Regional brain wave activity changes associated with fatigue

Investigating Virtual Reality for Alleviating Human-Computer Interaction Fatigue: A Multimodal Assessment and Comparison with Flat Video

Studies have shown that prolonged Human-Computer Interaction (HCI) fatigue can increase the risk of mental illness and lead to a higher probability of errors and accidents during operations. Virtual Reality (VR) technology can simultaneously stimulate multiple senses such as visual, auditory, and tactile, providing an immersive experience that enhances cognition and understanding. Therefore, this study collects multimodal data to develop evaluation methods for HCI fatigue and further explores the fatigue-relieving effects of VR technology by comparing it with flat video. Using a modular design, electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) data in the resting, fatigue-induced, and recovery states, eye movement data in the resting and fatigue-induced states, as well as subjective scale results after each state were collected from the participants. Preprocessing and statistical analysis are performed through data flow architecture. After fatigue induction, it was found that the degree of activation of brain areas, especially the Theta band of prefrontal cortex, occurred significantly higher, the effective connectivity in the Alpha and Theta bands occurred significantly lower, the subjects' pupil diameters decreased, the blink frequency increased, and subjective questionnaire scores increased, which verified the validity of the multimodal data for assessing HCI fatigue. Analyzing fatigue relief through subgroups, it was found that when using the natural grassland scene with soothing music, both flat video and VR had the ability to alleviate fatigue, which was manifested as a significant decrease in the Alpha band in the LPFC brain area and a decrease in the questionnaire score. Moreover, during the recovery state, it was found that compared to the video group, the VR group had significantly higher activation in the Alpha and Theta bands of the prefrontal cortex, while the video group had significantly higher effective connectivity than the VR group in the Alpha band. This study delved deeply into the multidimensional characterization of fatigue and investigated new scenarios for the use of VR, which can help to promote the use of VR and can be migrated to scenarios that require fatigue management and productivity enhancement.

Cortical Effects of Dopamine Replacement Account for Clinical Response Variability in Parkinson's Disease

BACKGROUND

Individual variability in clinical response to dopamine replacement therapy (DRT) is a key barrier to efficacious treatment for patients with Parkinson's disease (PD). A better understanding of the neurobiological sources of such interindividual differences is necessary to personalize DRT prescribing, inform future clinical interventions, and motivate translational research.

OBJECTIVE

One potential source of this variability is an unintended secondary activation of extra-nigrostriatal dopamine systems by DRT, particularly in the neocortex. Our goal was to determine the clinical effects of cortical dopamine system activation by DRT in patients with PD.

METHODS

We used pharmaco-magnetoencephalography data collected from patients with PD (NPD = 17, NHC = 20) before and after DRT to map their cortical neurophysiological responses to dopaminergic pharmacotherapy. By combining these DRT response maps with normative atlases of cortical dopamine system densities, we linked the variable enhancement of rhythmic cortical activity by DRT to dopamine-rich cortical regions and determined its clinical relevance.

RESULTS

We found beta-rhythmic responses to DRT in dopamine-rich regions of the cortex that are expressed variably across individuals. Importantly, patients who exhibited a larger dopaminergic beta cortical enhancement showed a smaller clinical improvement from DRT, indicating a potential source of individual variability in medication response for patients with PD.

CONCLUSIONS

We conclude that these findings inform our understanding of the dopaminergic basis of neurophysiological variability often seen in patients with PD, and indicate that our methodological approach may be useful for data-driven contextualization of medication effects on cortical neurophysiology in future research and clinical applications. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Mind the road: attention related neuromarkers during automated and manual simulated driving captured with a new mobile EEG sensor system

Background

Decline in vigilance due to fatigue is a common concern in traffic safety. Partially automated driving (PAD) systems can aid driving but decrease the driver's vigilance over time, due to reduced task engagement. Mobile EEG solutions can obtain neural information while operating a vehicle. The purpose of this study was to investigate how the behavior and brain activity associated with vigilance (i.e., alpha, beta and theta power) differs between PAD and manual driving, as well as changes over time, and how these effects can be detected using two different EEG systems.

Methods

Twenty-eight participants performed two 1-h simulated driving tasks, while wearing both a standard 24 channel EEG cap and a newly developed, unobtrusive and easy to apply 10 channel mobile EEG sensor-grid system. One scenario required manual control of the vehicle (manual) while the other required only monitoring the vehicle (PAD). Additionally, lane deviation, percentage eye-closure (PERCLOS) and subjective ratings of workload, fatigue and stress were obtained.

Results

Alpha, beta and theta power of the EEG as well as PERCLOS were higher in the PAD condition and increased over time in both conditions. The same spectral EEG effects were evident in both EEG systems. Lane deviation as an index of driving performance in the manual driving condition increased over time.

Conclusion

These effects indicate significant increases in fatigue and vigilance decrement over time while driving, and overall higher levels of fatigue and vigilance decrement associated with PAD. The EEG measures revealed significant effects earlier than the behavioral measures, demonstrating that EEG might allow faster detection of decreased vigilance than behavioral driving measures. This new, mobile EEG-grid system could be used to evaluate and improve driver monitoring systems in the field or even be used in the future as additional sensor to inform drivers of critical changes in their level of vigilance. In addition to driving, further areas of application for this EEG-sensor grid are safety critical work environments where vigilance monitoring is pivotal.

Adaptogens in Long-Lasting Brain Fatigue: An Insight from Systems Biology and Network Pharmacology

Long-lasting brain fatigue is a consequence of stroke or traumatic brain injury associated with emotional, psychological, and physical overload, distress in hypertension, atherosclerosis, viral infection, and aging-related chronic low-grade inflammatory disorders. The pathogenesis of brain fatigue is linked to disrupted neurotransmission, the glutamate-glutamine cycle imbalance, glucose metabolism, and ATP energy supply, which are associated with multiple molecular targets and signaling pathways in neuroendocrine-immune and blood circulation systems. Regeneration of damaged brain tissue is a long-lasting multistage process, including spontaneously regulating hypothalamus-pituitary (HPA) axis-controlled anabolic-catabolic homeostasis to recover harmonized sympathoadrenal system (SAS)-mediated function, brain energy supply, and deregulated gene expression in rehabilitation. The driving mechanism of spontaneous recovery and regeneration of brain tissue is a cross-talk of mediators of neuronal, microglia, immunocompetent, and endothelial cells collectively involved in neurogenesis and angiogenesis, which plant adaptogens can target. Adaptogens are small molecules of plant origin that increase the adaptability of cells and organisms to stress by interaction with the HPA axis and SAS of the stress system (neuroendocrine-immune and cardiovascular complex), targeting multiple mediators of adaptive GPCR signaling pathways. Two major groups of adaptogens comprise (i) phenolic phenethyl and phenylpropanoid derivatives and (ii) tetracyclic and pentacyclic glycosides, whose chemical structure can be distinguished as related correspondingly to (i) monoamine neurotransmitters of SAS (epinephrine, norepinephrine, and dopamine) and (ii) steroid hormones (cortisol, testosterone, and estradiol). In this narrative review, we discuss (i) the multitarget mechanism of integrated pharmacological activity of botanical adaptogens in stress overload, ischemic stroke, and long-lasting brain fatigue; (ii) the time-dependent dual response of physiological regulatory systems to adaptogens to support homeostasis in chronic stress and overload; and (iii) the dual dose-dependent reversal (hormetic) effect of botanical adaptogens. This narrative review shows that the adaptogenic concept cannot be reduced and rectified to the various effects of adaptogens on selected molecular targets or specific modes of action without estimating their interactions within the networks of mediators of the neuroendocrine-immune complex that, in turn, regulates other pharmacological systems (cardiovascular, gastrointestinal, reproductive systems) due to numerous intra- and extracellular communications and feedback regulations. These interactions result in polyvalent action and the pleiotropic pharmacological activity of adaptogens, which is essential for characterizing adaptogens as distinct types of botanicals. They trigger the defense adaptive stress response that leads to the extension of the limits of resilience to overload, inducing brain fatigue and mental disorders. For the first time, this review justifies the neurogenesis potential of adaptogens, particularly the botanical hybrid preparation (BHP) of Arctic Root and Ashwagandha, providing a rationale for potential use in individuals experiencing long-lasting brain fatigue. The review provided insight into future research on the network pharmacology of adaptogens in preventing and rehabilitating long-lasting brain fatigue following stroke, trauma, and viral infections.

The Effect of Music on Resistance to Mental Fatigue: Evidence from the EEG Power Spectrum

To evaluate the efficacy of listening to music in alleviating mental fatigue among healthy participants and to explore the neural evidence for this phenomenon via electroencephalography (EEG).

METHODS

A total of 30 participants were recruited and randomly assigned to either the music group or the control group. Mental fatigue was induced in both groups using a 30-minute Stroop task. Following this task, the music group listened to relaxing music for 20 min, whereas the control group sat quietly for the same duration. Measurements were taken at three time points: before the Stroop task, immediately after the Stroop task, and after the 20-minute intervention period. Visual analogue scale (VAS) scores and 3-minute resting-state EEG signals were collected at each time point.

RESULTS

The data indicated that listening to music significantly reduced mental fatigue. VAS scores decreased more in the music group than in the control group (P = 0.031). The EEG iAPF showed significant recovery in the music group (P < 0.0001). Delta power in the frontal region decreased significantly postintervention in the music group (P = 0.011). Theta and alpha power also decreased significantly in the music group across multiple brain regions (all Ps < 0.0076), with no significant changes observed in beta power.

CONCLUSION

These findings highlight the potential of listening to relaxing music as a noninvasive and enjoyable intervention for mitigating the effects of mental fatigue. Moreover, iAPF, theta, and alpha power can serve as reliable biomarkers for assessing mental fatigue and the restorative effects of interventions such as music.

Fatigue in Multiple Sclerosis: A Resting-State EEG Microstate Study

Fatigue affects approximately 80% of people with Multiple Sclerosis (PwMS) and can impact several domains of daily life. However, the neural underpinnings of fatigue in MS are still not completely clear. The aim of our study was to investigate the spontaneous large-scale networks functioning associated with fatigue in PwMS using the EEG microstate approach with a spectral decomposition. Forty-three relapsing-remitting MS patients and twenty-four healthy controls (HCs) were recruited. All participants underwent an administration of Modified Fatigue Impact scale (MFIS) and a 15-min resting-state high-density EEG recording. We compared the microstates of healthy subjects, fatigued (F-MS) and non-fatigued (nF-MS) patients with MS; correlations with clinical and behavioral fatigue scores were also analyzed. Microstates analysis showed six templates across groups and frequencies. We found that in the F-MS emerged a significant decrease of microstate F, associated to the salience network, in the broadband and in the beta band. Moreover, the microstate B, associated to the visual network, showed a significant increase in fatigued patients than healthy subjects in broadband and beta bands. The multiple linear regression showed that the high cognitive fatigue was predicted by both an increase and decrease, respectively, in delta band microstate B and beta band microstate F. On the other hand, higher physical fatigue was predicted with lower occurrence microstate F in beta band. The current findings suggest that in MS the higher level of fatigue might be related to a maladaptive functioning of the salience and visual network.

Comparison of mental fatigue using EEG signals and task performance in normal and slump posture adults during computer typing

OBJECTIVES

Slump sitting at workstations has been focused on by clinicians and researchers nowadays; however, there is limited evidence to date that improper positioning affects the mental state. Accordingly, the main objective of this research was to examine the impact of slump posture on mental fatigue and task performance.

METHODS

A sample of 60 participants, 30 in each group including those with normal and slump postures were recruited to perform an hour of typing on the computer. Mental fatigue through EEG and task performances were considered as outcome measures and then were analyzed statistically in the first and last 3 min of typing.

RESULTS

The EEG showed a significant increasing trend in theta rhythm at different brain regions during 60 min of typing (P < 0.05). Besides, an interaction between time and posture was observed; it can mean the increasing trend of theta rhythm is different in normal and slump posture acquired sets (P < 0.05). Interestingly the speed of typing was found to be better (P < 0.05) in the normal posture group while no difference found between the groups in terms of errors (P > 0.05).

CONCLUSION

Our results showed poor posture can induce more mental fatigue during the given task, than the normal posture. These findings have provided evidence to indicate that in addition to the peripheral and biomechanical component, the assessment of the cortex as the central component should be considered in poor posture individuals. Besides, for any possible physical therapy rehabilitation protocol for the management of poor posture, the peripheral and central components should be focused.

TRIAL REGISTRATION

Registered on the Iranian Registry of Clinical Trials on September 21, 2022, IRCT Identifier: IRCT20161026030516N2.

Research on brain functional network property analysis and recognition methods targeting brain fatigue

At present, researches on brain fatigue recognition are still in the stage of single task and simple brain region network features, while researches on high-order brain functional network features and brain region state mechanisms during fatigue in multi-task scenarios are still insufficient, making it difficult to meet the needs of fatigue recognition under complex conditions. Therefore, this study utilized functional near-infrared spectroscopy (fNIRS) technology to explore the correlation and differences in the low-order and high-order brain functional network attributes of three task induced mental fatigue, and to explore the brain regions that have a major impact on mental fatigue. Self-training algorithms were used to identify the three levels of brain fatigue. The results showed that during the fatigue development, the overall connection strength of the endothelial cell metabolic activity and neural activity frequency bands of the low-order brain functional network first decreased and then increased, while the myogenic activity and heart rate activity frequency bands showed the opposite pattern. Network topology analysis indicated that from no fatigue to mild fatigue, the clustering coefficient of endothelial cell metabolic activity and myogenic activity frequency bands significantly decreased, while the characteristic path length of myogenic activity significantly increased; when experiencing severe fatigue, the small-world attribute of the neural frequency band significantly weakened. However, each frequency band maintained its small-world attribute, reflecting the self-optimization and adaptability of the network during the fatigue process. During mild fatigue, neuronal activity bands' node degree, cluster coefficient, and efficiency rose in high-order brain networks, while low-order networks showed no significant changes. As fatigue progressed, the myogenic activity bands of high-order network properties dominated, but neural bands gained prominence in mild fatigue, approaching the level of myogenic bands in severe fatigue, indicating that brain fatigue orchestrated a shift from myogenic to neural dominance in frequency bands. In addition, during the process of fatigue, the four network attributes of the high-order network cluster composed of low-order nodes related to the prefrontal cortex region, left anterior motor region, motor assist region, and left frontal lobe eye movement region significantly increased, indicating that these brain regions had a significant impact on brain fatigue status. The accuracy of using both high-order and low-order features to identify fatigue levels reached 88.095%, indicating that the combined network features of both high-order and low-order fNIRS signals could effectively detect multi-level mental fatigue, providing innovative ideas for fatigue warning.

Diurnal biological effects of correlated colour temperature and its exposure timing on alertness, cognition, and mood in an enclosed environment

Artificial lighting, which profits from the non-visual effects of light, is a potentially promising solution to support residents' psychophysiological health and performance at specific times of the day in enclosed environments. However, few studies have investigated the non-visual effects of daytime correlated colour temperature (CCT) and its exposure timing on human alertness, cognition, and mood. However, the neural mechanisms underlying these effects are largely unknown. The current study evaluated the effects of daytime CCT and its exposure timing on markers of subjective experience, cognitive performance, and cerebral activity in a simulated enclosed environment. Forty-two participants participated a single-blind laboratory study with a 4 within (CCT: 4000 K vs. 6500 K vs. 8500 K vs. 12,000 K) × 2 between (exposure timing: morning vs. afternoon) mixed design. The results showed time of the day dependent benefits of the daytime CCT on subjective experience, vigilant attention, response inhibition, working memory, emotional perception, and risk decisions. The results of the electroencephalogram (EEG) revealed that lower-frequency EEG bands, including theta, alpha, and alpha-theta, were quite sensitive to daytime CCT intervention, which provides a valuable reference for trying to establish the underlying mechanisms that support the performance-enhancement effects of exposure to CCT in the daytime. However, the results revealed no consistent intervention pattern across these measurements. Therefore, future studies should consider personalised optimisation of daytime CCT for different cognitive demands.

The architecture of functional brain network modulated by driving under train running noise exposure

A noisy environment can considerably impact drivers' attention and fatigue, endangering driving safety. Consequently, this study designed a simulated driving experimental scenario to analyse the effects of noise generated during urban rail transit train operation on drivers' functional brain networks. The experiment recruited 16 participants, and the simulated driving scenario was conducted at noise levels of 50, 60, 70, and 80 dB. Functional connectivity between all electrode pairs across various frequency bands was evaluated using the weighted phase lag index (WPLI), and a brain network based on this was constructed. Graph theoretic analysis employed network global efficiency, degree, and clustering coefficient as metrics. Significant increases in the WPLI values of theta and alpha frequency bands were observed in high noise environments (70 dB, 80 dB), as well as enhanced brain synchronisation. Furthermore, concerning the topological metrics of brain networks, it was observed that the global efficiency of brain networks in theta and alpha frequency ranges, as well as the node degree and clustering coefficients, experienced substantial growth in high noise environments (70 dB, 80 dB) as opposed to 50 dB and 60 dB. This finding indicates that high-noise environments impact the reorganisation of functional brain networks, leading to a preference for network structures with improved global efficiency. Such findings may improve our understanding of the neural mechanisms of driving under noise exposure, and thus potentially reduce road accidents to some extent.

Research on mental fatigue during long-term motor imagery: a pilot study

Mental fatigue during long-term motor imagery (MI) may affect intention recognition in MI applications. However, the current research lacks the monitoring of mental fatigue during MI and the definition of robust biomarkers. The present study aims to reveal the effects of mental fatigue on motor imagery recognition at the brain region level and explore biomarkers of mental fatigue. To achieve this, we recruited 10 healthy participants and asked them to complete a long-term motor imagery task involving both right- and left-handed movements. During the experiment, we recorded 32-channel EEG data and carried out a fatigue questionnaire for each participant. As a result, we found that mental fatigue significantly decreased the subjects' motor imagery recognition rate during MI. Additionally the theta power of frontal, central, parietal, and occipital clusters significantly increased after the presence of mental fatigue. Furthermore, the phase synchronization between the central cluster and the frontal and occipital lobes was significantly weakened. To summarize, the theta bands of frontal, central, and parieto-occipital clusters may serve as powerful biomarkers for monitoring mental fatigue during motor imagery. Additionally, changes in functional connectivity between the central cluster and the prefrontal and occipital lobes during motor imagery could be investigated as potential biomarkers.

Passive BCI Towards Health and Safety in Industry: Forecasting Human Vigilance 5.5 s Ahead

Brain-computer interfaces based on electroencephalography (EEG) recordings are gaining increasing interest in the industrial domain, aiming to enhance health, safety and performance by optimizing the cognitive load of industrial operators and facilitating human-robot interactions. This study introduces a novel experimental protocol and analysis pipeline for predicting vigilance degradation during repetitive tasks. A dataset was recorded from 10 volunteers who observed a robotic arm executing three distinct movements. The EEG power spectrum was analyzed over time using the continuous wavelet transform. Upon verifying the increased amplitude of EEG oscillations in the 8-12 Hz frequency band, we forecast its behaviour, comparing the vector autoregressive model with two deep learning recurrent architectures. The proposed encoder-decoder gated recurrent unit model obtained accurate forecasts (mean absolute error = 0.048, R2 = 0.726) up to 5.5 s into the future. The findings suggested the feasibility of vigilance monitoring in the Industry 5.0 framework, proposing a strategy to prevent human accidents and performance decline during monotonous activities.

Fatigue in Parkinson's Disease Is Due to Decreased Efficiency of the Frontal Network: Quantitative EEG Analysis

OBJECTIVE

Fatigue is a common, debilitating nonmotor symptom of Parkinson's disease (PD), but its mechanism is poorly understood. We aimed to determine whether electroencephalography (EEG) could objectively measure fatigue and to explore the pathophysiology of fatigue in PD.

METHODS

We studied 32 de novo PD patients who underwent EEG. We compared brain activity between 19 PD patients without fatigue and 13 PD patients with fatigue via EEG power spectra and graphs, including the global efficiency, characteristic path length, clustering coefficient, small-worldness, local efficiency, degree centrality, closeness centrality, and betweenness centrality.

RESULTS

No significant differences in absolute or relative power were detected between PD patients without or with fatigue (all p > 0.02, Bonferroni-corrected). According to our network analysis, brain network efficiency differed by frequency band. Generally, the brain network in the frontal area for theta and delta bands showed greater efficiency, and in the temporal area, the alpha1 band was less efficient in PD patients without fatigue (p < 0.0001, p = 0.0011, and p = 0.0007, respectively, Bonferroni-corrected).

CONCLUSION

Our study suggests that PD patients with fatigue have less efficient networks in the frontal area than PD patients without fatigue. These findings may explain why fatigue is common in PD, a frontostriatal disorder. Increased efficiency in the temporal area in PD patients with fatigue is assumed to be compensatory. Brain network analysis using graph theory is more valuable than power spectrum analysis in revealing the brain mechanism related to fatigue.

Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study

Fatigue deteriorates the performance of a brain-computer interface (BCI) system; thus, reliable detection of fatigue is the first step to counter this problem. The fatigue evaluated by means of electroencephalographic (EEG) signals has been studied in many research projects, but widely different results have been reported. Moreover, there is scant research when considering the fatigue on steady-state visually evoked potential (SSVEP)-based BCI. Therefore, nowadays, fatigue detection is not a completely solved topic. In the current work, the issues found in the literature that led to the differences in the results are identified and saved by performing a new experiment on an SSVEP-based BCI system. The experiment was long enough to produce fatigue in the users, and different SSVEP stimulation ranges were used. Additionally, the EEG features commonly reported in the literature (EEG rhythms powers, SNR, etc.) were calculated as well as newly proposed features (spectral features and Lempel-Ziv complexity). The analysis was carried out on O1, Oz and O2 channels. This work found a tendency of displacement from high-frequency rhythms to low-frequency ones, and thus, better EEG features should present a similar behaviour. Then, the 'relative power' of EEG rhythms, the rates (θ + α)/β, α/β and θ/β, some spectral features (central and mean frequencies, asymmetry and kurtosis coefficients, etc.) and Lempel-Ziv complexity are proposed as reliable EEG features for fatigue detection. Hence, this set of features may be used to construct a more trustworthy fatigue index.

Effect of mental fatigue on hand force production capacities

Mental fatigue is common in society, but its effects on force production capacities remain unclear. This study aimed to investigate the impact of mental fatigue on maximal force production, rate of force development-scaling factor (RFD-SF), and force steadiness during handgrip contractions. Fourteen participants performed two randomized sessions, during which they either carried out a cognitively demanding task (i.e., a visual attention task) or a cognitively nondemanding task (i.e., documentary watching for 62 min). The mental fatigue was evaluated subjectively and objectively (performances and electroencephalography). Maximal voluntary contraction (MVC) force, RFD-SF, and force steadiness (i.e., force coefficient of variation at submaximal intensities; 25, 50, and 75% of MVC) were recorded before and after both tasks. The feeling of mental fatigue was much higher after completing the cognitively demanding task than after documentary watching (p < .001). During the cognitively demanding task, mental fatigue was evidenced by increased errors, missed trials, and decreased N100 amplitude over time. While no effect was reported on force steadiness, both tasks induced a decrease in MVC (p = .040), a force RFD-SF lower slope (p = .011), and a reduction in the coefficient of determination (p = .011). Nevertheless, these effects were not explicitly linked to mental fatigue since they appeared both after the mentally fatiguing task and after watching the documentary. The study highlights the importance of considering cognitive engagement and mental load when optimizing motor performance to mitigate adverse effects and improve force production capacities.

Multiscale entropy in a 10-minute vigilance task

Research has shown multiscale entropy, brain signal behavior across time scales, to reliably increase at lower time scales with time-on-task fatigue. However, multiscale entropy has not been examined in short vigilance tasks (i.e., ≤ 10 min). Addressing this gap, we examine multiscale entropy during a 10-minute Psychomotor Vigilance Test (PVT). Thirty-four participants provided neural data while completing the PVT. We compared the first 2 min of the task to the 7th and 8th minutes to avoid end-spurt effects. Results suggested increased multiscale entropy at lower time scales later compared to earlier in the task, suggesting multiscale entropy is a strong marker of time-on-task fatigue onset during short vigils. Separate analyses for Fast and Slow performers reveal differential entropy patterns, particularly over visual cortices. Here, observed brain-behavior linkage between entropy and reaction time for slow performers suggests that entropy assays over sensory cortices might have predictive value for fatigue onset or shifts from on- to off-task states.

Attempting to counteract vigilance decrement in older adults with brain stimulation

Introduction

Against the background of demographic change and the need for enhancement techniques for an aging society, we set out to repeat a study that utilized 40-Hz transcranial alternating current stimulation (tACS) to counteract the slowdown of reaction times in a vigilance experiment but with participants aged 65 years and older. On an oscillatory level, vigilance decrement is linked to rising occipital alpha power, which has been shown to be downregulated using gamma-tACS.

Method

We applied tACS on the visual cortex and compared reaction times, error rates, and alpha power of a group stimulated with 40 Hz to a sham and a 5-Hz-stimulated control group. All groups executed two 30-min-long blocks of a visual task and were stimulated according to group in the second block. We hypothesized that the expected increase in reaction times and alpha power would be reduced in the 40-Hz group compared to the control groups in the second block (INTERVENTION).

Results

Statistical analysis with linear mixed models showed that reaction times increased significantly over time in the first block (BASELINE) with approximately 3 ms/min for the SHAM and 2 ms/min for the 5-Hz and 40-Hz groups, with no difference between the groups. The increase was less pronounced in the INTERVENTION block (1 ms/min for SHAM and 5-Hz groups, 3 ms/min for the 40-Hz group). Differences among groups in the INTERVENTION block were not significant if the 5-Hz or the 40-Hz group was used as the base group for the linear mixed model. Statistical analysis with a generalized linear mixed model showed that alpha power was significantly higher after the experiment (1.37 μV2) compared to before (1 μV2). No influence of stimulation (40 Hz, 5 Hz, or sham) could be detected.

Discussion

Although the literature has shown that tACS offers potential for older adults, our results indicate that findings from general studies cannot simply be transferred to an old-aged group. We suggest adjusting stimulation parameters to the neurophysiological features expected in this group. Next to heterogeneity and cognitive fitness, the influence of motivation and medication should be considered.

Mental fatigue assessment by an arbitrary channel EEG based on morphological features and LSTM-CNN

In order to achieve more sensitive mental fatigue assessment (MFA) based on an arbitrary channel EEG, this study proposed a series of feature extraction methods that combine mathematical morphology (MM), as well as an LSTM-CNN architecture. Firstly, 37 subjects had their resting-state EEGs collected at rested wakefulness (RW) and after 24 h of sleep deprivation (SD) using a 30-channel EEG acquisition device, the RW and SD groups were regarded as the negative and positive groups of mental fatigue, respectively, and the EEG collection were further categorized into two conditions: eye-opened state (EO) and eye-closed state (EC). Then, since MM can reflect the morphological characteristics of EEG rhythms and their potentials relatively independently of the time-frequency analysis and phase calculation, the MM methods were found to better reflect the mental fatigue after SD statistically, whether for single features (ANOVA: p<0.000001), multiple features (clustering by K-means, t-test: p<0.01), or time series feature spaces (calculating CD, t-test: p<0.01) of a single channel. Finally, the LSTM-CNN enhanced the generalization ability when dealing with different single-channel EEG by combining GRUs with convolutional layers: comparing the AUCs of different architectures for MFA based on an arbitrary channel, LSTM-CNN (0.992) > LSTM network (0.94) > CNN (0.831) > MLP (0.754). Moreover, the use of MM also improved the accuracy of analyzed architectures, and the true/false positive rate (TPR/FPR) of the LSTM-CNN architecture for MFA based on an arbitrary channel reached 97.024 %/3.497 %, which provided a feasible solution for the arbitrary channel EEG-based MFA.

Videoconference fatigue from a neurophysiological perspective: experimental evidence based on electroencephalography (EEG) and electrocardiography (ECG)

In the recent past, many organizations and people have substituted face-to-face meetings with videoconferences. Among others, tools like Zoom, Teams, and Webex have become the "new normal" of human social interaction in many domains (e.g., business, education). However, this radical adoption and extensive use of videoconferencing tools also has a dark side, referred to as videoconference fatigue (VCF). To date only self-report evidence has shown that VCF is a serious issue. However, based on self-reports alone it is hardly possible to provide a comprehensive understanding of a cognitive phenomenon like VCF. Against this background, we examined VCF also from a neurophysiological perspective. Specifically, we collected and analyzed electroencephalography (continuous and event-related) and electrocardiography (heart rate and heart rate variability) data to investigate whether VCF can also be proven on a neurophysiological level. We conducted a laboratory experiment based on a within-subjects design (N = 35). The study context was a university lecture, which was given in a face-to-face and videoconferencing format. In essence, the neurophysiological data-together with questionnaire data that we also collected-show that 50 min videoconferencing, if compared to a face-to-face condition, results in changes in the human nervous system which, based on existing literature, can undoubtedly be interpreted as fatigue. Thus, individuals and organizations must not ignore the fatigue potential of videoconferencing. A major implication of our study is that videoconferencing should be considered as a possible complement to face-to-face interaction, but not as a substitute.

Effect of homeostatic pressure on daytime vigilance performance: Evidence from behaviour and resting-state EEG

Vigilance is highly sensitive to the time-of-day effect and goes through the daytime trough during the period of the post-noon dip. A midday nap could maintain individuals' vigilance at an optimal level. Thus, homeostatic sleep pressure is one of the main reasons for the post-noon dip in daytime vigilance. The current study focussed on the role of homeostatic sleep pressure in the diurnal variation of vigilance performance with normal circadian rhythms and the corresponding neural basis. With 34 healthy adults, we recorded the resting-state electroencephalogram activities and the following vigilance performance measured by psychomotor vigilance test in the morning, the no-nap mid afternoon, and the nap mid afternoon. The circadian process was controlled by measuring vigilance and resting-state electroencephalogram activities at the same time point in the nap and no-nap conditions. Homeostatic sleep pressure accumulated from morning to mid afternoon induced the declined vigilance performance and a global increase in resting-state delta, theta, alpha, and beta1 bands power, and a local increase in beta2 band power in the central region. Furthermore, the more the spontaneous beta2 power increased, the less vigilance declined from morning to mid afternoon. The current findings suggest that homeostatic sleep pressure increased cortical excitability but decreased cortical communication efficiency from morning to mid afternoon. In addition, the activity of the high beta waves probably reflected the compensatory effort to counteract the negative impact of the low arousal state on the following vigilance task by performing more action preparation in the no-nap afternoon.

Individualized Prediction of Task Performance Decline Using Pre-Task Resting-State Functional Connectivity

As a common complaint in contemporary society, mental fatigue is a key element in the deterioration of the daily activities known as time-on-task (TOT) effect, making the prediction of fatigue-related performance decline exceedingly important. However, conventional group-level brain-behavioral correlation analysis has the limitation of generalizability to unseen individuals and fatigue prediction at individual-level is challenging due to the significant differences between individuals both in task performance efficiency and brain activities. Here, we introduced a cross-validated data-driven analysis framework to explore, for the first time, the feasibility of utilizing pre-task idiosyncratic resting-state functional connectivity (FC) on the prediction of fatigue-related task performance degradation at individual level. Specifically, two behavioral metrics, namely ∆RT (between the most vigilant and fatigued states) and TOTslope over the course of the 15-min sustained attention task, were estimated among three sessions from 37 healthy subjects to represent fatigue-related individual behavioral impairment. Then, a connectome-based prediction model was employed on pre-task resting-state FC features, identifying the network-related differences that contributed to the prediction of performance deterioration. As expected, prominent populational TOT-related performance declines were revealed across three sessions accompanied with substantial inter-individual differences. More importantly, we achieved significantly high accuracies for individualized prediction of both TOT-related behavioral impairment metrics using pre-task neuroimaging features. Despite the distinct patterns between both behavioral metrics, the identified top FC features contributing to the individualized predictions were mainly resided within/between frontal, temporal and parietal areas. Overall, our results of individualized prediction framework extended conventional correlation/classification analysis and may represent a promising avenue for the development of applicable techniques that allow precaution of the TOT-related performance declines in real-world scenarios.

Understanding mental fatigue and its detection: a comparative analysis of assessments and tools

Mental fatigue has shown to be one of the root causes of decreased productivity and overall cognitive performance, by decreasing an individual's ability to inhibit responses, process information and concentrate. The effects of mental fatigue have led to occupational errors and motorway accidents. Early detection of mental fatigue can prevent the escalation of symptoms that may lead to chronic fatigue syndrome and other disorders. To date, in clinical settings, the assessment of mental fatigue and stress is done through self-reported questionnaires. The validity of these questionnaires is questionable, as they are highly subjective measurement tools and are not immune to response biases. This review examines the wider presence of mental fatigue in the general population and critically compares its various detection techniques (i.e., self-reporting questionnaires, heart rate variability, salivary cortisol levels, electroencephalogram, and saccadic eye movements). The ability of these detection tools to assess inhibition responses (which are sensitive enough to be manifested in a fatigue state) is specifically evaluated for a reliable marker in identifying mentally fatigued individuals. In laboratory settings, antisaccade tasks have been long used to assess inhibitory control and this technique can potentially serve as the most promising assessment tool to objectively detect mental fatigue. However, more studies need to be conducted in the future to validate and correlate this assessment with other existing measures of mental fatigue detection. This review is intended for, but not limited to, mental health professionals, digital health scientists, vision researchers, and behavioral scientists.

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here, we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while the participants rested with their eyes closed. A total of 289 good-quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: individual alpha frequency (IAF) increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation

Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver's drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.

Vigilance decrement and mind-wandering in sustained attention tasks: Two sides of the same coin?

Background

Decrements in performance and the propensity for increased mind-wandering (i.e., task-unrelated thoughts) across time-on-task are two pervasive phenomena observed when people perform vigilance tasks. In the present study, we asked whether processes that lead to vigilance decrement and processes that foster the propensity for mind-wandering (MW) can be dissociated or whether they share a common mechanism. In one experiment, we introduced two critical manipulations: increasing task demands and applying anodal high-definition transcranial direct current stimulation (HD-tDCS) to the left dorsolateral prefrontal cortex.

Method

Seventy-eight participants were randomly assigned to one of four groups resulting from the factorial combination of task demand (low, high) and stimulation (anodal, sham). Participants completed the sustained attention to response task (SART), which included thought probes on intentional and unintentional MW. In addition, we investigated the crucial role of alpha oscillations in a novel approach. By assessing pre-post resting EEG, we explored whether participants’ variability in baseline alpha power predicted performance in MW and vigilance decrement related to tDCS or task demands, respectively, and whether such variability was a stable characteristic of participants.

Results

Our results showed a double dissociation, such that task demands exclusively affected vigilance decrement, while anodal tDCS exclusively affected the rate of MW. Furthermore, the slope of the vigilance decrement function and MW rate (overall, intentional and unintentional) did not correlate. Critically, resting state alpha-band activity predicted tDCS-related gains in unintentional MW alone, but not in vigilance decrement, and remained stable after participants completed the task.

Conclusion

These results show that when a sustained attention task involving executive vigilance, such as the SART, is designed to elicit both vigilance decrement effects and MW, the processes leading to vigilance decrement should be differentiated from those responsible for MW, a claim that is supported by the double dissociation observed here and the lack of correlation between the measures chosen to assess both phenomena. Furthermore, the results provide the first evidence of how individual differences in alpha power at baseline may be of crucial importance in predicting the effects of tDCS on MW propensity.

Effects of opium tincture on the enteric and central nervous systems: A randomized controlled trial

Opioids change gut motility, and opium tincture has been used for treatment of chronic diarrhoea for centuries. However, the effects have never been documented in controlled trials. We aimed to investigate the effects of opium tincture on gastrointestinal transit and motility, frequency of bowel movements, stool consistency, gastrointestinal symptoms and sedation. Twenty healthy subjects were included in this randomized controlled trial. Opium tincture or placebo was each applied for 9 days. Gastrointestinal transit and motility were investigated with the 3D-transit system. Bowel movements and gastrointestinal symptoms were recorded daily. General cognition, reaction time, memory and electroencephalography were used to assess effects on the central nervous system. Opium tincture doubled colonic transit (49 vs. 23 h, p < 0.001), decreased antegrade colonic movements (p < 0.05), reduced daily bowel movements (0.7 vs. 1.2, p < 0.001) and increased stool consistency (Type 3 vs. Type 4, p < 0.001). No changes in general cognition, reaction time or memory were observed, and minor changes of power observed by electroencephalography did not indicate sedation. This study is the first to show that opium tincture has anti-propulsive properties in the healthy gut, while no sedative effects were seen. This indicates that opium tincture is a relevant and safe treatment option in chronic diarrhoea.

Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification

This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.

Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications

Brain-Computer Interfaces and especially passive Brain-Computer interfaces (pBCI), with their ability to estimate and monitor user mental states, are receiving increasing attention from both the fundamental research and the applied research and development communities. Testing new pipelines and benchmarking classifiers and feature extraction algorithms is central to further research within this domain. Unfortunately, data sharing in pBCI research is still scarce. The COG-BCI database encompasses the recordings of 29 participants over 3 separate sessions with 4 different tasks (MATB, N-Back, PVT, Flanker) designed to elicit different mental states, for a total of over 100 hours of open EEG data. This dataset was validated on a subjective, behavioral and physiological level, to ensure its usefulness to the pBCI community. Furthermore, a proof of concept is given with an example of mental workload estimation pipeline and results, to ensure that the data can be used for the design and evaluation of pBCI pipelines. This body of work presents a large effort to promote the use of pBCIs in an open science framework.

Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design

OBJECTIVE

We demonstrate and discuss the use of mobile electroencephalogram (EEG) for neuroergonomics. Both technical state of the art as well as measures and cognitive concepts are systematically addressed.

BACKGROUND

Modern work is increasingly characterized by information processing. Therefore, the examination of mental states, mental load, or cognitive processing during work is becoming increasingly important for ergonomics.

RESULTS

Mobile EEG allows to measure mental states and processes under real live conditions. It can be used for various research questions in cognitive neuroergonomics. Besides measures in the frequency domain that have a long tradition in the investigation of mental fatigue, task load, and task engagement, new approaches-like blink-evoked potentials-render event-related analyses of the EEG possible also during unrestricted behavior.

CONCLUSION

Mobile EEG has become a valuable tool for evaluating mental states and mental processes on a highly objective level during work. The main advantage of this technique is that working environments don't have to be changed while systematically measuring brain functions at work. Moreover, the workflow is unaffected by such neuroergonomic approaches.

Exploring the use of music to promote physical activity: From the viewpoint of psychological hedonism

Despite the global efforts to encourage people to regularly participate in physical activity (PA) at moderate-to-vigorous intensity, an inadequate number of adults and adolescents worldwide meet the recommended dose of PA. A major challenge to promoting PA is that sedentary or low-active people experience negative shifts in affective valence (feeling bad versus good) in response to moderate-to-vigorous intensity PA. Interestingly, empirical data indicate that listening to music during acute bouts of PA positively alters affective valence (feeling good versus bad), reduces perceived exertion, and improves physical performance and oxygen utilization efficiency. From the viewpoint of the ancient principle of psychological hedonism - humans have ultimate desires to obtain pleasure and avoid displeasure - we elaborate on three putative mechanisms underlying the affective and ergogenic effects of music on acute bouts of PA: (1) musical pleasure and reward, (2) rhythmic entrainment, and (3) sensory distraction from physical exertion. Given that a positive shift in affective valence during an acute bout of PA is associated with more PA in the future, an important question arises as to whether the affective effect of music on acute PA can be carried over to promote long-term PA. Although this research question seems intuitive, to our knowledge, it has been scarcely investigated. We propose a theoretical model of Music as an Affective Stimulant to Physical Activity (MASPA) to further explain the putative mechanisms underlying the use of music to promote long-term PA. We believe there have been important gaps in music-based interventions in terms of the rationale supporting various components of the intervention and the efficacy of these interventions to promote long-term PA. Our specification of relevant mechanisms and proposal of a new theoretical model may advance our understanding of the optimal use of music as an affective, ergogenic, and sensory stimulant for PA promotion. Future directions are suggested to address the gaps in the literature.

The mitigation of the executive vigilance decrement via HD-tDCS over the right posterior parietal cortex and its association with neural oscillations

Vigilance-maintaining a prolonged state of preparation to detect and respond to specific yet unpredictable environmental changes-usually decreases across prolonged tasks, causing potentially severe real-life consequences, which could be mitigated through transcranial direct current stimulation (tDCS). The present study aimed at replicating previous mitigatory effects observed with anodal high-definition tDCS (HD-tDCS) over the right posterior parietal cortex (rPPC) while extending the analyses on electrophysiological measures associated with vigilance. In sum, 60 participants completed the ANTI-Vea task while receiving anodal (1.5 mA, n = 30) or sham (0 mA, n = 30) HD-tDCS over the rPPC for ~ 28 min. EEG recordings were completed before and after stimulation. Anodal HD-tDCS specifically mitigated executive vigilance (EV) and reduced the alpha power increment across time-on-task while increasing the gamma power increment. To further account for the observed behavioral and physiological outcomes, a new index of Alphaparietal/Gammafrontal is proposed. Interestingly, the increment of this Alphaparietal/Gammafrontal Index with time-on-task is associated with a steeper EV decrement in the sham group, which was mitigated by anodal HD-tDCS. We highlight the relevance of replicating mitigatory effects of tDCS and the need to integrate conventional and novel physiological measures to account for how anodal HD-tDCS can be used to modulate cognitive performance.

The therapeutic potential of non-invasive brain stimulation for the treatment of Long-COVID-related cognitive fatigue

Following an acute COVID-19 infection, a large number of patients experience persisting symptoms for more than four weeks, a condition now classified as Long-COVID syndrome. Interestingly, the likelihood and severity of Long-COVID symptoms do not appear to be related to the severity of the acute COVID-19 infection. Fatigue is amongst the most common and debilitating symptoms of Long-COVID. Other symptomes include dyspnoea, chest pain, olfactory disturbances, and brain fog. Fatigue is also frequently reported in many other neurological diseases, affecting a broad range of everyday activities. However, despite its clinical significance, limited progress has been made in understanding its causes and developing effective treatment options. Non-invasive brain stimulation (NIBS) methods offer the unique opportunity to modulate fatigue-related maladaptive neuronal activity. Recent data show promising results of NIBS applications over frontoparietal regions to reduce fatigue symptoms. In this current paper, we review recent data on Long-COVID and Long-COVID-related fatigue (LCOF), with a special focus on cognitive fatigue. We further present widely used NIBS methods, such as transcranial direct current stimulation, transcranial alternating current stimulation, and transcutaneous vagus nerve stimulation and propose their use as possible therapeutic strategies to alleviate individual pathomechanisms of LCOF. Since NIBS methods are safe and well-tolerated, they have the potential to enhance the quality of life in a broad group of patients.

Alterations of resting-state networks of Parkinson's disease patients after subthalamic DBS surgery

The implantation of deep brain stimulation (DBS) electrodes in Parkinson's disease (PD) patients can lead to a temporary improvement in motor symptoms, known as the stun effect. However, the network alterations induced by the stun effect are not well characterized. As therapeutic DBS is known to alter resting-state networks (RSN) and subsequent motor symptoms in patients with PD, we aimed to investigate whether the DBS-related stun effect also modulated RSNs. Therefore, we analyzed RSNs of 27 PD patients (8 females, 59.0 +- 8.7 years) using magnetoencephalography and compared them to RSNs of 24 age-matched healthy controls (8 females, 62.8 +- 5.1 years). We recorded 30 min of resting-state activity two days before and one day after implantation of the electrodes with and without dopaminergic medication. RSNs were determined by use of phase-amplitude coupling between a low frequency phase and a high gamma amplitude and examined for differences between conditions (i.e., pre vs post surgery). We identified four RSNs across all conditions: sensory-motor, visual, fronto-occipital, and frontal. Each RSN was altered due to electrode implantation. Importantly, these changes were not restricted to spatially close areas to the electrode trajectory. Interestingly, pre-operative RSNs corresponded better with healthy control RSNs regarding the spatial overlap, although the stun effect is associated with motor improvement. Our findings reveal that the stun effect induced by implantation of electrodes exerts brain wide changes in different functional RSNs.

Fatigability-related oscillatory brain activity changes in people with MS

BACKGROUND

Fatigue, a multidimensional and challenging symptom associated with various underlying conditions, can manifest as a subjective feeling and a performance fatigability. The latter is often defined as an objectively measurable performance decline with time on task. Both syndromes are highly prevalent in people with multiple sclerosis (pwMS) and are often resistant to medical therapy. In the absence of valid and reliable objective parameters, the current cognitive fatigue diagnosis remains purely subjective. Assessing brain wave activity changes has repeatedly been a viable strategy for monitoring cognitive fatigue in healthy subjects. In this study, we aimed to investigate oscillatory brain activity changes and their associations with subjective fatigue in pwMS.

METHODS

We enrolled 21 pwMS and 21 healthy controls (HC) in this study. Subjects performed a sustained attention task divided into six blocks over the course of 30 minutes, and underwent resting state EEGs before and after the task. During the task, subjects were repeatedly asked to rate their subjective levels of mental fitness, mental exhaustion, and mind wandering. Using Linear Mixed Models, we explored fatigability-related changes by focusing on the time course of changes in reaction time variability, subjective ratings of fatigability, as well as frontomedial theta, and occipital alpha power. We further investigated initial and fatigability-induced differences between pwMS and HC at rest. Finally, Pearson correlations were used to examine the relationship between subjective fatigue and objective fatigability parameters.

RESULTS

Our results revealed a systematically stronger fatigability development in pwMS that was objectively measurable. PwMS reported lower mental fitness levels and demonstrated greater variability in reaction times with time on task. Occipital alpha power significantly increased during the task. Especially for upper alpha power, this increase was significantly more prominent in pwMS compared to HC. However, the time-on-task-induced changes in our study were not associated with the subjective fatigue ratings.

CONCLUSIONS

The results of this study expand the understanding of the neural mechanisms underlining cognitive fatigability and may complement the fatigue diagnosis and therapy monitoring with quantitative objective methods.

Vigilance described by the time-on-task effect in EEG activity during a cued Go/NoGo task

Vigilance refers to the ability to maintain attention and to remain alert to stimuli in prolonged and monotonous tasks. Vigilance decrement describes the decline in performance in the course of such sustained attention tasks. Time-related alterations in attention have been found to be associated with changes in EEG. We investigated these time-on-task effects on the basis of changes in the conventional EEG spectral bands with the aim of finding a compound measure of vigilance. 148 healthy adults performed a cued Go/NoGo task that lasted approximately 21 min. Behavioural performance was examined by comparing the number of errors in the first and last quarters of the task using paired t-test. EEG data were epoched per trial, and time-on-task effects were modelled by using multiple linear regression, with frequency spectra band power values as independent variables and trial number as the dependent variable. Behavioural performance decreased in terms of omission errors only. Performance of the models, expressed by predicted R-squared, was between 0.10 and 0.27, depending on the particular task condition. The time-on-task EEG spectral changes were characterized by broad changes in the alpha and frontal changes in the beta and gamma bands. We were able to identify a set of EEG spectral features that predict time-on-task. Our output is considered to be a measure of vigilance, reflecting the allocation of mental resources for the maintenance of attention.

Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls

(1) Objective: The aim of this study was to examine the effect of high altitude on inhibitory control processes that underlie sustained attention in the neural correlates of EEG data, and explore whether the EEG data reflecting inhibitory control contain valuable information to classify high-altitude chronic hypoxia and plain controls. (2) Methods: 35 chronic high-altitude hypoxic adults and 32 matched controls were recruited. They were required to perform the go/no-go sustained attention task (GSAT) using event-related potentials. Three machine learning algorithms, namely a support vector machine (SVM), logistic regression (LR), and a decision tree (DT), were trained based on the related ERP components and neural oscillations to build a dichotomous classification model. (3) Results: Behaviorally, we found that the high altitude (HA) group had lower omission error rates during all observation periods than the low altitude (LA) group. Meanwhile, the ERP results showed that the HA participants had significantly shorter latency than the LAs for sustained potential (SP), indicating vigilance to response-related conflict. Meanwhile, event-related spectral perturbation (ERSP) analysis suggested that lowlander immigrants exposed to high altitudes may have compensatory activated prefrontal cortexes (PFC), as reflected by slow alpha, beta, and theta frequency-band neural oscillations. Finally, the machine learning results showed that the SVM achieved the optimal classification F1 score in the later stage of sustained attention, with an F1 score of 0.93, accuracy of 92.54%, sensitivity of 91.43%, specificity of 93.75%, and area under ROC curve (AUC) of 0.97. The results proved that SVM classification algorithms could be applied to identify chronic high-altitude hypoxia. (4) Conclusions: Compared with other methods, the SVM leads to a good overall performance that increases with the time spent on task, illustrating that the ERPs and neural oscillations may provide neuroelectrophysiological markers for identifying chronic plateau hypoxia.

Scalable Gamma-Driven Multilayer Network for Brain Workload Detection Through Functional Near-Infrared Spectroscopy

This work proposes a scalable gamma non-negative matrix network (SGNMN), which uses a Poisson randomized Gamma factor analysis to obtain the neurons of the first layer of a network. These neurons obey Gamma distribution whose shape parameter infers the neurons of the next layer of the network and their related weights. Upsampling the connection weights follows a Dirichlet distribution. Downsampling hidden units obey Gamma distribution. This work performs up-down sampling on each layer to learn the parameters of SGNMN. Experimental results indicate that the width and depth of SGNMN are closely related, and a reasonable network structure for accurately detecting brain fatigue through functional near-infrared spectroscopy can be obtained by considering network width, depth, and parameters.

Modulating break types induces divergent low band EEG processes during post-break improvement: A power spectral analysis

Conventional wisdom suggests mid-task rest as a potential approach to relieve the time-on-task (TOT) effect while accumulating evidence indicated that acute exercise might also effectively restore mental fatigue. However, few studies have explored the neural mechanism underlying these different break types, and the results were scattered. This study provided one of the first looks at how different types of fatigue-recovery break exerted influence on the cognitive processes by evaluating the corresponding behavioral improvement and neural response (EEG power spectral) in a sustained attention task. Specifically, 19 participants performed three sessions of psychomotor vigilance tasks (PVT), with one session including a continuous 30-min PVT while the other two sessions additionally inserted a 15-min mid-task cycling and rest break, respectively. For behavioral performance, both types of break could restore objective vigilance transiently, while subjective feeling was only maintained after mid-task rest. Moreover, divergent patterns of EEG change were observed during post-break improvement. In detail, relative theta decreased and delta increased immediately after mid-task exercise, while decreased delta was found near the end of the rest-inserted task. Meanwhile, theta and delta could serve as neurological indicators to predict the reaction time change for exercise and rest intervention, respectively. In sum, our findings provided novel evidence to demonstrate divergent neural patterns following the mid-task exercise and rest intervention to counter TOT effects, which might lead to new insights into the nascent field of neuroergonomics for mental fatigue restoration.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

Automatic Detection of Driver Fatigue Based on EEG Signals Using a Developed Deep Neural Network

In recent years, detecting driver fatigue has been a significant practical necessity and issue. Even though several investigations have been undertaken to examine driver fatigue, there are relatively few standard datasets on identifying driver fatigue. For earlier investigations, conventional methods relying on manual characteristics were utilized to assess driver fatigue. In any case study, such approaches need previous information for feature extraction, which could raise computing complexity. The current work proposes a driver fatigue detection system, which is a fundamental necessity to minimize road accidents. Data from 11 people are gathered for this purpose, resulting in a comprehensive dataset. The dataset is prepared in accordance with previously published criteria. A deep convolutional neural network–long short-time memory (CNN–LSTM) network is conceived and evolved to extract characteristics from raw EEG data corresponding to the six active areas A, B, C, D, E (based on a single channel), and F. The study’s findings reveal that the suggested deep CNN–LSTM network could learn features hierarchically from raw EEG data and attain a greater precision rate than previous comparative approaches for two-stage driver fatigue categorization. The suggested approach may be utilized to construct automatic fatigue detection systems because of their precision and high speed.

State Changes During Resting-State (Magneto)encephalographic Studies: The Effect of Drowsiness on Spectral, Connectivity, and Network Analyses

Background

A common problem in resting-state neuroimaging studies is that subjects become drowsy or fall asleep. Although this could drastically affect neurophysiological measurements, such as magnetoencephalography (MEG), its specific impact remains understudied. We aimed to systematically investigate how often drowsiness is present during resting-state MEG recordings, and how the state changes alter quantitative estimates of oscillatory activity, functional connectivity, and network topology.

Methods

About 8-min MEG recordings of 19 healthy subjects, split into ~13-s epochs, were scored for the presence of eyes-open (EO), alert eyes-closed (A-EC), or drowsy eyes-closed (D-EC) states. After projection to source-space, results of spectral, functional connectivity, and network analyses in 6 canonical frequency bands were compared between these states on a global and regional levels. Functional connectivity was analyzed using the phase lag index (PLI) and corrected amplitude envelope correlation (AECc), and network topology was analyzed using the minimum spanning tree (MST).

Results

Drowsiness was present in >55% of all epochs that did not fulfill the AASM criteria for sleep. There were clear differences in spectral results between the states (A-EC vs. D-EC) and conditions (EO vs. A-EC). The influence of state and condition was far less pronounced for connectivity analyses, with only minimal differences between D-EC and EO in the AECc in the delta band. There were no effects of drowsiness on any of the MST measures.

Conclusions

Drowsiness during eyes-closed resting-state MEG recordings is present in the majority of epochs, despite the instructions to stay awake. This has considerable influence on spectral properties, but much less so on functional connectivity and network topology. These findings are important for interpreting the results of EEG/MEG studies using spectral analyses in neurological disease, where recordings should be evaluated for the presence of drowsiness. For connectivity analyses or studies on network topology, this seems of far less importance.

Effects of Physical and Mental Fatigue on Postural Sway and Cortical Activity in Healthy Young Adults

Physical fatigue (PF) negatively affects postural control, resulting in impaired balance performance in young and older adults. Similar effects on postural control can be observed for mental fatigue (MF) mainly in older adults. Controversial results exist for young adults. There is a void in the literature on the effects of fatigue on balance and cortical activity. Therefore, this study aimed to examine the acute effects of PF and MF on postural sway and cortical activity. Fifteen healthy young adults aged 28 ± 3 years participated in this study. MF and PF protocols comprising of an all-out repeated sit-to-stand task and a computer-based attention network test, respectively, were applied in random order. Pre and post fatigue, cortical activity and postural sway (i.e., center of pressure displacements [CoP_d], velocity [CoP_v], and CoP variability [CV CoP_d, CV CoP_v]) were tested during a challenging bipedal balance board task. Absolute spectral power was calculated for theta (4–7.5 Hz), alpha-2 (10.5–12.5 Hz), beta-1 (13–18 Hz), and beta-2 (18.5–25 Hz) in frontal, central, and parietal regions of interest (ROI) and baseline-normalized. Inference statistics revealed a significant time-by-fatigue interaction for CoP_d (p = 0.009, d = 0.39, Δ 9.2%) and CoP_v (p = 0.009, d = 0.36, Δ 9.2%), and a significant main effect of time for CoP variability (CV CoP_d: p = 0.001, d = 0.84; CV CoP_v: p = 0.05, d = 0.62). Post hoc analyses showed a significant increase in CoP_d (p = 0.002, d = 1.03) and CoP_v (p = 0.003, d = 1.03) following PF but not MF. For cortical activity, a significant time-by-fatigue interaction was found for relative alpha-2 power in parietal (p < 0.001, d = 0.06) areas. Post hoc tests indicated larger alpha-2 power increases after PF (p < 0.001, d = 1.69, Δ 3.9%) compared to MF (p = 0.001, d = 1.03, Δ 2.5%). In addition, changes in parietal alpha-2 power and measures of postural sway did not correlate significantly, irrespective of the applied fatigue protocol. No significant changes were found for the other frequency bands, irrespective of the fatigue protocol and ROI under investigation. Thus, the applied PF protocol resulted in increased postural sway (CoP_d and CoP_v) and CoP variability accompanied by enhanced alpha-2 power in the parietal ROI while MF led to increased CoP variability and alpha-2 power in our sample of young adults. Potential underlying cortical mechanisms responsible for the greater increase in parietal alpha-2 power after PF were discussed but could not be clearly identified as cause. Therefore, further future research is needed to decipher alternative interpretations.

Study on Fatigue Coefficient of Airline Pilots

This paper uses the Multidimensional Fatigue Inventory (MFI-16) to investigate the fatigue status of pilots, and the reliability and validity of the scale are tested by Cronbach’s α and exploratory factor analysis. The founding shows that mild fatigue and above accounted for 67.7%. For further quantify the impact of different flights on pilots’ fatigue, research improves the fatigue coefficient model based on the results of pilot fatigue feeling questionnaire. Combined with multifactor analysis of variance and multiple linear regression, it is found that the independent variables have different and positive effects on the dependent variables, and there is no multicollinearity. Through the actual test, its accuracy is improved by 16.7% compared with the original model.

Four Weeks of a Neuro-Meditation Program Improves Sleep Quality and Reduces Hypertension in Nursing Staff During the COVID-19 Pandemic: A Parallel Randomized Controlled Trial

The purpose of this study was to examine the effectiveness of a neuro-meditation program to support nurses during the COVID-19 pandemic. Forty-five (10 men and 35 women) nurses were classified into three groups based on their systolic blood pressure: normotensive (G-nor; n = 16, 43.8 ± 11.0 year), hypertensive (G-hyp; n = 13, 45.2 ± 10.7 year) and control (G-con; n = 16, 44.9 ± 10.6 year). Using a parallel, randomly controlled design across a 4-week period, 10 × 30-min sessions using the Rebalance© Impulse were completed. Sleep was assessed by wrist actigraphy and subjective sleep questionnaires; perceived sleep quality, Ford Insomnia Response to Stress Test questionnaire and the Spiegel Sleep Quality questionnaire (SSQ). Blood pressure, resting heart rate, mean heart rate (HRmean), heart rate variability index (RMSSD), cortisol, and alpha-amylase were also measured. Statistical analysis was completed using factorial ANOVA. Sleep improved in the G-hyp group; SSQ (p < 0.01); perceived sleep quality (p < 0.01); sleep efficiency and fragmentation index (p < 0.05). In the G-nor group, sleep was improved to a lesser extent; perceived sleep quality (p < 0.01). A significant time-group interaction was reported in resting heart rate (p < 0.01), systolic blood pressure (p < 0.01), and diastolic blood pressure (p < 0.05) with these measures being significantly reduced in the G-hyp group. RMSSD increased in the G-nor group (p < 0.01). This initial evidence suggests that neuro-meditation reduces excessive sympathetic activity, promoting enhanced sleep quality and autonomic control during periods of increased work-related stress.

Clinical Trial Registration

The study was conducted at Bioesterel, Sophia-Antipolis, France as a clinical trial: Neuro-meditation improves sleep quality, https://www.drks.de/ui_data_web/DrksUI.html?locale=en, DRKS00025731.

Heterogeneous overtaking and learning styles with varied EEG patterns in a reinforced driving task

Overtaking maneuvers occur when vehicle drivers pursue higher driving speeds or comfort scenarios through back-to-back lane-changing behaviors, which require active participation of mental resources and certain self-learning practices. However, few studies have investigated how brain activities change during overtaking. Moreover, the learning process, which indicates the heterogeneity of drivers from a process-based perspective, has been neglected. In this work, we studied varied overtaking and learning styles using electroencephalogram (EEG) signals collected from drivers during a simulated driving task with a possible learning process. The average speed, standard deviation of speed, steering wheel angle and lateral movement distance of overtaking behaviors are analyzed in these reinforced tasks to evaluate overtaking performance. Four types of overtaking styles (i.e., low-speed type, low-speed & strong-oscillation type, high-speed & strong-steering type, and high-speed & close-distance type) and three types of learning styles (i.e., stable, adaptive and changeful) are discovered, not only from eventual overtaking behaviors but also from behavioral changes in a certain learning process. EEG features, such as the power spectral density (PSD) in the θ, α, β and γ bands, are extracted to characterize driver mental states and to correlate with heterogeneous learning styles. The obtained results show that fatigue and fatigue confrontation are more likely with a stable learning style, and the mental workload is reduced with an adaptive learning style, whereas no significant changes in brain activity are apparent with a changeful learning style. Understanding and recognizing heterogeneous overtaking and learning styles with varying EEG patterns will be extremely useful in the future for deep integration of advanced driving assistance systems (ADASs) and brain computer interface (BCI) systems.

Assessment of Combination of Automated Pupillometry and Heart Rate Variability to Detect Driving Fatigue

Objectives

Approximately 20~30% of all traffic accidents are caused by fatigue driving. However, limited practicability remains a barrier for the real application of available techniques to detect driving fatigue. Use of pupillary light reflex (PLR) may be potentially effective for driving fatigue detection.

Methods

A 90 min monotonous simulated driving task was utilized to induce driving fatigue. During the task, PLR measurements were performed at baseline and at an interval of 30 min. Subjective rating scales, heart rate variability (HRV) were monitored simultaneously.

Results

Thirty-two healthy volunteers in China participated in our study. Based on the results of subjective evaluation and behavioral performances, driving fatigue was verified to be successfully induced by a simulated driving task. Significant variations of PLR and HRV parameters were observed, which also showed significant relevance with the change in Karolinska Sleepiness Scale at several timepoints (|r| = 0.55 ~ 0.72, P < 0.001). Furthermore, PLR variations had excellent ability to detect driving fatigue with high sensitivity and specificity, of which maximum constriction velocity variations achieved a sensitivity of 85.00% and specificity of 72.34% for driving fatigue detection, vs. 82.50 and 78.72% with a combination of HRV variations, a nonsignificant difference (AUC = 0.835, 0.872, P > 0.05).

Conclusions

Pupillary light reflex variation may be a potential indicator in the detection of driving fatigue, achieving a comparative performance compared with the combination with heart rate variability. Further work may be involved in developing a commercialized driving fatigue detection system based on pupillary parameters.

EEG-based brain functional connectivity representation using amplitude locking value for fatigue-driving recognition

It has been shown that brain functional networks constructed from electroencephalographic signals (EEG) continuously change topology as brain fatigue increases, and extracting the topological properties of the network can characterize the degree of brain fatigue. However, the traditional brain function network construction process often selects only the amplitude or phase components of the signal to measure the relationship between brain regions, and the use of a single component of the signal to construct a brain function network for analysis is rather one-sided. Therefore, we propose a method of functional synchronization analysis of brain regions. This method takes the EEG signal based on empirical modal decomposition (EMD) to obtain multiple intrinsic modal components (IMF) and inputs them into the Hilbert transform to obtain the instantaneous amplitude, and then calculates the amplitude locking value (ALV) to measure the synchronization relationship between all pairs of channels. The topological properties of the brain functional network are extracted to classify awake and fatigue states. The brain functional network is constructed based on the adjacency matrix of each waveform obtained from the ALV between all pairs of channels to realize the synchronization analysis between brain regions. Moreover, we achieved a satisfactory classification accuracy (82.84%) using the discriminative connection features in the Alpha band. In this study, we analyzed the functional network of ALV brain in fatigue and awake state, and the results showed that the connections between brain regions in fatigue state were significantly increased, and the connections between brain regions in the awake state were significantly decreased, and the information interaction between brain regions was more orderly and efficient.