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 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    0.05, 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

The changes in classical and nonlinear parameters after a maximal bout to elicit fatigue in competitive swimming

The aim was to assess the effect of fatigue on linear and nonlinear parameters in swimming. Twenty-four fitness-oriented swimmers performed a maximal bout of 100m at front-crawl to elicit fatigue. Before (pre-) and immediately after (post-test) the bout, participants swam an allout 25m to derive the speed fluctuation (dv), approximate entropy (ApEn) and fractal dimension (FD) from the speed-time series collected by a speedo-meter. Swim speed was 10.85% slower in the post-test than in the pre-test (p < .001, η2=0.72). There was an effect of the fatigue on the dv with a moderate effect size. The dv increased shifting the 95CI band from 0.116–0.134 to 0.140–0.161. The ApEn showed non-significant variations between the pre- and post-test having the 95CI of pre- and post-test overlapped (pre: 0.659–0.700; post: 0.641–0.682). The FD showed as well a significant variation (the 95CI moved from 1.954–1.965 to 1.933–1.951). It can be concluded that in swimming there are changes in classical and nonlinear parameters under fatigue.This research was funded by the NIE AcRF grant (RI 11/13 TB)info:eu-repo/semantics/acceptedVersio

Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 2

Extraction of the movement-related high-gamma (80 - 160 Hz) in electroencephalogram (EEG) from traumatic brain injury (TBI) patients who have had hemicraniectomies, remains challenging due to a confounding bandwidth overlap with surface electromyogram (EMG) artifacts related to facial and head movements. In part 1, we described an augmented independent component analysis (ICA) approach for removal of EMG artifacts from EEG, and referred to as EMG Reduction by Adding Sources of EMG (ERASE). Here, we tested ERASE on EEG recorded from six TBI patients with hemicraniectomies while they performed a thumb flexion task. ERASE removed a mean of 52 +/- 12% (mean +/- S.E.M) (maximum 73%) of EMG artifacts. In contrast, conventional ICA removed a mean of 27 +/- 19\% (mean +/- S.E.M) of EMG artifacts from EEG. In particular, high-gamma synchronization was significantly improved in the contralateral hand motor cortex area within the hemicraniectomy site after ERASE was applied. We computed fractal dimension (FD) of EEG high-gamma on each channel. We found relative FD of high-gamma over hemicraniectomy after applying ERASE were strongly correlated to the amplitude of finger flexion force. Results showed that significant correlation coefficients across the electrodes related to thumb flexion averaged 0.76, while the coefficients across the homologous electrodes in non-hemicraniectomy areas were nearly 0. Across all subjects, an average of 83% of electrodes significantly correlated with force was located in the hemicraniectomy areas after applying ERASE. After conventional ICA, only 19% of electrodes with significant correlations were located in the hemicraniectomy. These results indicated that the new approach isolated electrophysiological features during finger motor activation while selectively removing confounding EMG artifacts

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

Assessment of Driver’s Drowsiness Based on Fractal Dimensional Analysis of Sitting and Back Pressure Measurements

The most effective way of preventing motor vehicle accidents caused by drowsy driving is through a better understanding of drowsiness itself. Prior research on the detection of symptoms of drowsy driving has offered insights on providing drivers with advance warning of an elevated risk of crash. The present study measured back and sitting pressures during a simulated driving task under both high and low arousal conditions. Fluctuation of time series of center of pressure (COP) movement of back and sitting pressure was observed to possess a fractal property. The fractal dimensions were calculated to compare the high and low arousal conditions. The results showed that under low arousal (the drowsiness state) the fractal dimension was significantly lower than what was calculated with high arousal. Accumulated drowsiness thus contributed to the loss of self-similarity and unpredictability of time series of back and sitting pressure measurement. Drowsiness further reduces the complexity of the posture control system as viewed from back and sitting pressure. Thus, fractal dimension is a necessary and sufficient condition of a decreased arousal level. It further is a necessary condition for detecting the interval or point in time with high risk of crash

DEVELOPMENT OF AN EEG BRAIN-MACHINE INTERFACE TO AID IN RECOVERY OF MOTOR FUNCTION AFTER NEUROLOGICAL INJURY

Impaired motor function following neurological injury may be overcome through therapies that induce neuroplastic changes in the brain. Therapeutic methods include repetitive exercises that promote use-dependent plasticity (UDP), the benefit of which may be increased by first administering peripheral nerve stimulation (PNS) to activate afferent fibers, resulting in increased cortical excitability. We speculate that PNS delivered only in response to attempted movement would induce timing-dependent plasticity (TDP), a mechanism essential to normal motor learning. Here we develop a brain-machine interface (BMI) to detect movement intent and effort in healthy volunteers (n=5) from their electroencephalogram (EEG). This could be used in the future to promote TDP by triggering PNS in response to a patient’s level of effort in a motor task. Linear classifiers were used to predict state (rest, sham, right, left) based on EEG variables in a handgrip task and to determine between three levels of force applied. Mean classification accuracy with out-of-sample data was 54% (23-73%) for tasks and 44% (21-65%) for force. There was a slight but significant correlation (p\u3c0.001) between sample entropy and force exerted. The results indicate the feasibility of applying PNS in response to motor intent detected from the brain

Fractal features of surface electromyogram: a new measure for low level muscle activation

Identifying finger and wrist flexion based actions using single channel surface electromyogram have a number of rehabilitation, defence and human computer interface applications. These applications are currently infeasible because of unreliability in classification of sEMG when the level of muscle contraction is low and when there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during maintained wrist and finger flexion. It has been established in literature that surface electromyogram (sEMG) and other such biosignals are fractal signals. Some researchers have determined that fractal dimension (FD) is related to strength of muscle contraction. On careful analysis of fractal properties of sEMG, this research work has established that FD is related to the muscle size and complexity and not to the strength of muscle contraction. The work has also identified a novel feature, maximum fractal length (MFL) of the signal, as a good measure of strength of contraction of the muscle. From the analysis, it is observed that while at high level of contraction, root mean square (RMS) is an indicator of strength of contraction of the muscle, this relationship is not very strong when the muscle contraction is less than 50% maximum voluntary contraction. This work has established that MFL is a more reliable measure of strength of contraction compared to RMS, especially at low levels of contraction. This research work reports the use of fractal properties of sEMG to identify the small changes in strength of muscle contraction and the location of the active muscles. It is observed that fractal dimension (FD) of the signal is related with the properties of the muscle while maximum fractal length (MFL) is related to the strength of contraction of the associated muscle. The results show that classifying MFL and FD of a single channel sEMG from the forearm it is possible to accurately identify a set of finger and wrist flexion based actions even when the muscle activity is very weak. It is proposed that such a system could be used to control a prosthetic hand or for human computer interface

Topological Changes in the Functional Brain Networks Induced by Isometric Force Exertions Using a Graph Theoretical Approach: An EEG-based Neuroergonomics Study

Neuroergonomics, the application of neuroscience to human factors and ergonomics, is an emerging science focusing on the human brain concerning performance at work and in everyday settings. The advent of portable neurophysiological methods, including electroencephalography (EEG), has enabled measurements of real-time brain activity during physical tasks without restricting body movements. However, the EEG signatures of different physical exertion activity levels that involve the musculoskeletal system in everyday settings remain poorly understood. Furthermore, the assessment of functional connectivity among different brain regions during different force exertion levels remains unclear. One approach to investigating the brain connectome is to model the underlying mechanism of the brain as a complex network. This study applied employed a graph-theoretical approach to characterize the topological properties of the functional brain network induced by predefined force exertion levels, namely extremely light (EL), light (L), somewhat hard (SWH), hard (H), and extremely hard (EH) in two frequency bands, i.e., alpha and beta. Twelve female participants performed an isometric force exertion task and rated their perception of physical comfort at different physical exertion levels. A CGX-Mobile-64 EEG was used for recording spontaneous brain electrical activity. After preprocessing the EEG data, a source localization method was applied to study the functional brain connectivity at the source level. Subsequently, the alpha and beta networks were constructed by calculating the coherence between all pairs of 84 brain regions of interests that were selected using Brodmann Areas. Graph -theoretical measures were then employed to quantify the topological properties of the functional brain networks at different levels of force exertions at each frequency band. During an \u27extremely hard\u27 exertion level, a small-world network was observed for the alpha coherence network, whereas an ordered network was observed for the beta coherence network. The results suggest that high-level force exertions are associated with brain networks characterized by a more significant clustering coefficient, more global and local efficiency, and shorter characteristic path length under alpha coherence. The above suggests that brain regions are communicating and cooperating to a more considerable degree when the muscle force exertions increase to meet physically challenging tasks. The exploration of the present study extends the current understanding of the neurophysiological basis of physical efforts with different force levels of human physical exertion to reduce work-related musculoskeletal disorders

Contributions to the Development of Objective Techniques for Presence Measurement in Virtual Environments by means of Brain Activity Analysis

En esta tesis, se propone el uso de la técnica de Doppler transcraneal (DTC) para monitorizar la actividad cerebral durante la exposición a entornos virtuales (EV) y así poder analizar los correlatos cerebrales del sentido de presencia. Las hipótesis de partida son las siguientes: 1) DTC se podrá utilizar fácilmente en combinación con sistemas de realidad virtual. 2) Los datos de velocidad de flujo sanguíneo medidos por DTC se podrán utilizar para analizar cambios de actividad cerebral durante la exposición a EV. 3) Habrá diferencias en la velocidad del flujo sanguíneo asociadas a distintos niveles de presencia. 4) Habrá correlación entre el grado de presencia medido por cuestionarios y parámetros de la velocidad de flujo sanguíneo. 5) Cada componente de la experiencia virtual tendrá una influencia en las variaciones de velocidad observadas. Para analizar las hipótesis planteadas, se realizaron cuatro experimentos distintos, en los que se analizó la velocidad del flujo sanguíneo durante: 1) distintas condiciones de navegación, 2) distintas condiciones de inmersión, 3) una tarea de percepción visual y 4) tareas motoras para manejo de un joystick. Durante la tesis, se han propuesto distintas técnicas de procesado de señal basadas en análisis espectral y en la obtención parámetros no lineales de la señal, que no habían sido utilizadas previamente en experimentos psicofisiológicos con DTC. Se ha observado que existe un incremento en la velocidad del flujo sanguíneo durante la exposición a un EV, el cual puede deberse a distintos factores que intervienen en la experiencia: tareas de interacción visuoespacial, tareas de atención, la creación y ejecución de un plan motor, cambios emocionales Los análisis han mostrado que existen correlaciones significativas entre la velocidad media de flujo sanguíneo en las arterias cerebrales medias durante la exposición al EV y respuestas a los cuestionarios de presencia utilizados.Rey Solaz, B. (2010). Contributions to the Development of Objective Techniques for Presence Measurement in Virtual Environments by means of Brain Activity Analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8505Palanci