Nonlinear features of surface EEG showing systematic brain signal adaptations with muscle force and fatigue

Influence of compression garments on fatigue behaviour during running based on nonlinear dynamical analysis

The purpose of this study was to assess quantitatively the effects of compression garments (CGs) on fatigue behaviour during sport activities such as running, which are the subject of a series of qualitative and physiological studies. A quantitative biomechanical analysis of the effects of CGs could assist coaches and athletes to adopt these types of performance enhancement garments. In this research, kinematic changes are measured using 2D phase portraits to study the influence of CGs on fatigue behaviour. Fifteen healthy male intermediate athletes participated in this study and the kinematic data of hip repetitive movements with and without CG were measured during running tasks. These data are used to reconstruct the state space and the local flow variation method is adopted to quantify the trajectory drifts caused by fatigue in the state space. The effects of CGs on the complexity of kinematic changes are also evaluated using permutation entropy. The results indicate that fluctuations in the kinematics are reduced when compression garments are used. It is also shown that adoption of CGs results in a reduction of the rate of fatigue development and decreased complexities in the movement kinematics.

Isometric Arm Forces Exerted by Females at Different Levels of Physical Comfort and Their EEG Signatures

A variety of subjective measures have traditionally been used to assess the perception of physical exertion at work and related body responses. However, the current understanding of physical comfort experienced at work is very limited. The main objective of this study was first to investigate the magnitude of isometric arm forces exerted by females at different levels of physical comfort measured on a new comfort scale and, second, to assess their corresponding neural signatures expressed in terms of power spectral density (PSD). The study assessed PSDs of four major electroencephalography (EEG) frequency bands, focusing on the brain regions controlling motor and perceptual processing. The results showed statistically significant differences in exerted arm forces and the rate of perceived exertion at the various levels of comfort. Significant differences in power spectrum density at different physical comfort levels were found for the beta EEG band. Such knowledge can be useful in incorporating female users' force requirements in the design of consumer products, including tablets, laptops, and other hand-held information technology devices, as well as various industrial processes and work systems.

EEG Spectral Feature Modulations Associated With Fatigue in Robot-Mediated Upper Limb Gross and Fine Motor Interactions

This paper investigates the EEG spectral feature modulations associated with fatigue induced by robot-mediated upper limb gross and fine motor interactions. Twenty healthy participants were randomly assigned to perform a gross motor interaction with HapticMASTER or a fine motor interaction with SCRIPT passive orthosis for 20 min or until volitional fatigue. Relative and ratio band power measures were estimated from the EEG data recorded before and after the robot-mediated interactions. Paired-samples t-tests found a significant increase in the relative alpha band power and a significant decrease in the relative delta band power due to the fatigue induced by the robot-mediated gross and fine motor interactions. The gross motor task also significantly increased the (θ + α)/β and α/β ratio band power measures, whereas the fine motor task increased the relative theta band power. Furthermore, the robot-mediated gross movements mostly changed the EEG activity around the central and parietal brain regions, whereas the fine movements mostly changed the EEG activity around the frontopolar and central brain regions. The subjective ratings suggest that the gross motor task may have induced physical fatigue, whereas the fine motor task may have induced mental fatigue. Therefore, findings affirm that changes to localised brain activity patterns indicate fatigue developed from the robot-mediated interactions. It can also be concluded that the regional differences in the prominent EEG spectral features are most likely due to the differences in the nature of the task (fine/gross motor and distal/proximal upper limb) that may have differently altered an individual's physical and mental fatigue level. The findings could potentially be used in future to detect and moderate fatigue during robot-mediated post-stroke therapies.

The Relation between Chaotic Feature of Surface EEG and Muscle Force: Case Study Report

Background:

Nonlinear dynamics, especially the chaos characteristics, are useful in analyzing bio-potentials with many complexities. In this study, the evaluation of arm-tip force estimation method from the electroencephalography (EEG) signal in the vertical plane has been studied and chaos characteristics, including fractal dimension, Lyapunov exponent, entropy, and correlation dimension characteristics of EEG signals have been measured and analyzed at different levels of forces.

Method:

Electromyography signal was recorded with the help of the BIOPEC device (the Mp-100 model) and from the forearm muscle with surface electrodes, and the EEG signals were recorded from five major motor-related cortical areas according to 10–20 standard three times in a normal healthy 33-year-old male, athlete and right handed simultaneously with importing a force to 10 sinkers weighing from 10 to 100 Newton with step 10 Newton.

Results:

The findings confirm that force estimation through EEG signals is feasible, especially using fractal dimension feature. The R-squared values for Fractal dimension, Lyapunov exponent, and entropy and correlation dimension features for linear trend line were 0.93, 0.7, 0.86, and 0.41, respectively.

Conclusion:

The linear increase of characteristics especially fractal dimension and entropy, together with the results from other EEG and neuroimaging studies, suggests that under normal conditions, brain recruits motor neurons at a linear progress when increasing the force.

A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals

Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.

Quantification of the effect of compression garments on fatigue behavior in cycling

Effectiveness of compression garments to enhance athletic performance is the subject of numerous qualitative studies. This study aims at quantification of the effect of compression garments using nonlinear dynamics approach. Kinematic data of fifteen healthy male athletes was obtained and the state space was reconstructed. The trajectory drifts caused by fatigue in the state space were quantified using local flow variation technique. The study illustrates that compression garments (CGs) decrease rate of fatigue development and the body exhibits a more restricted complexity (more predictable and smaller fluctuations) when CGs are worn.

Neuroergonomics Applications of Electroencephalography in Physical Activities: A Systematic Review

Recent years have seen increased interest in neuroergonomics, which investigates the brain activities of people engaged in diverse physical and cognitive activities at work and in everyday life. The present work extends upon prior assessments of the state of this art. However, here we narrow our focus specifically to studies that use electroencephalography (EEG) to measure brain activity, correlates, and effects during physical activity. The review uses systematically selected, openly published works derived from a guided search through peer-reviewed journals and conference proceedings. Identified studies were then categorized by the type of physical activity and evaluated considering methodological and chronological aspects via statistical and content-based analyses. From the identified works (n = 110), a specific number (n = 38) focused on less mobile muscular activities, while an additional group (n = 22) featured both physical and cognitive tasks. The remainder (n = 50) investigated various physical exercises and sporting activities and thus were here identified as a miscellaneous grouping. Most of the physical activities were isometric exertions, moving parts of upper and lower limbs, or walking and cycling. These primary categories were sub-categorized based on movement patterns, the use of the event-related potentials (ERP) technique, the use of recording methods along with EEG and considering mental effects. Further information on subjects' gender, EEG recording devices, data processing, and artifact correction methods and citations was extracted. Due to the heterogeneous nature of the findings from various studies, statistical analyses were not performed. These were thus included in a descriptive fashion. Finally, contemporary research gaps were pointed out, and future research prospects to address those gaps were discussed.

Effects of force load, muscle fatigue and extremely low frequency magnetic stimulation on EEG signals during side arm lateral raise task

OBJECTIVE

This study was to quantitatively investigate the effects of force load, muscle fatigue and extremely low frequency (ELF) magnetic stimulation on electroencephalography (EEG) signal features during side arm lateral raise task.

APPROACH

EEG signals were recorded by a BIOSEMI Active Two system with Pin-Type active-electrodes from 18 healthy subjects when they performed the right arm side lateral raise task (90° away from the body) with three different loads (0 kg, 1 kg and 3 kg; their order was randomized among the subjects) on the forearm. The arm maintained the loads until the subject felt exhausted. The first 10 s recording for each load was regarded as non-fatigue status and the last 10 s before the subject was exhausted as fatigue status. The subject was then given a 5 min resting between different loads. Two days later, the same experiment was performed on each subject except that ELF magnetic stimulation was applied to the subject's deltoid muscle during the 5 min resting period. EEG features from C3 and C4 electrodes including the power of alpha, beta and gamma and sample entropy were analyzed and compared between different loads, non-fatigue/fatigue status, and with/without ELF magnetic stimulation.

MAIN RESULTS

The key results were associated with the change of the power of alpha band. From both C3-EEG and C4-EEG, with 1 kg and 3 kg force loads, the power of alpha band was significantly smaller than that from 0 kg for both non-fatigue and fatigue periods (all p  <  0.05). However, no significant difference of the power in alpha between 1 kg and 3 kg was observed (p  >  0.05 for all the force loads except C4-EEG with ELF simulation). The power of alpha band at fatigue status was significantly increased for both C3-EEG and C4-EEG when compared with the non-fatigue status (p  <  0.01 for all the force loads except 3 kg force from C4-EEG). With magnetic stimulation, the powers of alpha from C3-EEG and C4-EEG were significantly decreased than without stimulation (all p  <  0.05), and the difference in the power of alpha between fatigue and non-fatigue status disappeared with 1 kg and 3 kg force loads, The powers of beta and gamma bands and SampEn were not significantly different between different force loads, between fatigue and non-fatigue status, and between with and without ELF magnetic stimulation (all p  >  0.05, except between non-fatigue and fatigue with magnetic stimulation in gamma band of C3-EEG at 1 kg, and in the SampEn at 1 kg and 3 kg force loads from C4-EEG).

SIGNIFICANCE

Our study comprehensively quantified the effects of force, fatigue and the ELF magnetic stimulation on EEG features with difference forces, fatigue status and ELF magnetic stimulation.

Local State Space Temporal Fluctuations: A Methodology to Reveal Changes During a Fatiguing Repetitive Task

The effect of muscular fatigue on temporal and spectral features of muscle activities and motor performance, i.e., kinematics and kinetics, has been studied. It is of value to quantify fatigue related kinematic changes in biomechanics and sport sciences using simple measurements of joint angles. In this work, a new approach was introduced to extract kinematic changes from 2D phase portraits to study the fatigue adaptation patterns of subjects performing elbow repetitive movement. This new methodology was used to test the effect of load and repetition rate on the temporal changes of an elbow phase portrait during a dynamic iso-inertial fatiguing task. The local flow variation concept, which quantifies the trajectory shifts in the state space, was used to track the kinematic changes of an elbow repetitive fatiguing task in four conditions (two loads and two repetition rates). Temporal kinematic changes due to muscular fatigue were measured as regional curves for various regions of the phase portrait and were also expressed as a single curve to describe the total drift behavior of trajectories due to fatigue. Finally, the effect of load and repetition rate on the complexity of kinematic changes, measured by permutation entropy, was tested using analysis of variance with repeated measure design. Statistical analysis showed that kinematic changes fluctuated more (showed more complexity) under higher loads (p=0.014), but did not differ under high and low repetition rates (p=0.583). Using the proposed method, new features for complexity of kinematic changes could be obtained from phase portraits. The local changes of trajectories in epochs of time reflected the temporal kinematic changes in various regions of the phase portrait, which can be used for qualitative and quantitative assessment of fatigue adaptation of subjects and evaluation of the influence of task conditions (e.g., load and repetition rate) on kinematic changes.