Regional brain wave activity changes associated with fatigue

Mental Fatigue and Soccer: Current Knowledge and Future Directions

Fatigue is a complex state with multiple physiological and psychological origins. However, fatigue in soccer has traditionally been investigated from a physiological perspective, with little emphasis on the cognitive demands of competition. These cognitive demands may induce mental fatigue, which could contribute to the fatigue-related performance decrements observed during and after soccer matches. Recent research investigating the relationship between mental fatigue and soccer-specific performance supports this suggestion. This leading article provides an overview of the research in this emerging field, outlining the impact of mental fatigue on soccer-specific physical, technical, decision-making, and tactical performances. The second half of this review provides directions for future research in response to the limitations of the existing research. Emphasis is placed on translating the current body of knowledge into practical applications and developing a greater understanding of the mechanisms underpinning the negative impact of mental fatigue on soccer performance. A conceptual model is presented to help direct this future research.

Functional brain network analysis reveals time-on-task related performance decline

Because of the undesired consequences, particularly seen in deteriorated performance in real-word workspace, continuous efforts have been made to understand time-on-task (TOT) related mental fatigue. However, our understanding of the underlying neural mechanism of TOT is still rudimentary. In this study, EEG signals were recorded from 26 subjects undergoing a 20-min mentally-demanding psychomotor vigilance test. Instead of a mere two-point comparison (i.e., fatigue vs. vigilant), behaviour and EEG data were divided into 4 quartiles for better revealing the progression of TOT effect. We then employed advanced graph theoretical approach to quantify TOT effect in terms of global and local reorganisation of EEG functional connectivity within the lower alpha (8-10 Hz) band. Interestingly, we found a development trend towards disintegrated network topology with the TOT effect, as seen in significantly increased characteristic path length and reduced small-worldness. Moreover, we found TOT-related reduced local property of interconnectivity in left frontal and central areas with an increased local property in right parietal areas. These findings augment our understanding of how the brain reorganises following the accumulation of prolonged task and demonstrate the feasibility of using network metrics as neural biomarkers for mental fatigue assessment.

Brain mechanisms that underlie music interventions in the exercise domain

In this chapter we review recent work from the realms of neuroscience and neuropsychology to explore the brain mechanisms that underlie the effects of music on exercise. We begin with an examination of the technique of electroencephalography (EEG), which has proven popular with researchers in this domain. We go on to appraise work conducted with the use of functional magnetic resonance imaging (fMRI) and then, looking more toward the future, we consider the application of functional near-infrared spectroscopy (fNIRS) to study brain hemodynamics. The experimental findings expounded herein indicate that music has the potential to guide attention toward environmental sensory cues and prevent internal, fatigue-related signals from entering focal awareness. The brain mechanisms underlying such effects are primarily associated with the downregulation of theta waves across the cortex surface, reduction of communication among somatosensory regions, and increased activation of the left inferior frontal gyrus. Taken holistically, research in this subfield of exercise psychology demonstrates a vibrant and reflexive matrix of attentional, emotional, behavioral, physiological, and psychophysiological responses to music across a variety of exercise modalities and intensities. The emergent hypotheses that we propose can be used to frame future research efforts.

The impact of self-administered acupressure on sleep quality and fatigue among patients with migraine: A randomized controlled trial

BACKGROUND AND AIM

Poor sleep and fatigue are among the most common complaints of patients with migraine. These problems can lead to different negative consequences such as headaches. This study aimed to examine the impacts of self-administered acupressure on sleep quality and fatigue among patients with migraine.

METHODS

This double-blind randomized controlled trial was conducted in 2016 on 76 patients who suffered from migraine without aura. Patients were conveniently selected from the neurology clinic of Valiasr (PBUH) teaching hospital and randomly allocated to either an acupressure (n = 38) or a sham acupressure group (n = 38) group. Data collection instruments were a demographic questionnaire, Pittsburg Sleep Quality Index, and Fatigue Severity Scale. Patients in the acupressure and the sham acupressure groups were trained to apply acupressure on respectively acupoints and sham points thrice weekly at bedtime for four consecutive weeks. The data were analyzed through the Chi-square, the independent-sample t, the paired-sample t, and the ANCOVA tests at the significance level of less than 0.05.

RESULTS

After controlling sleep quality mean scores at baseline, no significant difference was found between the sleep quality of the two groups after intervention (P > 0.05). The mean scores of fatigue significantly decreased in both acupressure and sham acupressure groups (P < 0.05). However, the decrease in the acupressure group was significantly greater than in the sham acupressure group (P < 0.05).

CONCLUSION

As a noninvasive non-pharmacological therapy, acupressure can significantly reduce fatigue among patients with migraine.

An Adaptive SSVEP-Based Brain-Computer Interface to Compensate Fatigue-Induced Decline of Performance in Practical Application

Brain-computer interfaces based on steady-state visual evoked potentials are promising communication systems for people with speech and motor disabilities. However, reliable SSVEP response requires user's attention, which degrades over time due to significant eye-fatigue when low-frequency visual stimuli (5-15 Hz) are used. Previous studies have shown that eye-fatigue can be reduced using high-frequency flickering stimuli (>25 Hz). Here, it is quantitatively demonstrated that the performance of a high-frequency SSVEP BCI decreases over time, but this amount of decrease can be compensated effectively by using two proposed adaptive algorithms. This leaded to a robust alternative communication system for practical applications. The asynchronous spelling system implemented in this study uses a threshold-based version of LASSO algorithm for frequency recognition. In long online experiments, when participants typed a sentence with the BCI system for 16 times, accuracy of the system was close to its maximum along the experiment. However, regression analysis on typing speed of each sentence demonstrated a significant decrease in all 7 subjects ( ) when thresholds obtained from a calibration test were kept fixed over the experiment. In comparison, no significant change in typing speed was observed when the proposed adaptive algorithms were used. The analysis of variances revealed that the average typing speed of the last four sentences when using adaptive relational algorithm (8.7 char/min) was significantly higher than the tolerance-based algorithm (8.1 char/min) and significantly above 6 char/min when the fixed thresholds were used. Therefore, the relational algorithm proposed in this paper could successfully compensate for the effect of fatigue on performance of the SSVEP BCI system.

Identification of depression subtypes and relevant brain regions using a data-driven approach

It is well known that depressive disorder is heterogeneous, yet little is known about its neurophysiological subtypes. In the present study, we identified neurophysiological subtypes of depression related to specific neural substrates. We performed cluster analysis for 134 subjects (67 depressive subjects and 67 controls) using a high-dimensional dataset consisting of resting state functional connectivity measured by functional MRI, clinical questionnaire scores, and various biomarkers. Applying a newly developed, multiple co-clustering method to this dataset, we identified three subtypes of depression that are characterized by functional connectivity between the right Angular Gyrus (AG) and other brain areas in default mode networks, and Child Abuse Trauma Scale (CATS) scores. These subtypes are also related to Selective Serotonin-Reuptake Inhibitor (SSRI) treatment outcomes, which implies that we may be able to predict effectiveness of treatment based on AG-related functional connectivity and CATS.

Counteracting the Slowdown of Reaction Times in a Vigilance Experiment With 40-Hz Transcranial Alternating Current Stimulation

Indicators for a decrement in vigilance are a slowdown in reaction times and an increase in alpha power in the electroencephalogram in posterior regions of the brain. Transcranial alternating current stimulation (tACS) is a neuropsychological technique that has been found to interact with intrinsic brain oscillations and is able to enhance cognitive and behavioral performance. Recent studies show that tACS in the gamma frequency range (30-80 Hz) is able to downregulate amplitudes in the alpha frequency range (8-12 Hz), in accordance to the effect referred to as cross-frequency coupling, where intrinsic alpha and gamma waves modulate each other. We applied 40 Hz gamma-tACS to the visual cortex during a vigilance experiment and investigated if stimulation improves reaction times and error rates with time-on-task. In our sham controlled experiment, participants completed two blocks of 30 minutes duration while performing the same visual two-choice task. The first block was used as BASELINE. A statistical analysis with a linear mixed model revealed a significantly lower increase of modeled reaction times over time in the INTERVENTION-block of the tACS-group as compared with their BASELINE-block whereas there was no significant change between the BASELINE- and INTERVENTION-block for the SHAM-group. Error rates did not differ between groups. This paper indicates that gamma-tACS can enhance performance in vigilance tasks as it significantly decreased the slowdown of reaction times in our study.

Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model

Driver fatigue is increasingly a contributing factor for traffic accidents, so an effective method to automatically detect driver fatigue is urgently needed. In this study, in order to catch the main characteristics of the EEG signals, four types of entropies (based on the EEG signal of a single channel) were calculated as the feature sets, including sample entropy, fuzzy entropy, approximate entropy and spectral entropy. All feature sets were used as the input of a gradient boosting decision tree (GBDT), a fast and highly accurate boosting ensemble method. The output of GBDT determined whether a driver was in a fatigue state or not based on their EEG signals. Three state-of-the-art classifiers, k-nearest neighbor, support vector machine and neural network were also employed. To assess our method, several experiments including parameter setting and classification performance comparison were performed on 22 subjects. The results indicated that it is possible to use only one EEG channel to detect a driver fatigue state. The average highest recognition rate in this work was up to 94.0%, which could meet the needs of daily applications. Our GBDT-based method may assist in the detection of driver fatigue.

Cerebral effects of music during isometric exercise: An fMRI study

A block-design experiment was conducted using fMRI to examine the brain regions that activate during the execution of an isometric handgrip exercise performed at light-to-moderate-intensity in the presence of music. Nineteen healthy adults (7 women and 12 men; M_age = 24.2, SD = 4.9 years) were exposed to an experimental condition (music [MU]) and a no-music control condition (CO) in a randomized order within a single session. Each condition lasted for 10 min and participants were required to execute 30 exercise trials (i.e., 1 trial = 10 s exercise + 10 s rest). Attention allocation, exertional responses, and affective changes were assessed immediately after each condition. The BOLD response was compared between conditions to identify the combined effects of music and exercise on neural activity. The findings indicate that music reallocated attention toward task-unrelated thoughts (d = 0.52) and upregulated affective arousal (d = 0.72) to a greater degree when compared to a no-music condition. The activity of the left inferior frontal gyrus (lIFG) also increased when participants executed the motor task in the presence of music (F = 24.65), and a significant negative correlation was identified between lIFG activity and perceived exertion for MU (limb discomfort: r = -0.54; overall exertion: r = -0.62). The authors hypothesize that the lIFG activates in response to motor tasks that are executed in the presence of environmental sensory stimuli. Activation of this region might also moderate processing of interoceptive signals - a neurophysiological mechanism responsible for reducing exercise consciousness and ameliorating fatigue-related symptoms.

Detecting mental fatigue from eye-tracking data gathered while watching video: Evaluation in younger and older adults

Health monitoring technology in everyday situations is expected to improve quality of life and support aging populations. Mental fatigue among health indicators of individuals has become important due to its association with cognitive performance and health outcomes, especially in older adults. Previous models using eye-tracking measures allow inference of fatigue during cognitive tasks, such as driving, but they require us to engage in specific cognitive tasks. In addition, previous models were mainly tested by user groups that did not include older adults, although age-related changes in eye-tracking measures have been reported especially in older adults. Here, we propose a model to detect mental fatigue of younger and older adults in natural viewing situations. Our model includes two unique aspects: (i) novel feature sets to better capture fatigue in natural-viewing situations and (ii) an automated feature selection method to select a feature subset enabling the model to be robust to the target's age. To test our model, we collected eye-tracking data from younger and older adults as they watched video clips before and after performing cognitive tasks. Our model improved detection accuracy by up to 13.9% compared with a model based on the previous studies, achieving 91.0% accuracy (chance 50%).

The Effectiveness Of Compression Garments On EEG During a Running Test

The specific purpose of this present paper was to investigate whether the EEG activity has been affected by wearing whole body compression garments during a running test. Ten subjects (men, n=5; women, n=5; age: 24.11 ± 4.48 years; height: 163.56 ± 7.70 cm; chest: 87.78 ± 6.92 cm; weight: 58.67 ± 10.96 kg; BMI: 21.77 ± 2.63 kg.m^-2) completed a running protocol on a treadmill. Each subject participated in two running trials, wearing either a compression garment (CG) or a non-compression garment (NCG) during exercise. Electroencephalogram (EEG) signals were collected during exercise using wearable sensors. The present study revealed a statistically significant difference between CGs and NCGs in alpha, beta and theta power spectral density (p<0.05). Therefore, the brain activity was influenced by the application of CGs during the running test. This result would also recommends an application of CGs in training as well as in competition.

Flexible Conductive Composite Integrated with Personal Earphone for Wireless, Real-Time Monitoring of Electrophysiological Signs

We introduce optimized elastomeric conductive electrodes using a mixture of silver nanowires (AgNWs) with carbon nanotubes/polydimethylsiloxane (CNTs/PDMS), to build a portable earphone type of wearable system that is designed to enable recording electrophysiological activities as well as listening to music at the same time. A custom-built, plastic frame integrated with soft, deformable fabric-based memory foam of earmuffs facilitates essential electronic components, such as conductive elastomers, metal strips, signal transducers and a speaker. Such platform incorporates with accessory cables to attain wireless, real-time monitoring of electrical potentials whose information can be displayed on a cell phone during outdoor activities and music appreciation. Careful evaluations on experimental results reveal that the performance of fabricated dry electrodes are comparable to that of commercial wet electrodes, and position-dependent signal behaviors provide a route toward accomplishing maximized signal quality. This research offers a facile approach for a wearable healthcare monitor via integration of soft electronic constituents with personal belongings.

Fatigue Assessment by Blink Detected with Attachable Optical Sensors of Dye-Sensitized Photovoltaic Cells

This paper demonstrates fatigue assessment based on eye blinks that are detected by dye-sensitized photovoltaic cells. In particular, the sensors were attached to the temple of eyeglasses and positioned at the lateral side of the eye. They are wearable, did not majorly disturb the user's eyesight, and detected the position of the eyelid or the eye state. The optimal location of the sensor was experimentally investigated by evaluating the detection accuracy of blinks. We conducted fatigue assessment experiments using the developed wearable system, or smart glasses. Several parameters, including the frequency, duration, and velocity of eye blinks, were extracted as fatigue indices. Successful fatigue assessment by the proposed system will be of great benefit for maximizing performance and maintenance of physical/mental health.

Evaluating Pro- and Re-Active Driving Behavior by Means of the EEG

Traffic safety essentially depends on the drivers' alertness and vigilance, especially in monotonous or demanding driving situations. Brain oscillatory EEG activity offers insight into a drivers' mental state and has therefore attracted much attention in the past. However, EEG measures do not only vary with internal factors like attentional engagement and vigilance but might also interact with external factors like time on task, task demands, or the degree to which a traffic situation is predictable. In order to identify EEG parameters for cognitive mechanisms involved in tasks of high and low controllability, the present study investigated the interaction of time on task, task load, and cognitive controllability in simulated driving scenarios, using an either re-active or pro-active driving task. Participants performed a lane-keeping task, half of them compensating varying levels of crosswind (re-active task), and the other half driving along a winding road (pro-active task). Both driving tasks were adjusted with respect to difficulty. The analysis of oscillatory EEG parameters showed an increase in total power (1-30 Hz) with time on task, with decreasing task load, and in the re-active compared to the pro-active task. Furthermore, the relative power in Alpha band increased with decreasing task load and time on task, while relative Theta power showed the opposite pattern. Moreover, relative Alpha power was also higher in the re-active, than pro-active, driving situation, an effect that even increased with time on task. The results demonstrate that the controllability of a driving situation has a similar effect on oscillatory EEG activity like time on task and task load.

Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.

A Machine Learning Approach to the Detection of Pilot's Reaction to Unexpected Events Based on EEG Signals

This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipation of visual cue and reaction to it. Especially, a problem of selecting a proper classification algorithm for such problems is being examined. For that purpose an experiment involving 10 subjects was planned and conducted. Experimental electroencephalographic data was acquired using an Emotiv EPOC+ headset. Proposed methodology involved use of a popular method in biomedical signal processing, the Common Spatial Pattern, extraction of bandpower features, and an extensive test of different classification algorithms, such as Linear Discriminant Analysis, k-nearest neighbors, and Support Vector Machines with linear and radial basis function kernels, Random Forests, and Artificial Neural Networks.

An analysis on driver drowsiness based on reaction time and EEG band power

Falling asleep during driving is a serious problem that has resulted in fatal accidents worldwide. Thus, there is a need to detect driver drowsiness to counter it. This study analyzes the changes in the electroencephalography (EEG) collected from 4 subjects driving under monotonous road conditions using a driving simulator. The drowsiness level of the subjects is inferred from the time taken to react to events. The results from the analysis of the reaction time shows that drowsiness occurs in cycles, which correspond to short sleep cycles known as `microsleeps'. The results from a time-frequency analysis of the four frequency bands' power reveals differences between trials with fast and slow reaction times; greater beta band power is present in all subjects, greater alpha power in 2 subjects, greater theta power in 2 subjects, and greater delta power in 3 subjects, for fast reaction trials. Overall, this study shows that reaction time can be used to infer the drowsiness, and subject-specific changes in the EEG band power may be used to infer drowsiness. Thus the study shows a promising prospect of developing Brain-Computer Interface to detect driver drowsiness.

Recurrence of task-related electroencephalographic activity during post-training quiet rest and sleep

Offline reactivation of task-related neural activity has been demonstrated in animals but is difficult to directly observe in humans. We sought to identify potential electroencephalographic (EEG) markers of offline memory processing in human subjects by identifying a set of characteristic EEG topographies ("microstates") that occurred as subjects learned to navigate a virtual maze. We hypothesized that these task-related microstates would appear during post-task periods of rest and sleep. In agreement with this hypothesis, we found that one task-related microstate was increased in post-training rest and sleep compared to baseline rest, selectively for subjects who actively learned the maze, and not in subjects performing a non-learning control task. Source modeling showed that this microstate was produced by activity in temporal and parietal networks, which are known to be involved in spatial navigation. For subjects who napped after training, the increase in this task-related microstate predicted the magnitude of subsequent change in performance. Our findings demonstrate that task-related EEG patterns re-emerge during post-training rest and sleep.

A Taxonomy of Fatigue Concepts and Their Relation to Hearing Loss

Fatigue is common in individuals with a variety of chronic health conditions and can have significant negative effects on quality of life. Although limited in scope, recent work suggests persons with hearing loss may be at increased risk for fatigue, in part due to effortful listening that is exacerbated by their hearing impairment. However, the mechanisms responsible for hearing loss-related fatigue, and the efficacy of audiologic interventions for reducing fatigue, remain unclear. To improve our understanding of hearing loss-related fatigue, as a field it is important to develop a common conceptual understanding of this construct. In this article, the broader fatigue literature is reviewed to identify and describe core constructs, consequences, and methods for assessing fatigue and related constructs. Finally, the current knowledge linking hearing loss and fatigue is described and may be summarized as follows: Hearing impairment may increase the risk of subjective fatigue and vigor deficits; adults with hearing loss require more time to recover from fatigue after work and have more work absences; sustained, effortful, listening can be fatiguing; optimal methods for eliciting and measuring fatigue in persons with hearing loss remain unclear and may vary with listening condition; and amplification may minimize decrements in cognitive processing speed during sustained effortful listening. Future research is needed to develop reliable measurement methods to quantify hearing loss-related fatigue, explore factors responsible for modulating fatigue in people with hearing loss, and identify and evaluate potential interventions for reducing hearing loss-related fatigue.

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1–2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.

Monitoring driver fatigue using a single-channel electroencephalographic device: A validation study by gaze-based, driving performance, and subjective data

Driver fatigue can impair performance as much as alcohol does. It is the most important road safety concern, causing thousands of accidents and fatalities every year. Thanks to technological developments, wearable, single-channel EEG devices are now getting considerable attention as fatigue monitors, as they could help drivers to assess their own levels of fatigue and, therefore, prevent the deterioration of performance. However, the few studies that have used single-channel EEG devices to investigate the physiological effects of driver fatigue have had inconsistent results, and the question of whether we can monitor driver fatigue reliably with these EEG devices remains open. Here, we assessed the validity of a single-channel EEG device (TGAM-based chip) to monitor changes in mental state (from alertness to fatigue). Fifteen drivers performed a 2-h simulated driving task while we recorded, simultaneously, their prefrontal brain activity and saccadic velocity. We used saccadic velocity as the reference index of fatigue. We also collected subjective ratings of alertness and fatigue, as well as driving performance. We found that the power spectra of the delta EEG band showed an inverted U-shaped quadratic trend (EEG power spectra increased for the first hour and half, and decreased during the last thirty minutes), while the power spectra of the beta band linearly increased as the driving session progressed. Coherently, saccadic velocity linearly decreased and speeding time increased, suggesting a clear effect of fatigue. Subjective data corroborated these conclusions. Overall, our results suggest that the TGAM-based chip EEG device is able to detect changes in mental state while performing a complex and dynamic everyday task as driving.

EEG-neurofeedback training of beta band (12-22Hz) affects alpha and beta frequencies - A controlled study of a healthy population

The frequency-function relation of various EEG bands has inspired EEG-neurofeedback procedures intending to improve cognitive abilities in numerous clinical groups. In this study, we administered EEG-neurofeedback (EEG-NFB) to a healthy population to determine the efficacy of this procedure. We evaluated feedback manipulation in the beta band (12-22Hz), known to be involved in visual attention processing. Two groups of healthy adults were trained to either up- or down-regulate beta band activity, thus providing mutual control. Up-regulation training induced increases in beta and alpha band (8-12Hz) amplitudes during the first three sessions. Group-independent increases in the activity of both bands were observed in the later phase of training. EEG changes were not matched by measured behavioural indices of attention. Parallel changes in the two bands challenge the idea of frequency-specific EEG-NFB protocols and suggest their interdependence. Our study exposes the possibility (i) that the alpha band is more prone to manipulation, and (ii) that changes in the bands' amplitudes are independent from specified training. We therefore encourage a more comprehensive approach to EEG-neurofeedback training embracing physiological and/or operational relations among various EEG bands.

Neural mechanisms of mental fatigue elicited by sustained auditory processing

Despite growing evidence that prolonged episodes of effortful listening can lead to mental fatigue, little work has been done to examine the patterns of brain activation associated with listening over time. In order to gain a better understanding of the nature of listening-related mental fatigue, this study characterized the effects of sustained auditory processing on brain activation in 19 adults with normal hearing. A 50-min, auditory choice paradigm served as the fatiguing task. Mental fatigue was quantified using subjective (self-report) and behavioral (response time and accuracy) measures, as well as event-related potential (ERP) measures indexing motivation (error-related negativity; ERN) and general arousal (N1). Additional electrical neuroimaging analyses were carried out on ERP datasets. Subjective and behavioral results confirmed that participants became fatigued during the auditory task (data from the first 25min compared with the second 25min). ERPs revealed changes in neural activity consistent with decreased arousal (reduced N1 amplitude). Topographical analyses indicated decreased brain activation, without a change in underlying neural network configuration. Regions of decreased brain activation, as estimated via electrical neuroimaging, suggested a decrease in attention to task stimulus-response characteristics (reduced activation in regions associated with the dorsal attention network). The decrease in mean N1 amplitude revealed a significant, positive correlation with subjective report of reduced motivation. These findings support existing cognitive and neurophysiological models that suggest mental fatigue builds over time on task, and affects motivation to influence task performance. Furthermore, this study shows sustained auditory processing can elicit mental fatigue, and that dorsal parietal activity might provide a useful method of measuring its effects.

Correlation of reaction time and EEG log bandpower from dry frontal electrodes in a passive fatigue driving simulation experiment

Fatigue is one of the causes of falling asleep at the wheel, which can result in fatal accidents. Thus, it is necessary to have practical fatigue detection solutions for drivers. In literature, electroencephalography (EEG) along with the surrogate measure of reaction time (RT) has been used to develop fatigue detection algorithms. However, these solutions are often based upon wet multi-channel EEG electrodes which are not feasible or practical for drivers. Using dry electrodes and headband like designs would be better. Hence, this study aims to investigate the correlation of EEG log bandpower against RT via a Muse headband which has dry frontal EEG electrodes. 31 subjects underwent an hour-long driving simulation experiment with car deviation events. Based on the video and EEG data, 5 `Sleepy' and 5 `Alert' subjects are identified and analyzed. A differential signal between Fp1 and Fp2 is computed so as to remove the effects of eye blinks, and is analyzed for correlation with RT. Significant positive correlation is found for log delta (1-4 Hz) bandpower, and significant negative correlations for log theta (4-8 Hz) and alpha (8-12 Hz) bandpowers, but the positive correlation of log beta (12-30 Hz) bandpower with RT is not significant. This is a good first step towards building a practical fatigue detection solution for drivers in the future.

Trial-by-trial co-variation of pre-stimulus EEG alpha power and visuospatial bias reflects a mixture of stochastic and deterministic effects

Human perception of perithreshold stimuli critically depends on oscillatory EEG activity prior to stimulus onset. However, it remains unclear exactly which aspects of perception are shaped by this pre‐stimulus activity and what role stochastic (trial‐by‐trial) variability plays in driving these relationships. We employed a novel jackknife approach to link single‐trial variability in oscillatory activity to psychometric measures from a task that requires judgement of the relative length of two line segments (the landmark task). The results provide evidence that pre‐stimulus alpha fluctuations influence perceptual bias. Importantly, a mediation analysis showed that this relationship is partially driven by long‐term (deterministic) alpha changes over time, highlighting the need to account for sources of trial‐by‐trial variability when interpreting EEG predictors of perception. These results provide fundamental insight into the nature of the effects of ongoing oscillatory activity on perception. The jackknife approach we implemented may serve to identify and investigate neural signatures of perceptual relevance in more detail.

Altered insula-default mode network connectivity in fibromyalgia: a resting-state magnetoencephalographic study

Background

Fibromyalgia (FM) is a disabling chronic pain syndrome with unknown pathophysiology. Functional magnetic resonance imaging studies on FM have suggested altered brain connectivity between the insula and the default mode network (DMN). However, this connectivity change has not been characterized through direct neural signals for exploring the embedded spectrotemporal features and the pertinent clinical relevance.

Methods

We recorded the resting-state magnetoencephalographic activities of 28 patients with FM and 28 age- and sex-matched controls, and analyzed the source-based functional connectivity between the insula and the DMN at 1–40 Hz by using the minimum norm estimates and imaginary coherence methods. We also measured the connectivity between the DMN and the primary visual (V1) and somatosensory (S1) cortices as intrapatient negative controls. Connectivity measurement was further correlated with the clinical parameters of FM.

Results

Compared with the controls, patients with FM reported more tender points (15.2±2.0 vs. 5.9±3.7) and higher total tenderness score (TTS; 29.1±7.0 vs. 7.7±5.5; both p < 0.001); they also had decreased insula–DMN connectivity at the theta band (4–8 Hz; left, p = 0.007; right, p = 0.035), but displayed unchanged V1–DMN and S1–DMN connectivity (p > 0.05). When patients with FM and the controls were combined together, the insula-DMN theta connectivity was negatively correlated with the number of tender points (left insula, r = −0.428, p = 0.001; right insula, r = −0.4, p = 0.002) and TTS score (left insula, r = −0.429, p = 0.001; right insula, r = −0.389, p = 0.003). Furthermore, in patients with FM, the right insula–DMN connectivity at the beta band (13–25 Hz) was negatively correlated with the number of tender points (r = −0.532, p = 0.004) and TTS (r = −0.428, p = 0.023), and the bilateral insula–DMN connectivity at the delta band (1–4 Hz) was negatively correlated with FM Symptom Severity (left: r = −0.423, p = 0.025; right: r = −0.437, p = 0.020) and functional disability (Fibromyalgia Impact Questionnaire; left: r = −0.415, p = 0.028; right: r = −0.374, p = 0.050).

Conclusions

We confirmed the frequency-specific reorganization of the insula–DMN connectivity in FM. The clinical relevance of this connectivity change may warrant future studies to elucidate its causal relationship and potential as a neurological signature for FM.

Electronic supplementary material

The online version of this article (doi:10.1186/s10194-017-0799-x) contains supplementary material, which is available to authorized users.

EEG-based driver fatigue detection using hybrid deep generic model

Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.

Classification of EEG based-mental fatigue using principal component analysis and Bayesian neural network

This paper presents an electroencephalography (EEG) based-classification of between pre- and post-mental load tasks for mental fatigue detection from 65 healthy participants. During the data collection, eye closed and eye open tasks were collected before and after conducting the mental load tasks. For the computational intelligence, the system uses the combination of principal component analysis (PCA) as the dimension reduction method of the original 26 channels of EEG data, power spectral density (PSD) as feature extractor and Bayesian neural network (BNN) as classifier. After applying the PCA, the dimension of the data is reduced from 26 EEG channels in 6 principal components (PCs) with above 90% of information retained. Based on this reduced dimension of 6 PCs of data, during eyes open, the classification pre-task (alert) vs. post-task (fatigue) using Bayesian neural network resulted in sensitivity of 76.8 %, specificity of 75.1% and accuracy of 76% Also based on data from the 6 PCs, during eye closed, the classification between pre- and post-task resulted in a sensitivity of 76.1%, specificity of 74.5% and accuracy of 75.3%. Further, the classification results of using only 6 PCs data are comparable to the result using the original 26 EEG channels. This finding will help in reducing the computational complexity of data analysis based on 26 channels of EEG for mental fatigue detection.

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification

This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

Resting-state BOLD oscillation frequency predicts vigilance task performance at both normal and high environmental temperatures

Hyperthermia may impair vigilance functions and lead to slower reaction times (RTs) in the psychomotor vigilance task (PVT) and possibly disturbing cerebral hemodynamic rhythms. To test these hypotheses, we acquired the resting-state BOLD and cerebral blood flow (CBF) data, as well as PVTRTs from 15 participants in two simulated environmental thermal conditions (50 °C/25 °C). We adopted a data-driven method, frequency component analysis, to quantify the mean frequency of the BOLD series of each voxel. Across-subject correlation analysis was employed to detect the brain areas whose BOLD oscillation frequency was correlated with the RTs. Significant changes of BOLD frequency and CBF within these areas were compared between hyperthermia and normothermia conditions. Spatial correlations between BOLD frequency and CBF were calculated within different brain areas for each subject under both thermal conditions. Results showed that, under both thermal conditions, the RTs correlated with the BOLD frequency positively in the default mode network (DMN) and negatively in the sensorimotor network (SMN). The increase of BOLD frequency in the thalamus and ventral medial prefrontal cortex was correlated with the increase of RTs in hyperthermia compared with normothermia. Hyperthermia decreased BOLD frequency and CBF in the SMN, while it increased CBF in the thalamus and posterior cingulate. In both thermal conditions, the spatial distribution of CBF negatively correlated with the spatial distribution of BOLD oscillation frequency in most cortical areas, especially in cingulate cortices, precuneus, and primary visual cortex. These results suggest that hyperthermia might deteriorate task performance by interfering with the resting-state CBF, and with BOLD rhythms. The overlapping of the thermoregulatory and vigilance functions in the SMN and DMN might underlie the neural mechanisms of the cognitive-behavioral impairments induced by hyperthermia.

ICA-Derived EEG Correlates to Mental Fatigue, Effort, and Workload in a Realistically Simulated Air Traffic Control Task

Electroencephalograph (EEG) has been increasingly studied to identify distinct mental factors when persons perform cognitively demanding tasks. However, most of these studies examined EEG correlates at channel domain, which suffers the limitation that EEG signals are the mixture of multiple underlying neuronal sources due to the volume conduction effect. Moreover, few studies have been conducted in real-world tasks. To precisely probe EEG correlates with specific neural substrates to mental factors in real-world tasks, the present study examined EEG correlates to three mental factors, i.e., mental fatigue [also known as time-on-task (TOT) effect], workload and effort, in EEG component signals, which were obtained using an independent component analysis (ICA) on high-density EEG data. EEG data were recorded when subjects performed a realistically simulated air traffic control (ATC) task for 2 h. Five EEG independent component (IC) signals that were associated with specific neural substrates (i.e., the frontal, central medial, motor, parietal, occipital areas) were identified. Their spectral powers at their corresponding dominant bands, i.e., the theta power of the frontal IC and the alpha power of the other four ICs, were detected to be correlated to mental workload and effort levels, measured by behavioral metrics. Meanwhile, a linear regression analysis indicated that spectral powers at five ICs significantly increased with TOT. These findings indicated that different levels of mental factors can be sensitively reflected in EEG signals associated with various brain functions, including visual perception, cognitive processing, and motor outputs, in real-world tasks. These results can potentially aid in the development of efficient operational interfaces to ensure productivity and safety in ATC and beyond.

Automatic Detection of Driver Fatigue Using Driving Operation Information for Transportation Safety

Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers’ fatigue levels. It analyzes the operation features of SWA and YA under different fatigue statuses, then calculates the approximate entropy (ApEn) features of a short sliding window on time series. Using the nonlinear feature construction theory of dynamic time series, with the fatigue features as input, designs a “2-6-6-3” multi-level back propagation (BP) Neural Networks classifier to realize the fatigue detection. An approximately 15-h experiment is carried out on a real road, and the data retrieved are segmented and labeled with three fatigue levels after expert evaluation, namely “awake”, “drowsy” and “very drowsy”. The average accuracy of 88.02% in fatigue identification was achieved in the experiment, endorsing the value of the proposed method for engineering applications.

Effects of auditory stimuli on electrical activity in the brain during cycle ergometry

The present study sought to further understanding of the brain mechanisms that underlie the effects of music on perceptual, affective, and visceral responses during whole-body modes of exercise. Eighteen participants were administered light-to-moderate intensity bouts of cycle ergometer exercise. Each exercise bout was of 12-min duration (warm-up [3min], exercise [6min], and warm-down [3min]). Portable techniques were used to monitor the electrical activity in the brain, heart, and muscle during the administration of three conditions: music, audiobook, and control. Conditions were randomized and counterbalanced to prevent any influence of systematic order on the dependent variables. Oscillatory potentials at the Cz electrode site were used to further understanding of time-frequency changes influenced by voluntary control of movements. Spectral coherence analysis between Cz and frontal, frontal-central, central, central-parietal, and parietal electrode sites was also calculated. Perceptual and affective measures were taken at five timepoints during the exercise bout. Results indicated that music reallocated participants' attentional focus toward auditory pathways and reduced perceived exertion. The music also inhibited alpha resynchronization at the Cz electrode site and reduced the spectral coherence values at Cz-C4 and Cz-Fz. The reduced focal awareness induced by music led to a more autonomous control of cycle movements performed at light-to-moderate-intensities. Processing of interoceptive sensory cues appears to upmodulate fatigue-related sensations, increase the connectivity in the frontal and central regions of the brain, and is associated with neural resynchronization to sustain the imposed exercise intensity.

The interconnection of mental fatigue and aging: An EEG study

Mental fatigue, a state of reduced alertness and decreased overall performance due to prolonged cognitive activity, is a major cause for a large number of accidents in traffic and industry. Against the background of an aging workforce, the investigation of the interconnection of mental fatigue and aging is of great practical relevance. In the present study, a group of younger and a group of older adults performed a cognitive task for 3h. The experimental design also comprised breaks with various durations. Beside behavioral data, the spectral properties of the ongoing EEG with respect to time on task and breaks were analyzed. No differences between the age groups were found in behavior, but electrophysiological measures provide some evidence that older adults in our study were differentially affected by time on task. In the later course of the experiment modulations in frontal theta power became larger for older, compared to younger adults. This may indicate strain due to task demands, eventually resulting from the deployment of compensatory processes. Occipital alpha, which has been linked to internally oriented brain states, saturates faster in younger adults. It thus maybe, that especially the younger participants' performance deteriorated due to the monotonous nature of the task itself. Both mechanisms, an increased consumption of cognitive resources in older adults and a decrease of motivation in younger adults, could mask differences in performance decrements between the age groups due to mental fatigue.

Brain-heart linear and nonlinear dynamics during visual emotional elicitation in healthy subjects

This study investigates brain-heart dynamics during visual emotional elicitation in healthy subjects through linear and nonlinear coupling measures of EEG spectrogram and instantaneous heart rate estimates. To this extent, affective pictures including different combinations of arousal and valence levels, gathered from the International Affective Picture System, were administered to twenty-two healthy subjects. Time-varying maps of cortical activation were obtained through EEG spectral analysis, whereas the associated instantaneous heartbeat dynamics was estimated using inhomogeneous point-process linear models. Brain-Heart linear and nonlinear coupling was estimated through the Maximal Information Coefficient (MIC), considering EEG time-varying spectra and point-process estimates defined in the time and frequency domains. As a proof of concept, we here show preliminary results considering EEG oscillations in the θ band (4-8 Hz). This band, indeed, is known in the literature to be involved in emotional processes. MIC highlighted significant arousal-dependent changes, mediated by the prefrontal cortex interplay especially occurring at intermediate arousing levels. Furthermore, lower and higher arousing elicitations were associated to not significant brain-heart coupling changes in response to pleasant/unpleasant elicitations.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

Comparing features extractors in EEG-based cognitive fatigue detection of demanding computer tasks

An electroencephalography (EEG)-based classification system could be used as a tool for detecting cognitive fatigue from demanding computer tasks. The most widely used feature extractor in EEG-based fatigue classification is power spectral density (PSD). This paper investigates PSD and three alternative feature extraction methods, in order to find the best feature extractor for the classification of cognitive fatigue during cognitively demanding tasks. These compared methods are power spectral entropy (PSE), wavelet, and autoregressive (AR). Bayesian neural network was selected as the classifier in this study. The results showed that the use of PSD and PSE methods provide an average accuracy of 60% for each computer task. This finding is slightly improved using the wavelet method which has an average accuracy of 61%. The AR method is the best feature extractor compared with the PSD, PSE and wavelet in this study with accuracy of 75.95% in AX-continuous performance test (AX-CPT), 75.23% in psychomotor vigilance test (PVT) and 76.02% in Stroop task (p-value <; 0.05).

Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.

Cerebral mechanisms underlying the effects of music during a fatiguing isometric ankle-dorsiflexion task

The brain mechanisms by which music-related interventions ameliorate fatigue-related symptoms during the execution of fatiguing motor tasks are hitherto under-researched. The objective of the present study was to investigate the effects of music on brain electrical activity and psychophysiological measures during the execution of an isometric fatiguing ankle-dorsiflexion task performed until the point of volitional exhaustion. Nineteen healthy participants performed two fatigue tests at 40% of maximal voluntary contraction while listening to music or in silence. Electrical activity in the brain was assessed by use of a 64-channel EEG. The results indicated that music downregulated theta waves in the frontal, central, and parietal regions of the brain during exercise. Music also induced a partial attentional switching from associative thoughts to task-unrelated factors (dissociative thoughts) during exercise, which led to improvements in task performance. Moreover, participants experienced a more positive affective state while performing the isometric task under the influence of music.

Mid-Task Break Improves Global Integration of Functional Connectivity in Lower Alpha Band

Numerous efforts have been devoted to revealing neurophysiological mechanisms of mental fatigue, aiming to find an effective way to reduce the undesirable fatigue-related outcomes. Until recently, mental fatigue is thought to be related to functional dysconnectivity among brain regions. However, the topological representation of brain functional connectivity altered by mental fatigue is only beginning to be revealed. In the current study, we applied a graph theoretical approach to analyse such topological alterations in the lower alpha band (8~10 Hz) of EEG data from 20 subjects undergoing a two-session experiment, in which one session includes four successive blocks with visual oddball tasks (session 1) whereas a mid-task break was introduced in the middle of four task blocks in the other session (session 2). Phase lag index (PLI) was then employed to measure functional connectivity strengths for all pairs of EEG channels. Behavior and connectivity maps were compared between the first and last task blocks in both sessions. Inverse efficiency scores (IES = reaction time/response accuracy) were significantly increased in the last task block, showing a clear effect of time-on-task in participants. Furthermore, a significant block-by-session interaction was revealed in the IES, suggesting the effectiveness of the mid-task break on maintaining task performance. More importantly, a significant session-independent deficit of global integration and an increase of local segregation were found in the last task block across both sessions, providing further support for the presence of a reshaped topology in functional brain connectivity networks under fatigue state. Moreover, a significant block-by-session interaction was revealed in the characteristic path length, small-worldness, and global efficiency, attributing to the significantly disrupted network topology in session 1 in comparison of the maintained network structure in session 2. Specifically, we found increased nodal betweenness centrality in several channels resided in frontal regions in session 1, resembling the observations of more segregated global architecture under fatigue state. Taken together, our findings provide insights into the substrates of brain functional dysconnectivity patterns for mental fatigue and reiterate the effectiveness of the mid-task break on maintaining brain network efficiency.