Evaluation of driver drowsiness based on respiratory metrics.

BACKGROUND: The transition from alertness to drowsiness can cause considerable changes in the respiratory system, providing an opportunity to detect driver drowsiness. OBJECTIVE: The aim of this study was to determine which respiratory features indicate driver drowsiness and then use these features to classify the level of drowsiness and alertness. METHODS: Twenty male students (mean age 25.6±2.41 years) participated in the study using a driving simulator, and eight features, including expiration duration (ED), inspiration duration (ID), peak-to-peak amplitude (PA), inspiration-to-expiration time ratio (I/E ratio), driving, timing, respiration rate (RR), and yawning, were extracted from the respiratory signal generated by abdominal motions using a belt equipped with a force sensor. RESULTS: All eight features were statistically significant at the significance level of 0.05. Drowsiness can be detected using respiratory features with 88% accuracy, 82% precision, 86% recall, and an 90% F1 score. CONCLUSION: The findings of this study may be useful in the development of driver drowsiness monitoring systems based on less intrusive respiratory signal analysis, particularly for specific process automation applications when vehicle control is not in the hands of the driver.

Changes in electroencephalographic microstates between evening and morning are associated with overnight sleep slow waves in healthy individuals.

STUDY OBJECTIVES: Microstates are semi-stable voltage topographies that account for most of electroencephalogram (EEG) variance. However, the impact of time of the day and sleep on microstates has not been examined. To address this gap, we assessed whether microstates differed between the evening and morning and whether sleep slow waves correlated with microstate changes in healthy participants. METHODS: Forty-five healthy participants were recruited. Each participant underwent 6 minutes of resting state EEG recordings in the evening and morning, interleaved by sleep EEGs. Evening-to-morning changes in microstate duration, coverage, and occurrence were assessed. Furthermore, correlation between microstate changes and sleep slow-wave activity (SWA) and slow-wave density (SWD) were performed. RESULTS: Two-way ANOVAs with microstate class (A, B, C, and D) and time (evening and morning) revealed significant microstate class × time interaction for duration (F(44) = 5.571, p = 0.002), coverage (F(44) = 6.833, p = 0.001), and occurrence (F(44) = 5.715, p = 0.002). Post hoc comparisons showed significant effects for microstate C duration (padj = 0.048, Cohen's d = -0.389), coverage (padj = 0.002, Cohen's d = -0.580), and occurrence (padj = 0.002, Cohen's d = -0.606). Topographic analyses revealed inverse correlations between SWD, but not SWA, and evening-to-morning changes in microstate C duration (r = -0.51, padj = 0.002), coverage (r = -0.45, padj = 0.006), and occurrence (r = -0.38, padj = 0.033). CONCLUSIONS: Microstate characteristics showed significant evening-to-morning changes associated with, and possibly regulated by, sleep slow waves. These findings suggest that future microstate studies should control for time of day and sleep effects.

Effect of muscular fatigue on the cumulative lumbar damage during repetitive lifting task: a comparative study of damage calculation methods.

Several methods have been put forward to quantify cumulative loads; however, limited evidence exists as to the subsequent damages and the role of muscular fatigue. The present study assessed whether muscular fatigue could affect cumulative damage imposed on the L5-S1 joint. Trunk muscle electromyographic (EMG) activities and kinematics/kinetics of 18 healthy male individuals were evaluated during a simulated repetitive lifting task. A traditional EMG-assisted model of the lumbar spine was modified to account for the effect of erector spinae fatigue. L5-S1 compressive loads for each lifting cycle were estimated based on varying (i.e. actual), fatigue-modified, and constant Gain factors. The corresponding damages were integrated to calculate the cumulative damage. Moreover, the damage calculated for one lifting cycle was multiplied by the lifting frequency, as the traditional approach. Compressive loads and the damages obtained through the fatigue-modified model were predicted in close agreement with the actual values. Similarly, the difference between actual damages and those driven by the traditional approach was not statistically significant (p = 0.219). However, damages based on a constant Gain factor were significantly greater than those based on the actual (p = 0.012), fatigue-modified (p = 0.017), and traditional (p = 0.007) approaches.Practitioner summary: In this study, we managed to include the effect of muscular fatigue on cumulative lumbar damage calculations. Including the effect of muscular fatigue leads to an accurate estimation of cumulative damages while eliminating computational complexity. However, using the traditional approach also appears to provide acceptable estimates for ergonomic assessments.

Changes in Neck and Shoulder Muscles Fatigue Threshold When Using a Passive Head/Neck Supporting Exoskeleton During Repetitive Overhead Tasks.

OBJECTIVE: This study aimed to investigate the effects of a head/neck supporting exoskeleton (HNSE) on the electromyographic fatigue threshold (EMGFT) of the neck and shoulder muscles during a simulated overhead work task. BACKGROUND: Overhead work is a well-known risk factor for neck and shoulder musculoskeletal disorders due to the excessive strain imposed on the muscles and joints in these regions. METHOD: Fourteen healthy males performed a repetitive overhead nut fastening/unfastening task to exhaustion while wearing and not wearing the HNSE at two neck extension angles (40% and 80% of neck maximum range of motion). Electromyographic signals were continuously recorded from the right and left sternocleidomastoid (SCMR, SCML), splenius capitis (SCR, SCL), upper trapezius (UTR, UTL), and anterior deltoid (ADR, ADL) muscles. The normalized electromyographic amplitude (nEMG) data was time normalized, and a bisegmental linear regression was applied to determine the muscle fatigue break point. RESULTS: The results showed a significant increase in fatigue threshold time in the SCMR (p < .001), SCML (p = .002), and UTR (p = .037) muscles when the HNSE was used. However, the EMGFT times for the right and left deltoid and left trapezius muscles showed a nonsignificant reduction due to the head/neck support exoskeleton use. In addition, the neck extension angle did not reveal a significant effect on muscles' EMGFT time. CONCLUSION: Overall, the findings confirmed a significant delay in fatigue onset in sternocleidomastoid muscles, as measured by the electromyographic fatigue threshold. This finding suggests that the HNSE can be an effective ergonomic intervention for reducing the risk of musculoskeletal disorders in overhead workers. However, further studies are needed to investigate the effect of the HNSE at other neck extension angles and more realistic tasks to ensure the generalizability of our results. APPLICATION: The present findings emphasize the application of the fatigue onset time to evaluate the effectiveness of ergonomic interventions, including exoskeletons, which can subsequently be utilized to alleviate postural demands and reduce the risk of musculoskeletal disorders.

N-Acetylaspartate and Choline Metabolites in Cortical and Subcortical Regions in Clinical High Risk Relative to Healthy Control Subjects: An Exploratory 7T MRSI Study.

N-acetylaspartate (NAA) and choline (Cho) are two brain metabolites implicated in several key neuronal functions. Abnormalities in these metabolites have been reported in both early course and chronic patients with schizophrenia (SCZ). It is, however, unclear whether NAA and Cho's alterations occur even before the onset of the disorder. Clinical high risk (CHR) individuals are a population uniquely enriched for psychosis and SCZ. In this exploratory study, we utilized 7-Tesla magnetic resonance spectroscopic imaging (MRSI) to examine differences in total NAA (tNAA; NAA + N-acetylaspartylglutamate [NAAG]) and major choline-containing compounds, including glycerophosphorylcholine and phosphorylcholine [tCho], over the creatine (Cre) levels between 26 CHR and 32 healthy control (HC) subjects in the subcortical and cortical regions. While no tCho/Cre differences were found between groups in any of the regions of interest (ROIs), we found that CHR had significantly reduced tNAA/Cre in the right dorsal lateral prefrontal cortex (DLPFC) compared to HC, and that the right DLPFC tNAA/Cre reduction in CHR was negatively associated with their positive symptoms scores. No tNAA/Cre differences were found between CHR and HC in other ROIs. In conclusion, reduced tNAA/Cre in CHR vs. HC may represent a putative molecular biomarker for risk of psychosis and SCZ that is associated with symptom severity.

Circadian Rhythm Changes in Healthy Aging and Mild Cognitive Impairment.

Disruptions in circadian rhythms can occur in healthy aging; however, these changes are more severe and pervasive in individuals with age-related and neurodegenerative diseases, such as dementia. Circadian rhythm alterations are also present in preclinical stages of dementia, for example, in patients with mild cognitive impairments (MCI); thus, providing a unique window of opportunity for early intervention in neurodegenerative disorders. Nonetheless, there is a lack of studies examining the association between relevant changes in circadian rhythms and their relationship with cognitive dysfunctions in MCI individuals. In this review, circadian system alterations occurring in MCI patients are examined compared to healthy aging individuals while also considering their association with MCI neurocognitive alterations. The main findings are that abnormal circadian changes in rest-activity, core body temperature, melatonin, and cortisol rhythms appear in the MCI stage and that these circadian rhythm disruptions are associated with some of the neurocognitive deficits observed in MCI patients. In addition, preliminary evidence indicates that interventions aimed at restoring regular circadian rhythms may prevent or halt the progress of neurodegenerative diseases and mitigate their related cognitive impairments. Future longitudinal studies with repeated follow-up assessments are needed to establish the translational potential of these findings in clinical practice.

Nonlinear analysis of postural changes related to the movement interventions during prolonged standing task.

This study assessed the effects of movement-based interventions on the complexity of postural changes during prolonged standing. Twenty participants, equally distributed in gender and standing work experience (SWE), completed three simulated prolonged standing sessions: without movement (control), leg exercise and footrest. The amount and complexity of variability in the centre of pressure (COP) and lumbar curvature angle were quantified using linear and nonlinear tools. Lower leg swelling and back/leg discomfort were also monitored. Participants in the SWE group showed significantly greater postural complexity during the standing. Regular leg exercise resulted in significantly higher postural complexity and lower leg discomfort and swelling. The footrest led to significant changes in amount of COP variability. Both interventions significantly reduced back discomfort. Overall, the nonlinear analysis of postural changes provided different findings compared to linear ones, considering the standing time, interventions and standing job experience. Nonlinear results were consistent with leg discomfort and swelling.Practitioner summary: The effect of movement-based interventions on dynamics of postural alterations over prolonged standing were characterised using nonlinear techniques. The effect of standing work experience was also considered. Previous experience of standing jobs and leg movements increase the complexity of postural behaviour over standing period.

Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis.

Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the classification. In this proof-of-concept preliminary study based on secondary analysis, 20 microstate features were extracted from 14 SZ patients and 14 healthy controls' EEG signals. These parameters were then ranked as predictors based on their importance, and an optimized machine learning approach was applied to evaluate the performance of the classification. SZ patients had altered microstate features compared to healthy controls. Furthermore, Bayesian optimization outperformed conventional multivariate analyses and showed the highest accuracy (90.93%), AUC (0.90), sensitivity (91.37%), and specificity (90.48%), with reliable results using just six microstate predictors. Altogether, in this proof-of-concept study, we showed that machine learning with Bayesian optimization can be utilized to characterize EEG microstate alterations and contribute to the classification of SZ patients.

Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study.

Cortico-muscular interactions play important role in sensorimotor control during motor task and are commonly studied by cortico-muscular coherence (CMC) method using joint electroencephalogram-surface electromyogram (EEG-sEMG) signals. As noise and time delay between the two signals weaken the CMC value, coupling difference between non-task sEMG channels is often undetectable. We used sparse representation of EEG channels to compute CMC and detect coupling for task-related and non-task sEMG signals. High-density joint EEG-sEMG (53 EEG channels, 4 sEMG bipolar channels) signals were acquired from 15 subjects (30.26 ± 4.96 years) during four specific hand and foot contraction tasks (2 dynamic and 2 static contraction). Sparse representations method was applied to detect projection of EEG signals on each sEMG channel. Bayesian optimization was employed to select best-fitted method with tuned hyperparameters on the input feeding data while using 80% data as the train set and 20% as test set. K-fold (K = 5) cross-validation method was used for evaluation of trained model. Two models were trained separately, one for CMC data and the other from sparse representation of EEG channels on each sEMG channel. Sensitivity, specificity, and accuracy criteria were obtained for test dataset to evaluate the performance of task-related and non-task sEMG channels detection. Coupling values were significantly different between grand average of task-related compared to the non-task sEMG channels (Z = -6.33, p< 0.001, task-related median = 2.011, non-task median = 0.112). Strong coupling index was found even in single trial analysis. Sparse representation approach (best fitted model: SVM, Accuracy = 88.12%, Sensitivity = 83.85%, Specificity = 92.45%) outperformed CMC method (best fitted model: KNN, Accuracy = 50.83%, Sensitivity = 52.17%, Specificity = 49.47%). Sparse representation approach offers high performance to detect CMC for discerning the EMG channels involved in the contraction tasks and non-tasks.

Design and ergonomic assessment of a passive head/neck supporting exoskeleton for overhead work use.

Overhead work is an important risk factor associated with musculoskeletal disorders of the neck and shoulder region. This study aimed to propose and evaluate a passive head/neck supporting exoskeleton (HNSE) as a potential ergonomic intervention for overhead work applications. Fourteen male participants were asked to perform a simulated overhead task of fastening/unfastening nut in 4 randomized sessions, characterized by two variables: neck extension angle (40% and 80% of neck maximum range of motion) and exoskeleton condition (wearing and not wearing the HNSE). Using the HNSE, significantly alleviated perceived discomfort in the neck (p-value = 0.009), right shoulder (p-value = 0.05) and left shoulder (p-value = 0.02) and reduced electromyographic activity of the right (p-value = 0.005) and left (p-value = 0.01) sternocleidomastoid muscles. However, utilizing the exoskeleton caused a remarkable increase in right (p-value = 0.04) and left (p-value = 0.05) trapezius electromyographic activities. Performance was not significantly affected by the HNSE. Although the HNSE had promising effects with respect to discomfort and muscular activity in the static overhead task, future work is still needed to investigate its effect on performance and to provide support for the generalizability of study results.

A Comprehensive Evaluation of Spine Kinematics, Kinetics, and Trunk Muscle Activities During Fatigue-Induced Repetitive Lifting.

OBJECTIVE: Spine kinematics, kinetics, and trunk muscle activities were evaluated during different stages of a fatigue-induced symmetric lifting task over time. BACKGROUND: Due to neuromuscular adaptations, postural behaviors of workers during lifting tasks are affected by fatigue. Comprehensive aspects of these adaptations remain to be investigated. METHOD: Eighteen volunteers repeatedly lifted a box until perceived exhaustion. Body center of mass (CoM), trunk and box kinematics, and feet center of pressure (CoP) were estimated by a motion capture system and force-plate. Electromyographic (EMG) signals of trunk/abdominal muscles were assessed using linear and nonlinear approaches. The L5-S1 compressive force (Fc) was predicted via a biomechanical model. A two-way multivariate analysis of variance (MANOVA) was performed to examine the effects of five blocks of lifting cycle (C1 to C5) and lifting trial (T1 to T5), as independent variables, on kinematic, kinetic, and EMG-related measures. RESULTS: Significant effects of lifting trial blocks were found for CoM and CoP shift in the anterior-posterior direction (respectively p < .001 and p = .014), trunk angle (p = .004), vertical box displacement (p < .001), and Fc (p = .005). EMG parameters indicated muscular fatigue with the extent of changes being muscle-specific. CONCLUSION: Results emphasized variations in most kinematics/kinetics, and EMG-based indices, which further provided insight into the lifting behavior adaptations under dynamic fatiguing conditions. APPLICATION: Movement and muscle-related variables, to a large extent, determine the magnitude of spinal loading, which is associated with low back pain.

Determining the interactions between postural variability structure and discomfort development using nonlinear analysis techniques during prolonged standing work.

PURPOSE: Nonlinear analysis techniques provide a powerful approach to explore dynamics of posture-related time-varying signals. The aim of this study was to investigate the fundamental interactions between postural variability structure and discomfort development during prolonged standing. METHODS: Twenty participants, with equal distribution for gender and standing work experience (SWE), completed a simulated long-term standing test. Low back and legs discomfort, center of pressure, lumbar curvature, and EMG activity of trunk and leg muscles were monitored. Nonlinear measures including largest lyapunov exponent, multi-scale entropy, and detrended fluctuation analysis were applied to characterize the variability structure (i.e., complexity) in each signal. The size (i.e., amount) of variability was also computed using traditional linear metrics. RESULTS: With progress of low back and legs discomfort over standing periods, significant lower levels were perceived by the participants having SWE. The amount of variability in all signals (except external oblique EMG activity) were significantly increased with the time progress for all participants. The structure of variability in most signals demonstrated a lower complexity (more regularity) with fractal properties that deviated from 1/f noise. The SWE group showed a higher complexity levels. CONCLUSIONS: Overall, the findings verified variations in structure and amount of the postural variability. However, nonlinear analysis identified postural strategies according to the perceived discomfort in a different way. These results provide supports for future application of nonlinear tools in evaluating standing tasks and related ergonomics interventions as it allows further insight into how discomfort development impact the structure of postural changes.

Toward New Modalities in VEP-Based BCI Applications Using Dynamical Stimuli: Introducing Quasi-Periodic and Chaotic VEP-Based BCI.

Visual evoked potentials (VEPs) to periodic stimuli are commonly used in brain computer interfaces for their favorable properties such as high target identification accuracy, less training time, and low surrounding target interference. Conventional periodic stimuli can lead to subjective visual fatigue due to continuous and high contrast stimulation. In this study, we compared quasi-periodic and chaotic complex stimuli to common periodic stimuli for use with VEP-based brain computer interfaces (BCIs). Canonical correlation analysis (CCA) and coherence methods were used to evaluate the performance of the three stimulus groups. Subjective fatigue caused by the presented stimuli was evaluated by the Visual Analogue Scale (VAS). Using CCA with the M2 template approach, target identification accuracy was highest for the chaotic stimuli (M = 86.8, SE = 1.8) compared to the quasi-periodic (M = 78.1, SE = 2.6, p = 0.008) and periodic (M = 64.3, SE = 1.9, p = 0.0001) stimulus groups. The evaluation of fatigue rates revealed that the chaotic stimuli caused less fatigue compared to the quasi-periodic (p = 0.001) and periodic (p = 0.0001) stimulus groups. In addition, the quasi-periodic stimuli led to lower fatigue rates compared to the periodic stimuli (p = 0.011). We conclude that the target identification results were better for the chaotic group compared to the other two stimulus groups with CCA. In addition, the chaotic stimuli led to a less subjective visual fatigue compared to the periodic and quasi-periodic stimuli and can be suitable for designing new comfortable VEP-based BCIs.

Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction.

Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes.

Use of Sine Shaped High-Frequency Rhythmic Visual Stimuli Patterns for SSVEP Response Analysis and Fatigue Rate Evaluation in Normal Subjects.

Background: Recent EEG-SSVEP signal based BCI studies have used high frequency square pulse visual stimuli to reduce subjective fatigue. However, the effect of total harmonic distortion (THD) has not been considered. Compared to CRT and LCD monitors, LED screen displays high-frequency wave with better refresh rate. In this study, we present high frequency sine wave simple and rhythmic patterns with low THD rate by LED to analyze SSVEP responses and evaluate subjective fatigue in normal subjects. Materials and Methods: We used patterns of 3-sequence high-frequency sine waves (25, 30, and 35 Hz) to design our visual stimuli. Nine stimuli patterns, 3 simple (repetition of each of above 3 frequencies e.g., P25-25-25) and 6 rhythmic (all of the frequencies in 6 different sequences e.g., P25-30-35) were chosen. A hardware setup with low THD rate (<0.1%) was designed to present these patterns on LED. Twenty two normal subjects (aged 23-30 (25 ± 2.1) yrs) were enrolled. Visual analog scale (VAS) was used for subjective fatigue evaluation after presentation of each stimulus pattern. PSD, CCA, and LASSO methods were employed to analyze SSVEP responses. The data including SSVEP features and fatigue rate for different visual stimuli patterns were statistically evaluated. Results: All 9 visual stimuli patterns elicited SSVEP responses. Overall, obtained accuracy rates were 88.35% for PSD and > 90% for CCA and LASSO (for TWs > 1 s). High frequency rhythmic patterns group with low THD rate showed higher accuracy rate (99.24%) than simple patterns group (98.48%). Repeated measure ANOVA showed significant difference between rhythmic pattern features (P < 0.0005). Overall, there was no significant difference between the VAS of rhythmic [3.85 ± 2.13] compared to the simple patterns group [3.96 ± 2.21], (P = 0.63). Rhythmic group had lower within group VAS variation (min = P25-30-35 [2.90 ± 2.45], max = P35-25-30 [4.81 ± 2.65]) as well as least individual pattern VAS (P25-30-35). Discussion and Conclusion: Overall, rhythmic and simple pattern groups had higher and similar accuracy rates. Rhythmic stimuli patterns showed insignificantly lower fatigue rate than simple patterns. We conclude that both rhythmic and simple visual high frequency sine wave stimuli require further research for human subject SSVEP-BCI studies.