatigue, possibly leading to increases in cortical arousal. Further evidence for this comes from the observation that theta power reduction over frontal electrodes from the pre- to post-break period was significantly correlated with behavioral improvement. Interestingly, the regression line plotted between these variables passes fairly near the origin point (Fig. 3), indicating that the sign of the change may be meaningful as well (i.e. relative increases in theta may always correspond to relative increases in reaction time). Decreases were also observed in delta power in all but the frontal electrode clusters, although we did not observe increases in this band from the beginning to the end of the 65-minute task. Increases in delta owing to fatigue have been reported in a number of studies (Caldwell et al., 2003; Tanaka et al., 1997; Torsvall and Akerstedt, 1987), with slight (non-significant) decreases observed in this power band observed over break periods (Phipps-Nelson et al., 2011). The interpretation of these results remains unclear, and such findings may be due to changes in eyeblink rate, (although we note that eyeblink artifacts were removed in the current dataset). In the absence of other evidence, it is not apparent that this change is psychologically meaningful, particularly as delta waves do not typically manifest in the waking EEG of healthy adults. Upper alpha power during the break correlates with reaction time change Among the five bands tested, upper alpha power was the only band to show an association with reaction time change over the break; subjects with lower levels of resting upper alpha in posterior electrodes during the break period tended to experience more of a beneficial effect. Importantly, this variable also mediated the correlation between reductions in theta power and reaction time, indicating that upper alpha power governs the relationship that leads to this change. The relationship between alpha synchronization and task performance is relatively well understood. Critically, the direction of this relationship depends both on task type, and whether EEG measurements are taken during task performance, or during a resting (or reference) period. During task performance, high levels of tonic alpha are generally thought to reflect attentional demands, particularly in tasks that require inhibition and internal, top–down control (Cooper et al., 2003; Ray and Cole, 1985; von Stein and Sarnthein, 2000; for a review, see Klimesch et

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al., 2007). However, the effect of alpha synchronization on behavior during pre-task or pre-stimulus reference periods is task-specific. In tests of higher cognitive function (e.g. working memory or mental rotation), large power in a pre-stimulus reference interval is typically associated with good performance (Doppelmayr et al., 1998). Tonic levels of baseline alpha (during periods of eyes opened and eyes closed) have also been positively related to performance on a verbal memory task (Klimesch et al., 2000), and increasing upper alpha using neurofeedback has been shown to improve performance on a subsequent cognitive task (Hanslmayr et al., 2005b). In contrast, in perceptual or simple attentional tasks, small reference alpha power has been found to promote detection of phasic stimuli. For example, Ergenoglu et al. (2004) studied prestimulus EEG power (1 s) in a stimulus detection task and found that relative alpha power was significantly lower for detected than undetected stimuli. Hanslmayr et al. (2005a) report similar findings in a test of visual discrimination. However, a test of sustained attention that engaged processes beyond signal detection (i.e. a go no-go task; Dockree et al., 2007) have reported effects in the same direction as those in the paragraph above, suggesting that low reference alpha predicts only good performance on simple perceptual tasks. Furthermore, we are not aware of previous studies that have investigated alpha power over longer time-scale resting (as opposed to reference) periods. In line with the findings summarized above, subjects with relatively more upper alpha synchronization during the break performed relatively worse in the subsequent 10-minute block. These data suggest that processes occurring throughout the duration of the break, and not just when subjects re-engage with the task, are responsible for the observed behavioral changes. We speculate that subjects manifesting this pattern persisted in maintaining strong internal control of attention once there was an absence of external stimuli (Klimesch et al., 2007), and that this hindered effective recovery, particularly as they were in a pre-fatigued state. This shift to internal attention may also place such subjects in a lower state of cognitive preparedness prior to resumption of the task. It is possible that trait-like psychological mechanisms may underlie these individual differences; for example, subjects who tend to be more anxious or ruminative may find it harder to shift from an inwardly-attentive to a more generally relaxed state (Andersen et al., 2009). Alternatively, subjects who are more introspective may also manifest higher alpha during periods of rest. Our present results are consistent with data showing that tonic levels of resting brain activity in regions associated with sustained attention are meaningfully related to attentional capacity. For example, cerebral perfusion in the thalamus and the middle frontal gyrus during a resting baseline period were found to be correlated with subsequent TOT declines (Lim et al., 2010), and polymorphisms in dopaminergic alleles related to attention also show associations with this variable (Lim et al., 2012). Moreover, recent data have drawn connections between resting-state cerebral blood flow and interindividual differences in peak EEG alpha frequency, showing correlations particularly in brain regions associated with arousal and attention (Jann et al., 2010). In summary, studies which focus solely on brain activity during periods of task performance and ignore activity during baseline periods may not fully capture the causes of any observable TOT effects. Additionally, the current data tease at the possibility that subjects may be able to control the replenishment of depleted neural resources during these baseline periods, either by constraining the form of mental activity they engage in during this period, or through the use of training and neurofeedback techniques (Besserve et al., 2007). Limitations A primary limitation in the current work is that the correlation between EEG activity during the rest period and behavioral improvement

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does not necessarily relate to break quality per se, but could reflect individual differences in EEG detectable any time a subject is not actively engaged in a cognitive task. Indeed, substantial individual differences in EEG alpha power have been observed even in a normal resting state (Klimesch et al., 2000). We reiterate, therefore, that the interpretation of our results in the section “Upper alpha power during the break correlates with reaction time change” is currently only speculative. Future work in this area could extend the current findings to isolate the psychological processes underlying the observed EEG differences, for example by directly manipulating break quality, or by comparing rest-related activity with existing baseline differences. A second limitation is that the “break” afforded to the subjects in the current paradigm was not ecologically valid; given free choice, participants would probably have opted to stretch their legs, check their social media accounts and so forth. It is thus possible that allowing subjects to take a “real” break containing unconstrained activity may lead to greater recuperative effects. We opted for the current design in order to retain more experimental control over the results, as our hypothesis was relatively novel. However, future studies could address the concern of ecological validity through the use of novel designs and mobile EEG recording in real-world situations. 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 work stemming from this result may include investigating how to make the effects of a break more long-lasting, possibly through the use of neurofeedback techniques during both rest periods and periods of task performance. 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 Andersen, S.B., Moore, R.A., Venables, L., Corr, P.J., 2009. Electrophysiological correlates of anxious rumination. Int. J. Psychophysiol. 71, 156–169. Ariga, A., Lleras, A., 2011. Brief and rare mental “breaks” keep you focused: deactivation and reactivation of task goals preempt vigilance decrements. Cognition 118, 439–443. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300. Besserve, M., Jerbi, K., Laurent, F., Baillet, S., Martinerie, J., Garnero, L., 2007. Classification methods for ongoing EEG and MEG signals. Biol. Res. 40, 415–437. Boksem, M.A., Meijman, T.F., Lorist, M.M., 2005. Effects of mental fatigue on attention: an ERP study. Cogn. Brain Res. 25, 107–116. Bonnefond, A., Doignon-Camus, N., Touzalin-Chretien, P., Dufour, A., 2010. Vigilance and intrinsic maintenance of alert state: an ERP study. Behav. Brain Res. 211, 185–190. Cajochen, C., Brunner, D.P., Kraeuchi, K., Graw, P., Wirz-Justice, A., 1995. Power density in theta/alpha frequencies of the waking EEG progressively increases during sustained wakefulness. Sleep 18, 890–894. Caldwell, J.A., Prazinko, B., Caldwell, J.L., 2003. Body posture affects electroencephalographic activity and psychomotor vigilance task performance in sleep-deprived subjects. Clin. Neurophysiol. 114, 23–31.

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