Motor Unit Tracking Using High Density Surface Electromyography (HDsEMG) . Automated Correction of Electrode Displacement Errors.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Ad-vanced Methods for Neural Signals and Images". OBJECTIVES: The study discusses a technique to automatically correct for effects of electrode grid displacement across serial surface EMG measurements with high-density electrode arrays (HDsEMG). The goal is to match motor unit signatures from subsequent measurements and by this, achieve automated motor unit tracking. METHODS: Test recordings of voluntary muscle contractions using HDsEMG were performed on three healthy individuals. Electrode grid displacements were mimicked in repeated recordings while measuring the exact position of the grid. A concept of accounting for translational and rotational displacements by making the projection of the recorded motor unit action potentials is first introduced. Then, this concept was tested for the performed measurements attempting the automated matching of the similar motor unit action potentials across different trials. RESULTS: The ability to perform automated correction (projection) of the isolated motor unit action potentials was first shown using large angular displacements. Then, for accidental (small) displacements of the recording grid, the ability to automatically track motor units across different measurement trials was shown. It was possible to track 10 -15% of identified motor units. CONCLUSIONS: This proof of concept study demonstrates an automated correction allowing the identification of an increased number of same motor unit action potentials across different measurements. By this, great potential is demonstrated for assisting motor unit tracking studies, indicating that otherwise electrode displacements cannot always be precisely described.

Improving spatiotemporal characterization of cognitive processes with data-driven EEG-fMRI analysis.

To fully understand the cognitive processes occurring in the human brain, high resolution in both spatial and temporal information is needed. Most neuroimaging approaches, however, only possess high accuracy in one of these two domains. Therefore, the multimodal analysis of brain activity is becoming more and more popular among the research community. One of these approaches concerns the integration of simultaneously acquired electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data. This combination poses a series of challenges, ranging from recovering data quality to the fusion of two types of data of a completely different nature. In this work, several of these challenges will be addressed, and an overview of different integration approaches is provided.

The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task.

Simultaneous EEG-fMRI measurements can combine the high spatial resolution of fMRI with the high temporal resolution of EEG. Therefore, we applied this approach to the study of peripheral vision. More specifically, we presented visual field quadrant fragments of checkerboards and a full central checkerboard in a simple detection task. A technique called "integration-by-prediction" was used to integrate EEG and fMRI data. In particular, we used vectors of single-trial ERP amplitude differences between left and right occipital electrodes as regressors in an ERP-informed fMRI analysis. The amplitude differences for the regressors were measured at the latencies of the visual P1 and N1 components. Our results indicated that the traditional event-related fMRI analysis revealed mostly activations in the vicinity of the primary visual cortex and in the ventral visual stream, while both P1 and N1 regressors revealed activation of areas in the temporo-parietal junction. We conclude that simultaneous EEG-fMRI in a spatial detection task can separate visual processing at 100-200 ms from stimulus onset from the rest of the information processing in the brain.

Assessment of pain expression in infant cry signals using empirical mode decomposition.

BACKGROUND: The presence of decoupling, i.e. the absence of coupling between fundamental frequency variation and intensity contour during phonetic crying, and its extent, reflects the degree of maturation of the central nervous system. OBJECTIVES: The aim of this work was to evaluate whether Empirical Mode Decomposition (EMD) is a suitable technique for analyzing infant cries. We hereby wanted to assess the existence and extent of decoupling in term neonates and whether an association between decoupling (derived from EMD) and clinical pain expression could be unveiled. METHODS: To assess decoupling in healthy term neonates during procedural pain, 24 newborns were videotaped and crying was recorded during venous blood sampling. Besides acoustic analysis, pain expression was quantified based on the Modified Behavioral Pain Scale (MBPS). Fundamental frequency and the intensity contour of the cry signals were extracted by applying the EMD to the data, and the correlation between the two was studied. RESULTS: Based on data collected in healthy term neonates, correlation coefficients varied between 0.39 and 0.83. The degree of decoupling displayed extended variability between the neonates and also in different cry bouts in a crying sequence within an individual neonate. CONCLUSION: When considering the individual ratio between the mean correlation of cry bouts during a crying sequence and their standard deviation, there seems to be a positive trend with increasing MBPS value. This might indicate that higher stressed subjects have less consistency in the investigated acoustic cry features, concluding that EMD has potential in the assessment of infant cry analysis.

Synergistic control of forearm based on accelerometer data and artificial neural networks.

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

3D computer modeling of sitting working place.

Ergonomic contribution to designing and modeling of sitting working place by use of a computer and computer programs have been presented in this work. The influences of modeling working places on regular posture of a man/woman during work have been reconsidered, so that consumption of energy and fatigue are brought down to a minimum. For that purpose a computer program has been made which with input data on various kinds of work, sex and height of a worker determines the optimal ergonomic parameters during the modeling of a sitting working place. By computer visualisation the values of angle of spine curving have been calculated, the manipulation angle of arms and legs for three anthropometric heights of workers (160 cm, 175 cm and 190 cm). The dimensions of manipulative body space have been established by computerised 3D anthropometric analysis of movement as for example, reach of arms, legs, head, back etc positions. In this process the dimensions of machine and working space surrounding it in respect to optimal utilisation have been put in accordance with the anthropometric size of a man/woman.

Visualization of anthropometric measures of workers in computer 3D modeling of work place.

In this work, 3D visualization of a work place by means of a computer-made 3D-machine model and computer animation of a worker have been performed. By visualization of 3D characters in inverse kinematic and dynamic relation with the operating part of a machine, the biomechanic characteristics of worker's body have been determined. The dimensions of a machine have been determined by an inspection of technical documentation as well as by direct measurements and recordings of the machine by camera. On the basis of measured body height of workers all relevant anthropometric measures have been determined by a computer program developed by the authors. By knowing the anthropometric measures, the vision fields and the scope zones while forming work places, exact postures of workers while performing technological procedures were determined. The minimal and maximal rotation angles and the translation of upper and lower arm which are basis for the analysis of worker burdening were analyzed. The dimensions of the seized space of a body are obtained by computer anthropometric analysis of movement, e.g. range of arms, position of legs, head, back. The influence of forming of a work place on correct postures of workers during work has been reconsidered and thus the consumption of energy and fatigue can be reduced to a minimum.

Application of ergonomics principles of burdening of the worker in the process of clothes cutting.

In the technological procedure of clothes cutting, lifting, transferring and putting of textile bundles on the machine for laying of cutting layers are considered to be the most difficult physical tasks. Upon standing position of the worker, centre of the mass is relatively high and hydrostatic pressure of blood column is increased influencing relatively increased blood circulation at the end of extremities. High incidence of muscle-bone and other health problems occur although this job has been classified as an easy one according to the criteria of energy consumption. These are ever growing cause of degenerative diseases of the vertebral column, absence from the work and early invalidity. Distortion of vertebral column towards right and left as well as forward in cervical and lumbar part of the body have been investigated upon monitoring of the work, as well as rotation of dorsal part of the body on the workers in the technological process of clothes cutting. Corresponding mathematical formulation has been shown for this investigation. Size of the distortion and rotation angles were investigated on four workers who perform various technological clothes cutting operations on two variously formed working places. Based on the monitoring of the workers values of vertebral column during working time were established, and also maximum distortion and rotation angles at the beginning and at the end of carrying out of cutting technological procedure, were established.