The impact of mental fatigue on brain activity: a comparative study both in resting state and task state using EEG

The impact of hyperventilation on brain alpha activity: An EEG study

Hyperventilation (HV) is a major physiological risk factor in environments like high altitudes or hypoxic conditions, causing a range of physiological changes that can potentially impair cognitive functions. As an important bridge connecting brain physiological states and cognitive functions, the variation of alpha activity under the effect of HV has not been fully explored. To this end, this work aims to reveal the changes in EEG alpha activity induced by HV in terms of power spectrum and functional connectivity (FC). EEG data were recorded from 305 healthy young male subjects when they were under three stages: Pre-HV, HV, and Post-HV. Then, EEG power spectrum was estimated and adjusted by removing the aperiodic components. The alpha peak frequency (APF) and adjusted alpha peak frequency (aAPF) were both slowed from Pre-HV to HV and recovered in Post-HV, which revealed a U-shaped trend. Both the alpha peak power (AP) and adjusted alpha peak power (aAP) decreased during HV. FC was assessed via the weighted Phase Lag Index (wPLI), which exhibited a HV-related decrease followed by an increase in Post-HV, with a rightward lateralization shift. In summary, both the power spectrum and FC metrics showed a U-shaped tendency, suggesting a negative impact of HV on alpha activity. Our findings provide some of the first quantitative insights into the effects of HV on alpha activity, further confirming the regulatory patterns of HV on neural activity.

Quantifying Sleep Quality Through Delta-Beta Coupling Across Sleep and Wakefulness

Modern lifestyles contribute to insufficient sleep, impairing cognitive function and weakening the immune system. Sleep quality (SQ) is vital for physiological and mental health, making its understanding and accurate assessment critical. However, its multifaceted nature, shaped by neurological and environmental factors, makes precise quantification challenging. Here, we address this challenge by utilizing electroencephalography (EEG) for phase-amplitude coupling (PAC) analysis to elucidate the neurological basis of SQ, examining both states of sleep and wakefulness, including resting state (RS) and working memory. Our results revealed distinct patterns in beta power and delta connectivity in sleep and RS, together with the reaction time of working memory. A notable finding was the pronounced delta-beta PAC, a feature markedly stronger in individuals with good SQ. We further observed that SQ was positively correlated with increased delta-beta PAC. Leveraging these insights, we applied machine learning models to classify SQ at an individual level, demonstrating that the delta-beta PAC outperformed other EEG characteristics. These findings establish delta-beta PAC as a robust electrophysiological marker to quantify SQ and elucidate its neurological determinants.

Music boosts the recovery of attention after mental fatigue in healthy young male subjects: A human auditory event-related potential study

Daily life faces continuous cognitive tasks. Several methods could lessen cognitive fatigue including music. To find out how music functions in recovering cognitive fatigue, twenty-seven participants were randomly assigned to the rest group (N = 12) and the music group (N = 15). To evaluate the effects of Mozart K488 music on attention function after a continuous cognitively demanding task. Participants completed subjective questionnaires and the contingent negative variation (CNV) task before fatigue, after fatigue, and after the rest/musical intervention. EEG and ECG data were also collected during the experiment. The results showed that 5 min of Mozart K488 music resulted in improved CNV task performance in the musical intervention group. For EEG data, recoveries of the initial CNV and terminal CNV amplitude in Cz and CPz electrodes were observed and compared with the values after Mental Fatigue, which music increased the iCNV and tCNV. Alpha-ERD was lower after listening to music than after resting. Moreover, during music playing, compared to other brain regions the EEG alpha power of participants was significantly high in the central frontal region. This study demonstrates a short-term musical intervention can effectively boost the recovery of attention after Mental Fatigue.

Impact of expectancy on fatigue by exposure to the fifth generation of mobile communication signals

There is a long-standing debate about the relationship between Radio Frequency Electromagnetic Field (RF-EMF) exposure and fatigue. Past studies primarily rely on self-report scales to assess fatigue, but these methods are often susceptible to personal biases. Notably, the role of psychological factors in the fatigue response induce by RF-EMF exposure remains unclear. Therefore, our study focuses on exploring the impact of 5 G signal exposure on human fatigue, particularly considering the influence of expectancy induced by psychological priming on the outcomes. In this study, we recruited 21 healthy subjects who were tested in three sessions. Each session included two 30-min exposures to either real or sham 5 G signals, with the order randomized. The experiment was conducted under varying informational conditions: subjects were provided with correct, false, or no information about the order of exposure. Additionally, subjects completed a fatigue scoring questionnaire and underwent Electroencephalogram (EEG) measurements during the experiment. The statistical comparison indicates that 5 G RF-EMF exposure at routine levels does not lead to changes in EEG power. The finding reveals that the report of fatigue can be altered by the conveyed information of being exposed by 5 G signals although there is no real exposure and no detectable electrophysiological indicator. Our findings suggest that it is necessary to prevent psychological priming in any kind or to take its possible consequence into consideration, to reveal this effect of RF-EMF exposure.

Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals

BACKGROUND/OBJECTIVES

The study explores the integration of human feedback into the control loop of mobile robots for real-time obstacle detection and avoidance using EEG brain-computer interface (BCI) methods. The goal is to assess the possible paradigms applicable to the most current navigation system to enhance safety and interaction between humans and robots.

METHODS

The research explores passive and active brain-computer interface (BCI) technologies to enhance a wheelchair-mobile robot's navigation. In the passive approach, error-related potentials (ErrPs), neural signals triggered when users comment or perceive errors, enable automatic correction of the robot navigation mistakes without direct input or command from the user. In contrast, the active approach leverages steady-state visually evoked potentials (SSVEPs), where users focus on flickering stimuli to control the robot's movements directly. This study evaluates both paradigms to determine the most effective method for integrating human feedback into assistive robotic navigation. This study involves experimental setups where participants control a robot through a simulated environment, and their brain signals are recorded and analyzed to measure the system's responsiveness and the user's mental workload.

RESULTS

The results show that a passive BCI requires lower mental effort but suffers from lower engagement, with a classification accuracy of 72.9%, whereas an active BCI demands more cognitive effort but achieves 84.9% accuracy. Despite this, task achievement accuracy is higher in the passive method (e.g., 71% vs. 43% for subject S2) as a single correct ErrP classification enables autonomous obstacle avoidance, whereas SSVEP requires multiple accurate commands.

CONCLUSIONS

This research highlights the trade-offs between accuracy, mental load, and engagement in BCI-based robot control. The findings support the development of more intuitive assistive robotics, particularly for disabled and elderly users.

Unveiling the EEG signatures of extrasensory perception during spiritual experiences: A single-case study with a well-renowned channeler

Just as the brain of Albert Einstein is studied in an attempt to understand human intelligence or the bodies of elite athletes are examined to improve muscle strength, the study of people who claim to have spiritual experiences could enrich the investigation of the brain-mind relationship. Although mediumship with deceased people is widely extensively studied in spiritual experiences, we explored a mediumistic experience called "channeling" where the individual connects with a non-corporeal intelligence (NCI) source. To approach this kind of spiritual experience, we considered three hypotheses: the fraud hypothesis (i), the mental pathology hypothesis (ii), and the extrasensory perception hypothesis (iii). In this single case study, the participant was a well-known channeler with nearly three decades of experience connecting with NCIs. Given the EEG results, we rejected the fraud hypothesis, rejected the mental pathology hypothesis, and felt we needed more information to conclude the extrasensory perception hypothesis. The approach of the present single-case study may help researchers design follow-up rigorous protocols for mediumship and channeling studies, which could contribute to a better understanding of the brain during spiritual experiences.

Different cortex activation between young and middle-aged people during different type problem-solving: An EEG&fNIRS study

Problem-solving strategies vary depending on the type of problem and aging. This study investigated the hemodynamic response measured by the changes in the oxyhemoglobin concentration (HbO), alpha frequency power, and their interrelation during problem-solving in healthy young and middle-aged individuals, employing combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recordings. The study included 39 young and 30 middle-aged subjects. The brain activation that occurred while answering different questions was recorded using combined EEG and fNIRS. During the EEG & fNIRS recording, four questions (arithmetic, general knowledge, insight, and basic operation) were used for problem-solving. Alpha power (8-13 Hz) and HbO changes were analyzed. The behavioral results indicated significant differences between age groups in various question types. While the middle-aged group performed better on the general knowledge questions, the older group performed better on the insight and four-process questions. The fNIRS results reveal significant differences in brain activation during problem-solving tasks, particularly in regions like DLPFC/TA, STG, pSSC/Wernicke, and STG/angular gyrus Wernicke's area. The young group with the highest HbO was recorded during arithmetic questions, general knowledge questions, and basic operation questions. In contrast, there was no significant highest HbO during insight questions. Similar findings were observed in the middle-aged group, with the highest HbO recorded during general knowledge questions. However, there was no significant HbO in other channels during the solving of other question types in this group. The alpha power varied across different electrodes for various question types in both young and middle-aged groups. The highest alpha frequency band power for different electrodes was recorded while solving general knowledge questions in the young group and insight questions in the middle-aged group. Finally, the EEG and fNIRS correlation results showed positive correlations between HbO and alpha frequency band power in specific brain regions while solving general knowledge questions, particularly in the middle-aged group. The study reveals age-related differences in behavioral performance, brain activation patterns, and neural correlates during various cognitive tasks, showcasing distinct strengths between middle-aged and young individuals in specific question types.

The power spectrum and functional connectivity characteristics of resting-state EEG in patients with generalized anxiety disorder

Recent studies have suggested a relationship between abnormal neurophysiological functions and generalized anxiety disorder (GAD). However, studies on its electrophysiological characteristics, such as its power spectrum and functional connectivity are relatively few and scattered than those on other mental disorders (e.g., depression, ADHD, etc.). The present study aims to reveal the multidimensional electrophysiological characteristics of GAD via comparative analysis of electroencephalogram (EEG) data between GAD patients and healthy controls. Specifically, resting-state EEG, with a duration of 10 min, was recorded from 98 GAD patients and 92 healthy control participants. The electrophysiological characteristics, including the power spectrum, alpha asymmetry, and functional connectivity, were extracted and compared between the two groups. The results revealed significantly increased beta-band activity; decreased ipsilateral fronto-temporal and parieto-temporal functional connectivities in the lower frequency bands (theta-beta band); as well as decreased frontal‒parietal and frontal‒occipital connectivities in the higher frequency bands (beta‒gamma band) in GAD patients. Additionally, alpha asymmetry analysis revealed a significantly greater rightward temporal alpha asymmetry in GAD patients. These findings suggest the existence of significant EEG characteristics in patients with GAD, supporting previous conclusions regarding abnormal neurophysiological functions in psychiatric disorders and potentially leading to the identification of biomarkers for clinical diagnosis.

Functional connectivity in burnout syndrome: a resting-state EEG study

Chronic occupational stress is associated with a pronounced decline in emotional and cognitive functioning. Studies on neural mechanisms indicate significant changes in brain activity and changed patterns of event-related potentials in burnout subjects. This study presents an analysis of brain functional connectivity in a resting state, thus providing a deeper understanding of the mechanisms accompanying burnout syndrome. The sample consists of 49 burnout employees and 49 controls, matched by age, gender and occupation (Mage = 36.15, SD = 8.10; 59 women, 39 men). Continuous dense-array EEG data were collected from a 256-channel EEG system. The difference in functional connectivity between burnout and control subjects was tested in the eyes-closed (EC) and eyes-open (EO) conditions using the resting-state paradigm. The results indicate significant differences in brain activity between the burnout and the control groups. The resting-state network of the burnout group is characterized by decreased functional connectivity in frontal and midline areas in the alpha3 sub-band (11-13 Hz) in an eyes-open condition. The most significant effect of decreased connectivity was observed in the right frontal brain area. For the first time, these analyses point to distinctive aspects of functional connectivity within the alpha3 sub-band in burnout syndrome. These findings provide insights into the neurobiological underpinnings of burnout syndrome and its associations with changed resting-state networks. The data on neural characteristics in burnout subjects may help to understand the mechanisms of decline in cognitive function and emotion regulation and to search for adequate methods of treatment.

Effect of cannabis on brain activity in males: Quantitative electroencephalography and its relationship with duration, dosage, and age of onset

OBJECTIVE

Brain function changes as a result of cannabis use. This study examined the brain activity of cannabis users compared to a healthy group and nicotine smokers, focusing on the age of onset, duration of use, and dosage.

METHOD

Demographic and quantitative electroencephalography (QEEG) data of 15 healthy individuals, 20 patients with chronic cannabis use, and 15 nicotine smokers were collected and recorded during the eyes-closed and eyes-open conditions in the resting state. The data were analyzed using MATLAB software and the EEGLAB toolbox.

RESULTS

In the eyes-closed condition, cannabis users exhibited significantly elevated relative theta band power in widespread brain regions compared to both the healthy group and nicotine smokers. They showed decreased relative power in the beta and gamma bands in the parietal and occipital regions when compared to nicotine smokers. In the eyes-open condition, cannabis users displayed increased relative theta band power in widespread brain regions relative to both groups. Additionally, lower relative power in the beta and gamma bands was observed in cannabis users compared to the healthy group in the frontal region, as well as in various brain regions compared to nicotine smokers. A significant relationship was identified between gamma-band power, age of onset, and dosage of cannabis use.

CONCLUSION

These findings suggest that cannabis use leads to changes in brain wave patterns during the resting state, which may be linked to cognitive impairments affecting functions. Understanding these associations is essential for developing effective intervention programs aimed at mitigating cognitive deficits related to cannabis use.

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.

Sleep Fragmentation Modulates the Neurophysiological Correlates of Cognitive Fatigue

Cognitive fatigue (CF) is a critical factor affecting performance and well-being. It can be altered in suboptimal sleep quality conditions, e.g., in patients suffering from obstructive sleep apnea who experience both intermittent hypoxia and sleep fragmentation (SF). Understanding the neurophysiological basis of SF in healthy individuals can provide insights to improve cognitive functioning in disrupted sleep conditions. In this electroencephalographical (EEG) study, we investigated in 16 healthy young participants the impact of experimentally induced SF on the neurophysiological correlates of CF measured before, during, and after practice on the TloadDback, a working memory task tailored to each individual's maximal cognitive resources. The participants spent three consecutive nights in the laboratory two times, once in an undisrupted sleep (UdS) condition and once in an SF condition induced by non-awakening auditory stimulations, counterbalanced and performed the TloadDback task both in a high (HCL) and a low (LCL) cognitive load condition. EEG activity was recorded during wakefulness in the 5 min resting state immediately before and after, as well as during the 16 min of the TloadDback task practice. In the high cognitive load under a sleep-fragmentation (HCL/SF) condition, high beta power increased during the TloadDback, indicating heightened cognitive effort, and the beta and alpha power increased in the post- vs. pre-task resting state, suggesting a relaxation rebound. In the low cognitive load/undisturbed sleep (LCL/UdS) condition, low beta activity increased, suggesting a relaxed focus, as well as mid beta activity associated with active thinking. These findings highlight the dynamic impact of SF on the neurophysiological correlates of CF and underscore the importance of sleep quality and continuity to maintain optimal cognitive functioning.

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.

Reduction of cognitive fatigue and improved performance at a VR-based driving simulator using tRNS

Cognitive fatigue (CF) increases accident risk reducing performance, especially during complex tasks such as driving. We evaluated whether transcranial random noise stimulation (tRNS) could mitigate CF and improve driving performance. In a double-blind study, thirty participants performed a virtual reality truck driving task during real (n = 15) or sham (n = 15) tRNS applied bilaterally on the "anti-fatigue network". They completed two 30-min driving sessions while their driving performances were constantly monitored; heart rate was also monitored to evaluate arousal (Root-Mean-Square of successive R-R difference). tRNS was applied only during the first driving session to evaluate both online and offline stimulation effects. The primary outcome was CF reduction and performance improvement in the second (non-stimulated) driving session. Real tRNS significantly improved driving performances in the second driving session and reduced perceived CF. These results might also lead to the use of tRNS in those neurological disorders characterized by fatigue.

Resting-state electroencephalogram in drug-free subjects with at-risk mental states who later developed psychosis: a low-resolution electromagnetic tomography analysis

Background and objectives

Several studies have reported on the resting-state electroencephalogram (EEG) power in patients with schizophrenia, with a decrease in α (especially α2) and an increase in δ and β1 power compared with healthy control; however, reports on at-risk mental states (ARMS) are few. In this study, we measured the resting-state EEG power in ARMS, and investigated its features and the relationship between the power of the frequency bands and their diagnostic outcomes.

Methods

Patients with ARMS who were not on any psychotropic medication and met the Comprehensive Assessment of At-Risk Mental State criteria were included. Patients who developed psychotic disorders were labeled as the ARMS-P group, while patients with ARMS who were followed up prospectively for more than 2 years and did not develop psychotic disorders were classified as the ARMS-NP group. EEGs were measured in the resting state, and frequencies were analyzed using standardized low-resolution brain electromagnetic tomography (sLORETA). Seven bands (δ, θ, α1, α2, β1-3) underwent analysis. The sLORETA values (current source density [CSD]) were compared between the ARMS-P and ARMS-NP groups. Clinical symptoms were assessed at the time of EEG measurements using the Positive and Negative Syndrome Scale (PANSS).

Results

Of the 39 patients included (25 males, 14 females, 18.8 ± 4.5 years old), eight developed psychotic disorders (ARMS-P). The ARMS-P group exhibited significantly higher CSD in the β1 power within areas of the left middle frontal gyrus (MFG) compared with the ARMS-NP group (best match: X = -35, Y = 25, Z = 50 [MNI coordinates], Area 8, CSD = 2.33, p < 0.05). There was a significant positive correlation between the β1/α ratio of the CSD at left MFG and the Somatic concern score measured by the PANSS.

Discussion

Increased β1 power was observed in the resting EEG before the onset of psychosis and correlated with a symptom. This suggests that resting EEG power may be a useful marker for predicting future conversion to psychosis and clinical symptoms in patients with ARMS.

EEG markers of successful allocentric spatial working memory maintenance in humans

Several brain regions in the frontal, occipital and medial temporal lobes are known to contribute to spatial information processing. In contrast, the oscillatory patterns contributing to allocentric spatial working memory maintenance are poorly understood, especially in humans. Here, we tested twenty-three 21- to 32-year-old and twenty-two 64- to 76-year-old healthy right-handed adults in a real-world, spatial working memory task and recorded electroencephalographic (EEG) activity during the maintenance period. We established criteria for designating recall trials as perfect (no errors) or failed (errors and random search) and identified 8 young and 13 older adults who had at least 1 perfect and 1 failed trial amongst 10 recall trials. Individual alpha frequency-based analyses were used to identify oscillatory patterns during the maintenance period of perfect and failed trials. Spectral scalp topographies showed that individual theta frequency band relative power was stronger in perfect than in failed trials in the frontal midline and posterior regions. Similarly, gamma band (30-40 Hz) relative power was stronger in perfect than in failed trials over the right motor cortex. Exact low-resolution brain electromagnetic tomography in the frequency domain identified greater theta power in perfect than in failed trials in the secondary visual area (BA19) and greater gamma power in perfect than in failed trials in the right supplementary motor area. The findings of this exploratory study suggest that theta oscillations in the occipital lobe and gamma oscillations in the secondary motor cortex (BA6) play a particular role in successful allocentric spatial working memory maintenance.

Electrophysiological impact of mental fatigue on brain activity during a bike task: A wavelet analysis approach

This study explored how mental fatigue affects brain activity during a low-intensity bike task utilising a continuous wavelet transformation in electroencephalography (EEG) analysis. The aim was to examine changes in brain activity potentially linked to central motor commands and to investigate their relationship with ratings of perceived exertion (RPE). In this study, sixteen participants (age: 21 ± 6 y, 7 females, 9 males) underwent one familiarization and two experimental trials in a randomised, blinded, cross-over study design. Participants executed a low-intensity bike task (9 min; 45 rpm; intensity (W): 10 % below aerobic threshold) after performing a mentally fatiguing (individualized 60-min Stroop task) or a control (documentary) task. Physiological (heart rate, EEG) and subjective measures (self-reported feeling of mental fatigue, RPE, cognitive load, motivation) were assessed prior, during and after the bike task. Post-Stroop, self-reported feeling of mental fatigue was higher in the intervention group (EXP) (74 ± 16) than in the control group (CON) (37 ± 17; p < 0.001). No significant differences in RPE during the bike task were observed between conditions. EEG analysis revealed significant differences (p < 0.05) in beta frequency (13-30 Hz) during the bike task, with EXP exhibiting more desynchronization during the pedal push phase and synchronization during the pedal release phase. These results suggest that mental fatigue, confirmed by both subjective and neurophysiological markers, did not significantly impact RPE during the bike task, possibly due to the use of the CR100 scale or absence of a performance outcome. However, EEG data did reveal significant beta band alterations during the task, indicating increased neural effort under mental fatigue. These findings reveal, for the first time, how motor-related brain activity at the motor cortex is impacted during a low-intensity bike task when mentally fatigued.

Unsupervised Hybrid Deep Feature Encoder for Robust Feature Learning from Resting-State EEG Data

EEG classification is a challenging task due to the nonstationary nature of EEG data and the covariance shift induced by cross-subject variance. Recently, various machine learning and deep learning models have been developed to learn robust features for inter-subject EEG classification tasks. However, current existing models are designed based on active task-related EEG, with a lack of investigation into learning robust feature representation from resting-state EEG data. Given the differences in the nature of brain activities captured by resting-state and active task-related EEG, existing models might not be applicable to resting-state EEG. This study proposed an unsupervised hybrid deep feature encoder to learn robust feature representation in resting-state EEG data. It involves using a Variational Autoencoder (VAE) to learn latent feature representation, followed by a further feature selection conducted through a non-task-related sample-level proximity classification using K-means clustering. We demonstrate the efficiency of our proposed model through significantly improved classification accuracies compared to benchmark models, as well as the high between-subject separability manifested by the learned feature representation.

Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach

Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD.

Comparative Analysis of High-Frequency and Low-Frequency Transcutaneous Electrical Stimulation of the Right Median Nerve in the Regression of Clinical and Neurophysiological Manifestations of Generalized Anxiety Disorder

Background/Objectives: The anxiolytic effect of transcutaneous electrical nerve stimulation (TENS) is associated with the activation of endogenous inhibitory mechanisms in the central nervous system. Both low-frequency, high-amplitude TENS (LF-TENS) and high-frequency, low-amplitude TENS (HF-TENS) are capable of activating opioid, GABA, serotonin, muscarinic, and cannabinoid receptors. However, there has been no comparative analysis of the effectiveness of HF-TENS and LF-TENS in the treatment of GAD. The purpose of our research was to study the effectiveness of direct HF-TENS and LF-TENS of the right median nerve in the treatment of patients with GAD compared with sham TENS. Methods: The effectiveness of direct HF-TENS and LF-TENS of the right median nerve in the treatment of GAD was studied using Generalized Anxiety Disorder 7-item scale (GAD-7) and the Hamilton Anxiety Rating Scale (HAM-A). 40 patients underwent sham TENS, 40 patients passed HF-TENS (50 Hz-50 μs-sensory response) and 41 patients completed LF -TENS (1 Hz-200 μs-motor response) for 30 days daily. After completion of treatment, half of the patients received weekly maintenance therapy for 6 months. Electroencephalography was performed before and after treatment. Results: Our study showed that a significant reduction in the clinical symptoms of GAD as assessed by GAD-7 and HAM-A was observed after HF-TENS and LF-TENS by an average of 42.4%, and after sham stimulation only by 13.5% for at least 2 months after the end of treatment. However, LF-TENS turned out to be superior in effectiveness to HF-TENS by 51% and only on electroencephalography leads to an increase in PSD for the alpha rhythm in the occipital regions by 24% and a decrease in PSD for the beta I rhythm in the temporal and frontal regions by 28%. The prolonged effect of HF-TENS and LF-TENS was maintained without negative dynamics when TENS treatment was continued weekly throughout the entire six-month observation period. Conclusions: A prolonged anxiolytic effect of direct TENS of the right median nerve has been proven with greater regression of clinical and neurophysiological manifestations of GAD after LF-TENS compared to HF-TENS. Minimal side effects, low cost, safety, and simplicity of TENS procedures are appropriate as a home treatment modality.

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.

Fatigue in children using motor imagery and P300 brain-computer interfaces

BACKGROUND

Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children.

METHODS

Thirty-seven typically-developing school-aged children were recruited to a prospective, crossover study. Participants attended three sessions: (A) motor imagery-BCI, (B) visual P300-BCI, and (C) video viewing (control). The motor imagery task involved an imagined left- or right-hand squeeze. The P300 task involved attending to one square on a 3 × 3 grid during a random single flash sequence. Each paradigm had respective calibration periods and a similar visual counting game. Primary outcomes were self-reported fatigue and the power of the EEG alpha band both collected during resting-state periods pre- and post-task. Self-reported fatigue was measured using a 10-point visual analog scale. EEG alpha band power was calculated as the integrated power spectral density from 8 to 12 Hz of the EEG spectrum.

RESULTS

Thirty-two children completed the protocol (age range 7-16, 63% female). Self-reported fatigue and EEG alpha band power increased across all sessions (F(1,155) = 33.9, p < 0.001; F = 5.0(1,149), p = 0.027 respectively). No differences in fatigue development were observed between session types. There was no correlation between self-reported fatigue and EEG alpha band power change. BCI performance varied between participants and paradigms as expected but was not associated with self-reported fatigue or EEG alpha band power.

CONCLUSION

Short periods (30-mintues) of BCI use can increase self-reported fatigue and EEG alpha band power to a similar degree in children performing motor imagery and P300 BCI paradigms. Performance was not associated with our measures of fatigue; the impact of fatigue on useability and enjoyment is unclear. Our results reflect the variability of fatigue and the BCI experience more broadly in children and warrant further investigation.

Cognitive Impairment in the Post-Acute Phases of COVID-19 and Mechanisms: An Introduction and Narrative Review

Cognitive impairment is a primary manifestation of neurological symptoms associated with COVID-19 and may occur after disease resolution. Although cognitive impairment has been extensively reported in the literature, its duration and rate of remission remain controversial. This study discusses the various factors that influence cognitive impairment, including demographic characteristics, genetics, as well as disease course and severity. Furthermore, imaging and laboratory data have suggested various associations with cognitive impairment, most notably changes in EEG patterns, PET imaging, and serum markers. Some findings suggest similarities and potential links between COVID-related cognitive impairment and Alzheimer's disease. Moreover, this study reviews the various mechanisms proposed to explain the development of cognitive impairment in COVID-19, including cytokine storm, damage to the blood-brain barrier, compromise of small vessel integrity, hypoxic conditions, and immune dysregulation.

Driving fatigue detection based on brain source activity and ARMA model

Fatigue among drivers is a significant issue in society, and according to organizational reports, it substantially contributes to accidents. So accurate fatigue detection in drivers plays a crucial role in reducing the number of people fatalities or injured resulting from accidents. Several methods are proposed for fatigue driver recognition among which electroencephalography (EEG) is one. This paper proposed a method for fatigue recognition by EEG signals with extracted features from source and sensor spaces. The proposed method starts with preprocessing by applying filtering and artifact rejection. Then source localization methods are applied to EEG signals for active source extraction. A multivariate autoregressive (MVAR) model is fitted to selected sources, and a dual Kalman filter is applied to estimate the source activity and their relationships. Then multivariate autoregressive moving average (ARMA) is fitted between EEG and source activity signals. Features are extracted from model parameters, source relationship matrix, and wavelet transform of EEG and source activity signals. The novelty of this approach is the use of ARMA model between source activities (as input) and EEG signals (as output) and feature extraction from source relations. Relevant features are selected using a combination of RelifF and neighborhood component analysis (NCA) methods. Three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayesian (NB) classifiers, are employed to classify drivers. To improve performance, the final label for fatigue detection is calculated by combining these classifiers using the voting method. The results demonstrate that the proposed method accurately recognizes and classifies fatigued drivers with the ensemble classifiers in comparison with other methods.

Mental fatigue recognition study based on 1D convolutional neural network and short-term ECG signals

BACKGROUND

Mental fatigue has become a non-negligible health problem in modern life, as well as one of the important causes of social transportation, production and life accidents.

OBJECTIVE

Fatigue detection based on traditional machine learning requires manual and tedious feature extraction and feature selection engineering, which is inefficient, poor in real-time, and the recognition accuracy needs to be improved. In order to recognize daily mental fatigue level more accurately and in real time, this paper proposes a mental fatigue recognition model based on 1D Convolutional Neural Network (1D-CNN), which inputs 1D raw ECG sequences of 5 s duration into the model, and can directly output the predicted fatigue level labels.

METHODS

The fatigue dataset was constructed by collecting the ECG signals of 22 subjects at three time periods: 9:00-11:00 a.m., 14:00-16:00 p.m., and 19:00-21:00 p.m., and then inputted into the 19-layer 1D-CNN model constructed in the present study for the classification of mental fatigue in three grades.

RESULTS

The results showed that the model was able to recognize the fatigue levels effectively, and its accuracy, precision, recall, and F1 score reached 98.44%, 98.47%, 98.41%, and 98.44%, respectively.

CONCLUSION

This study further improves the accuracy and real-time performance of recognizing multi-level mental fatigue based on electrocardiography, and provides theoretical support for real-time fatigue monitoring in daily life.

Quantifying Fatigue Using Electrophysiological Techniques and Non-invasive Brain Stimulation in People With Multiple Sclerosis- A Review and Discussion

Objective: The purpose of this literature review article is to provide a synthesis of recent research focused on the use of 3 techniques to evaluate MS-related fatigue: electroencephalography [EEG], transcranial direct-current stimulation (tDSC), and transcranial- magnetic stimulation (TMS). Method: We performed a literature search in the Cumulative Index to Nursing and Allied Health Literature (CINAHL, EBSCOhost), MEDLINE (OVID), APA PsycInfo (OVID), Scopus (Elsevier), and Web of Science (Clarivate) databases, limited to 2015 and after. Results: Our review revealed that fatigue in MS patients can be quantified and predicted using electrophysiological techniques. Such techniques, which yield objective data, are historically assessed in relation to subjective data, or perceived fatigue. We identified studies using EEG, TMS, and/or tDCS to study fatigue in people with MS. In total, 220 records were identified with 19 studies meeting inclusion criteria. Quality appraisal revealed that the level of evidence was generally graded "good". Conclusions: Despite the heterogenous nature of reviewed the studies and selected the varied self-report fatigue measures, our literature synthesis suggests promise for the use of EEG, TMS, and/or tDCS approaches in more accurately assessing fatigue in people with MS. Further research is needed in this arena.

An Exploratory Study of the Effect of Tinnitus on Listening Effort Using EEG and Pupillometry

OBJECTIVE

Previous behavioral studies on listening effort in tinnitus patients did not consider extended high-frequency hearing thresholds and had conflicting results. This inconsistency may be related that listening effort is not evaluated by the central nervous system (CNS) and autonomic nervous system (ANS), which are directly related to tinnitus pathophysiology. This study matches hearing thresholds at all frequencies, including the extended high-frequency and reduces hearing loss to objectively evaluate listening effort over the CNS and ANS simultaneously in tinnitus patients.

STUDY DESIGN

Case-control study.

SETTING

University hospital.

METHODS

Sixteen chronic tinnitus patients and 23 matched healthy controls having normal pure-tone averages with symmetrical hearing thresholds were included. Subjects were evaluated with 0.125 to 20 kHz pure-tone audiometry, Montreal Cognitive Assessment Test (MoCA), Tinnitus Handicap Inventory (THI), Visual Analog Scale (VAS), electroencephalography (EEG), and pupillometry.

RESULTS

Pupil dilation and EEG alpha band in the "coding" phase of the sentence presented in tinnitus patients was less than in the control group (p < .05). VAS score was higher in the tinnitus group (p < .01). Also, there was no statistically significant relationship between EEG and pupillometry components and THI or MoCA (p > .05).

CONCLUSION

This study suggests that tinnitus patients may need to make an extra effort to listen. Also, pupillometry may not be sufficiently reliable to assess listening effort in ANS-related pathologies. Considering the possible listening difficulties in tinnitus patients, reducing the listening difficulties, especially in noisy environments, can be added to the goals of tinnitus therapy protocols.

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.

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.

Resting state electroencephalography power correlates with individual differences in implicit sequence learning

Neuroimaging resting state paradigms have revealed synchronised oscillatory activity is present even in the absence of completing a task or mental operation. One function of this neural activity is likely to optimise the brain's sensitivity to forthcoming information that, in turn, likely promotes subsequent learning and memory outcomes. The current study investigated whether this extends to implicit forms of learning. A total of 85 healthy adults participated in the study. Resting state electroencephalography was first acquired from participants before they completed a serial reaction time task. On this task, participants implicitly learnt a visuospatial-motor sequence. Permutation testing revealed a negative correlation between implicit sequence learning and resting state power in the upper theta band (6-7 Hz). That is, lower levels of resting state power in this frequency range were associated with superior levels of implicit sequence learning. This association was observed at midline-frontal, right-frontal and left-posterior electrodes. Oscillatory activity in the upper theta band supports a range of top-down processes including attention, inhibitory control and working memory, perhaps just for visuospatial information. Our results may be indicating that disengaging theta-supported top-down attentional processes improves implicit learning of visuospatial-motor information that is embedded in sensory input. This may occur because the brain's sensitivity to this type of information is optimally achieved when learning is driven by bottom-up processes. Moreover, the results of this study further demonstrate that resting state synchronised brain activity influences subsequent learning and memory.

The measurement of mental fatigue following an overnight on-call duty among doctors using electroencephalogram

This study aimed to measure the spectral power differences in the brain rhythms among a group of hospital doctors before and after an overnight on-call duty. Thirty-two healthy doctors who performed regular on-call duty in a tertiary hospital in Sarawak, Malaysia were voluntarily recruited into this study. All participants were interviewed to collect relevant background information, followed by a self-administered questionnaire using Chalder Fatigue Scale and electroencephalogram test before and after an overnight on-call duty. The average overnight sleep duration during the on-call period was 2.2 hours (p<0.001, significantly shorter than usual sleep duration) among the participants. The mean (SD) Chalder Fatigue Scale score of the participants were 10.8 (5.3) before on-call and 18.4 (6.6) after on-call (p-value < 0.001). The theta rhythm showed significant increase in spectral power globally after an overnight on-call duty, especially when measured at eye closure. In contrast, the alpha and beta rhythms showed reduction in spectral power, significantly at temporal region, at eye closure, following an overnight on-call duty. These effects are more statistically significant when we derived the respective relative theta, alpha, and beta values. The finding of this study could be useful for development of electroencephalogram screening tool to detect mental fatigue.

A Quantitative Investigation of Mental Fatigue Elicited during Motor Imagery Practice: Selective Effects on Maximal Force Performance and Imagery Ability

In the present study, we examined the development of mental fatigue during the kinesthetic motor imagery (MI) of isometric force contractions performed with the dominant upper limb. Participants (n = 24) underwent four blocks of 20 MI trials of isometric contractions at 20% of the maximal voluntary contraction threshold (20% MVC_MI) and 20 MI trials of maximal isometric contractions (100% MVC_MI). Mental fatigue was assessed after each block using a visual analogue scale (VAS). We assessed maximal isometric force before, during and after MI sessions. We also assessed MI ability from self-report ratings and skin conductance recordings. Results showed a logarithmic pattern of increase in mental fatigue over the course of MI, which was superior during 100% MVC_MI. Unexpectedly, maximal force improved during 100% MVC_MI between the 1st and 2nd evaluations but remained unchanged during 20% MVC_MI. MI ease and vividness improved during 100% MVC_MI, with a positive association between phasic skin conductance and VAS mental fatigue scores. Conversely, subjective measures revealed decreased MI ability during 20% MVC_MI. Mental fatigue did not hamper the priming effects of MI on maximal force performance, nor MI's ability for tasks involving high physical demands. By contrast, mental fatigue impaired MI vividness and elicited boredom effects in the case of motor tasks with low physical demands.

Source-Based EEG Neurofeedback for Sustained Motor Imagery of a Single Leg

The aim of the study was to test the feasibility of visual-neurofeedback-guided motor imagery (MI) of the dominant leg, based on source analysis with real-time sLORETA derived from 44 EEG channels. Ten able-bodied participants took part in two sessions: session 1 sustained MI without feedback and session 2 sustained MI of a single leg with neurofeedback. MI was performed in 20 s on and 20 s off intervals to mimic functional magnetic resonance imaging. Neurofeedback in the form of a cortical slice presenting the motor cortex was provided from a frequency band with the strongest activity during real movements. The sLORETA processing delay was 250 ms. Session 1 resulted in bilateral/contralateral activity in the 8-15 Hz band dominantly over the prefrontal cortex while session 2 resulted in ipsi/bilateral activity over the primary motor cortex, covering similar areas as during motor execution. Different frequency bands and spatial distributions in sessions with and without neurofeedback may reflect different motor strategies, most notably a larger proprioception in session 1 and operant conditioning in session 2. Single-leg MI might be used in the early phases of rehabilitation of stroke patients. Simpler visual feedback and motor cueing rather than sustained MI might further increase the intensity of cortical activation.

A serial mediation model of social media addiction and college students' academic engagement: the role of sleep quality and fatigue

BACKGROUND

It has been documented that social media addiction (SMA) has a detrimental effect on college students' academic engagement. However, the mechanisms underlying this association are not well understood. This study aimed to determine the serial mediation effects of sleep quality and fatigue on the relationship between SMA and academic engagement among college students.

METHODS

A cross-sectional survey was conducted with 2661 college students (43.3% males, mean age = 19.97 years). The participants completed the Bergen Social Media Addiction Scale, the Utrecht Student Work Engagement Scale for Students, the Pittsburgh Sleep Quality Index, and the Fatigue Assessment Scale. The serial mediation effects were examined using Model 6 in the Hayes' PROCESS macro for SPSS.

RESULTS

The results showed that SMA among college students had a direct negative relationship with their academic engagement (Effect = - 0.051, 95% CI: -0.087 to - 0.015). In addition, sleep quality and fatigue mediated the relationship between SMA and academic engagement both independent and serially, with the independent mediation effect of sleep quality being - 0.031 (95% CI: -0.048 to - 0.016), the independent mediation effect of fatigue being - 0.109 (95% CI: -0.133 to - 0.088), and the serial mediation effect of sleep quality and fatigue being - 0.080 (95% CI: -0.095 to - 0.066). The total indirect effect of the three mediation paths was 80.9%.

CONCLUSIONS

Decreased academic engagement caused by SMA can be aggravated by poor sleep quality and fatigue. Strengthening supervision and intervention in social media use among college students, supplemented by attention to psychosomatic health, including sleep quality and fatigue could promote their engagement in academic work.

Surgical stress: the muscle and cognitive demands of robotic and laparoscopic surgery

Introduction

Surgeons are among the most at-risk professionals for work-related musculoskeletal decline and experience high mental demands. This study examined the electromyographic (EMG) and electroencephalographic (EEG) activities of surgeons during surgery.

Methods

Surgeons who performed live laparoscopic (LS) and robotic (RS) surgeries underwent EMG and EEG measurements. Wireless EMG was used to measure muscle activation in four muscle groups bilaterally (biceps brachii, deltoid, upper trapezius, and latissimus dorsi), and an 8-channel wireless EEG device was used to measure cognitive demand. EMG and EEG recordings were completed simultaneously during (i) noncritical bowel dissection, (ii) critical vessel dissection, and (iii) dissection after vessel control. Robust ANOVA was used to compare the %MVC_RMS and alpha power between LS and RS.

Results

Thirteen male surgeons performed 26 laparoscopic surgeries (LS) and 28 robotic surgeries (RS). Muscle activation was significantly higher in the right deltoid (p = 0.006), upper trapezius (left, p = 0.041; right, p = 0.032), and latissimus dorsi (left, p = 0.003; right, p = 0.014) muscles in the LS group. There was greater muscle activation in the right biceps than in the left biceps in both surgical modalities (both p = 0.0001). There was a significant effect of the time of surgery on the EEG activity (p <0.0001). A significantly greater cognitive demand was observed in the RS than in the LS with alpha, beta, theta, delta, and gamma (p = 0.002 - p <0.0001).

Conclusion

These data suggest greater muscle demands in laparoscopic surgery, but greater cognitive demands in robotic surgery.

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.

Alleviation of Cognitive and Physical Fatigue with Enzymatic Porcine Placenta Hydrolysate Intake through Reducing Oxidative Stress and Inflammation in Intensely Exercised Rats

Intense exercise is reported to induce physical and cognitive fatigue, but few studies have focused on treatments to alleviate fatigue. We hypothesized that the oral supplementation of enzymatic porcine placenta hydrolysate (EPPH) prepared using protease enzymes could alleviate exercise-induced fatigue in an animal model. The objectives of the study were to examine the hypothesis and the action mechanism of EPPH in relieving physical and cognitive fatigue. Fifty male Sprague−Dawley rats aged 8 weeks (body weight: 201 g) were classified into five groups, and rats in each group were given oral distilled water, EPPH (5 mg nitrogen/mL) at doses of 0.08, 0.16, or 0.31 mL/kg body weight (BW)/day, or glutathione (100 mg/kg BW/day) by a feeding needle for 5 weeks, which were named as the control, L-EPPH, M-EPPH, H-EPPH, or positive-control groups, respectively. Ten additional rats had no intense exercise with water administration and were designated as the no-exercise group. After 2 weeks, the rats were subjected to intense exercise and forced swimming trial for 30 min once per week for an additional 4 weeks. At 5 min after the intense exercise, lactate concentrations and lactate dehydrogenase (LDH) activity in the serum and the gastrocnemius muscle were higher in the control group, whereas M-EPPH and H-EPPH treatments suppressed the increase better than in the positive-control (p < 0.05). Intense exercise decreased glycogen content in the liver and gastrocnemius muscle, and M-EPPH and H-EPPH inhibited the decrement (p < 0.05). Moreover, lipid peroxide contents in the gastrocnemius muscle and liver were higher in the control group than in the M-EPPH, H-EPPH, positive-control, and no-exercise groups (p < 0.05). However, antioxidant enzyme activities such as superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) were opposite to the lipid peroxide contents. Hypothalamic corticosterone and hippocampal mRNA expressions of tumor necrosis factor (TNF)-α and IL-1β were higher. However, hippocampal brain-derived neurotrophic factor (BDNF) mRNA expression and protein contents were lower in the control group than in the positive-control group. M-EPPH, H-EPPH, and positive-control suppressed the changes via activating hippocampal cAMP response element-binding protein phosphorylation, and H-EPPH showed better activity than in the positive-control (p < 0.05). In conclusion, EPPH (0.16−0.31 mL/kg BW) intake reduced exercise-induced physical and cognitive fatigue in rats and could potentially be developed as a therapeutic agent for relieving fatigue in humans.

Effects of Mental Fatigue on Reaction Time in Sportsmen

AIM

Mental fatigue (MF) has been defined as a psychobiological state commonly caused by prolonged periods of demanding cognitive activity. However, the differences between women and men in their reaction times (RTs) to visual stimuli due to mental fatigue remain largely unknown. We compare the differences in RT and heart rate after an acute intervention of mental fatigue between male and female athletes.

MATERIALS AND METHODS

For this aim, 64 participants (age 31.7 ± 6.2 y) performed a routine of 15 min of the Stroop test (PsyTool), with 600 tasks and five different colors. Their heart rate (HR) was registered before, during, and one, three, and five minutes after the Stroop test. Meanwhile, the RT was evaluated before and after the Stroop test. A general linear mixed model (GLMM) and a Bonferroni post hoc test were used to compare the HR between the conditions and an ANOVA two-way analysis was used to compare the values pre-/post-Stroop test. (α = 0.05).

RESULTS

The GLMM for HR showed an effect on the time (p < 0.001) and the time × group interaction (p = 0.004). The RT was significantly increased pre- to post-Stroop test (p < 0.05); however, there was no difference between the pre- and post-HR measurements (p = 1.000) and the measurements one (p = 0.559), three (p = 1.000) and five (p = 1.000) min after the Stroop test.

CONCLUSION

The present findings suggest that the parasympathetic branch of the autonomous nervous system which functions as a relaxation system tends to be activated under increasing mental fatigue, with a decreased performance (RT) similarly in men and women. Therefore, athletes could use MF induced during training to improve the time delay related to motor tasks.

Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue

Mental fatigue is a widely studied topic on account of its serious negative effects. But how the neural mechanism of task switching before and after mental fatigue remains a question. To this end, this study aims to use brain functional network features to explore the answer to this question. Specifically, task-state EEG signals were recorded from 20 participants. The tasks include a 400-s 2-back-task (2-BT), followed by a 6480-s of mental arithmetic task (MAT), and then a 400-s 2-BT. Network features and functional connections were extracted and analyzed based on the selected task switching states, referred to from Pre_2-BT to Pre_MAT before mental fatigue and from Post_MAT to Post_2-BT after mental fatigue. The results showed that mental fatigue has been successfully induced by long-term MAT based on the significant changes in network characteristics and the high classification accuracy of 98% obtained with Support Vector Machines (SVM) between Pre_2-BT and Post_2-BT. when the task switched from Pre_2-BT to Pre_MAT, delta and beta rhythms exhibited significant changes among all network features and the selected functional connections showed an enhanced trend. As for the task switched from Post_MAT to Post_2-BT, the network features and selected functional connectivity of beta rhythm were opposite to the trend of task switching before mental fatigue. Our findings provide new insights to understand the neural mechanism of the brain in the process of task switching and indicate that the network features and functional connections of beta rhythm can be used as neural markers for task switching before and after mental fatigue.

Review of electroencephalography signals approaches for mental stress assessment

The innovation of electroencephalography (EEG) more than a century ago supports the technique to assess brain structure and function in clinical health and research applications. The EEG signals were identified on their frequency ranges as delta (from 0.5 to 4 Hz), theta (from 4 to 7 Hz), alpha (from 8 to 12 Hz), beta (from 16 to 31 Hz), and gamma (from 36 to 90 Hz). Stress is a sense of emotional tension caused by several life events. For example, worrying about something, being under pressure, and facing significant challenges are causes of stress. The human body is affected by stress in various ways. It promotes inflammation, which affects cardiac health. The autonomic nervous system is activated during mental stress. Posttraumatic stress disorder and Alzheimer's disease are common brain stress disorders. Several methods have been used previously to identify stress, for instance, magnetic resonance imaging, single-photon emission computed tomography and EEG. The EEG identifies the electrical activity in the human brain by applying small electrodes positioned on the scalp of the brain. It is a useful non-invasive method and collects feedback from stress hormones. In addition, it can serve as a reliable tool for measuring stress. Furthermore, evaluating human stress in real-time is complicated and challenging. This review demonstrates the power of frequency bands for mental stress and the behaviors of frequency bands based on medical and research experiencebands based on medical and research experience.

The effect of job satisfaction regulating workload on miners' unsafe state

Miners’ unsafe behavior is the main cause of accidents in coal mines, and unsafe state have an important influence on unsafe behavior among miners. To minimize accidents from the source of accident chain, we evaluated the impact of workload on miners’ unsafe state. It is important for coal enterprises to monitor miners’ unsafe state and to prevent unsafe accidents. Workload is divided into two dimensions: work time and work demand. Meanwhile, we introduced job satisfaction as a moderating variable. Through empirical research methods, first-line employees from two coal mines in China were enrolled in the questionnaire survey. Regression analysis was used to verify the impact of workload and its various dimensions, job satisfaction, and miners’ unsafe state. We found that workload, work time and work demand have significant positive effects on miners’ unsafe state. Job satisfaction plays a moderating effect in the relationship between workload and miners’ unsafe state. To some extent, a higher job satisfaction was associated with reduced workload, reduced occurrence of miners’ unsafe state and minimal incidences of unsafe accidents. On this basis, measures were proposed to improve miners’ unsafe state in terms of workload and job satisfaction. This study informs the establishment of effective intervention measures to monitor miners’ unsafe state and is also beneficial to the improvement of coal mine safety.

Comparison of QEEG Findings before and after Onset of Post-COVID-19 Brain Fog Symptoms

Previous research and clinical reports have shown that some individuals after COVID-19 infection may demonstrate symptoms of so-called brain fog, manifested by cognitive impairment and disorganization in behavior. Meanwhile, in several other conditions, related to intellectual function, a specific pattern of changes in electric brain activity, as recorded by quantitative electroencephalography (QEEG) has been documented. We hypothesized, that in post-COVID brain fog, the subjective complaints may be accompanied by objective changes in the QEEG profile. In order to test this hypothesis, we have performed an exploratory study on the academic staff of our University with previous records of QEEG originating in the pre-COVID-19 era. Among them, 20 subjects who revealed neurological problems in the cognitive sphere (confirmed as covid fog/brain fog by a clinical specialist) after COVID-19 infection were identified. In those individuals, QEEG was performed. We observed, that opposite to baseline QEEG records, increased Theta and Alpha activity, as well as more intensive sensimotor rhythm (SMR) in C4 (right hemisphere) in relation to C3 (left hemisphere). Moreover, a visible increase in Beta 2 in relation to SMR in both hemispheres could be documented. Summarizing, we could demonstrate a clear change in QEEG activity patterns in individuals previously not affected by COVID-19 and now suffering from post-COVID-19 brain fog. These preliminary results warrant further interest in delineating their background. Here, both neuroinflammation and psychological stress, related to Sars-CoV2-infection may be considered. Based on our observation, the relevance of QEEG examination as a supportive tool for post-COVID clinical workup and for monitoring the treatment effects is also to be explored.

Single-Source to Single-Target Cross-Subject Motor Imagery Classification Based on Multisubdomain Adaptation Network

In the electroencephalography (EEG) based cross-subject motor imagery (MI) classification task, the device and subject problems can cause the time-related data distribution shift problem. In a single-source to single-target (STS) MI classification task, such a shift problem will certainly provoke an increase in the overall data distribution difference between the source and target domains, giving rise to poor classification accuracy. In this paper, a novel multi-subdomain adaptation method (MSDAN) is proposed to solve the shift problem and improve the classification accuracy of the traditional approaches. In the proposed MSDAN, the adaptation losses in both class-related and time-related subdomains (that are divided by different data labels and session labels) are obtained by measuring the distribution differences between the source and target subdomains. Then, the adaptation and classification losses in the loss function of MSDAN are minimized concurrently. To illustrate the application value of the proposed method, our method is applied to solve the STS MI classification task about data analysis with respect to the brain-computer interface (BCI) competition III-IVa dataset. The resultant experiment results demonstrate that compared with other well-known domain adaptation and deep learning methods, the proposed method is capable of solving the time-related data distribution problem at higher classification accuracy.

Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework

Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized anxiety disorder (GAD). To this end, this study intends to develop an analysis framework for automatic GAD detection through incorporating multidimensional EEG feature extraction and machine learning techniques. Specifically, resting-state EEG signals with a duration of 10 min were obtained from 45 patients with GAD and 36 healthy controls (HC). Then, an analysis framework of multidimensional EEG characteristics (including univariate power spectral density (PSD) and fuzzy entropy (FE), and multivariate functional connectivity (FC), which can decode the EEG information from three different dimensions) were introduced for extracting aberrated multidimensional EEG features via statistical inter-group comparisons. These aberrated features were subsequently fused and fed into three previously validated machine learning methods to evaluate classification performance for automatic patient detection. We showed that patients exhibited a significant increase in beta rhythm and decrease in alpha1 rhythm of PSD, together with the reduced long-range FC between frontal and other brain areas in all frequency bands. Moreover, these aberrated features contributed to a very good classification performance with 97.83 ± 0.40% of accuracy, 97.55 ± 0.31% of sensitivity, 97.78 ± 0.36% of specificity, and 97.95 ± 0.17% of F1. These findings corroborate previous hypothesis of disconnectivity in psychiatric disorders and further shed light on distribution patterns of aberrant spatio-spectral EEG characteristics, which may lead to potential application of automatic diagnosis of GAD.

Association of Fatigue With Sleep Duration and Bedtime During the Third Trimester

PURPOSE

To investigate the association between fatigue and sleep habits of pregnant women to further explore the effect of sleep duration and bedtime on fatigue during the third trimester.

MATERIALS AND METHODS

A total of 465 Chinese Han pregnant women in the third trimester (after 28 weeks) with a singleton gestation were recruited. Sleep habits (such as bedtime, sleep onset latency, and night sleep duration) and the 14-item Fatigue Scale scores (FS-14, used to assess fatigue) were collected.

RESULTS

The effects of sleep duration and bedtime on FS-14 physical and total scores were significant. FS-14 physical scores and total scores of the participants in the group of sleep before 23 o'clock (SBC) of short sleep duration (<7 h) were significantly higher as compared to the participants in the group of SBC of normal sleep duration, and those of the participants in the group of SBC of normal sleep duration were significantly lower than the participants in the group of sleep after 23 o'clock of normal sleep duration. There were negative correlations of sleep duration with FS-14 physical score and total score in the SBC of short sleep duration group.

CONCLUSION

Sleep less than 7 h or bedtime after 23 o'clock was associated with increased fatigue levels of pregnant women in the third trimester. Therefore, it is necessary to develop good sleep habits (enough sleep duration and early bedtime) to keep fatigue at a low level for pregnant women in the third trimester.

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.

Pareidolia in a Built Environment as a Complex Phenomenological Ambiguous Stimuli

Pareidolia is a kind of misperception caused by meaningless, ambiguous stimuli perceived with meaning. Pareidolia in a built environment may trigger the emotions of residents, and the most frequently observed pareidolian images are human faces. Through a pilot experiment and an in-depth questionnaire survey, this research aims to compare built environmental pareidolian phenomena at different time points (6 a.m., 12 p.m., 2 a.m.) and to determine people’s sensitivity and reactions towards pareidolia in the built environment. Our findings indicate that the differences in stress level do not influence the sensitivity and reactions towards pareidolia in the built environment; however, age does, and the age of 40 seems to be a watershed. Females are more likely to identify pareidolian faces than males. Smokers, topers, and long-term medicine users are more sensitive to pareidolian images in the built environment. An unexpected finding is that most pareidolian images in built environments are much more easily detected in the early morning and at midnight but remain much less able to be perceived at midday. The results help architects better understand people’s reactions to pareidolia in the built environment, thus allowing them to decide whether to incorporate it appropriately or avoid it consciously in building design.

Resilience-Building for Mental Health among Early Childhood Educators: A Systematic Review and Pilot-Study towards an EEG-VR Resilience Building Intervention

Resilience is a key factor that reflects a teacher's ability to utilize their emotional resources and working skills to provide high-quality teaching to children. Resilience-building interventions aim to promote positive psychological functioning and well-being. However, there is lack of evidence on whether these interventions improve the well-being or mental health of teachers in early childhood education (ECE) settings. This review examined the overall effectiveness of resilience-building interventions conducted on teachers working in the ECE field. A systematic approach is used to identify relevant studies that focus on resilience-building in countering work stress among early childhood educators. Findings from this review observed a preference of group approaches and varying durations of interventions. This review highlights the challenges of the group approach which can lead to lengthy interventions and attrition amongst participants. In addition to the concerns regarding response bias from self-report questionnaires, there is also a lack of physiological measures used to evaluate effects on mental health. The large efforts by 11 studies to integrate multiple centres into their intervention and the centre-based assessment performed by four studies highlight the need for a centre-focused approach to build resilience among teachers from various ECE centres. A pilot study is conducted to evaluate the feasibility of an integrated electroencephalography-virtual reality (EEG-VR) approach in building resilience in teachers, where the frontal brain activity can be monitored during a virtual classroom task. Overall, the findings of this review propose the integration of physiological measures to monitor changes in mental health throughout the resilience-building intervention and the use of VR as a tool to design a unique virtual environment.

Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review

The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual's interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user's attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.