EMG Modeling

The aim of this chapter is to describe the approaches used for modelling electromyographic (EMG) signals as well as the principles of electrical conduction within the muscle. Sections are organized into a progressive, step-by-step EMG modeling of structures of increasing complexity. First, the basis of the electrical conduction that allows for the propagation of the EMG signals within the muscle is presented. Second, the models used for describing the electrical activity generated by a single fibre described. The third section is devoted to modeling the organization of the motor unit and the generation of motor unit potentials. Based on models of the architectural organization of motor units and their activation and firing mechanisms, the last section focuses on modeling the electrical activity of a complete muscle as recorded at the surface

A dendritic mechanism for decoding traveling waves: Principles and applications to motor cortex

Traveling waves of neuronal oscillations have been observed in many cortical regions, including the motor and sensory cortex. Such waves are often modulated in a task-dependent fashion although their precise functional role remains a matter of debate. Here we conjecture that the cortex can utilize the direction and wavelength of traveling waves to encode information. We present a novel neural mechanism by which such information may be decoded by the spatial arrangement of receptors within the dendritic receptor field. In particular, we show how the density distributions of excitatory and inhibitory receptors can combine to act as a spatial filter of wave patterns. The proposed dendritic mechanism ensures that the neuron selectively responds to specific wave patterns, thus constituting a neural basis of pattern decoding. We validate this proposal in the descending motor system, where we model the large receptor fields of the pyramidal tract neurons — the principle outputs of the motor cortex — decoding motor commands encoded in the direction of traveling wave patterns in motor cortex. We use an existing model of field oscillations in motor cortex to investigate how the topology of the pyramidal cell receptor field acts to tune the cells responses to specific oscillatory wave patterns, even when those patterns are highly degraded. The model replicates key findings of the descending motor system during simple motor tasks, including variable interspike intervals and weak corticospinal coherence. By additionally showing how the nature of the wave patterns can be controlled by modulating the topology of local intra-cortical connections, we hence propose a novel integrated neuronal model of encoding and decoding motor commands

Numerical modelling in transcranial magnetic stimulation

Tese de doutoramento, Engenharia Biomédica e Biofísica, Universidade de Lisboa, Faculdade de Ciências, 2009In this work powerful numerical methods were used to study several problems that still remain unsolved in TMS.The first problem that was studied is related to the difficulties that arise when stimulating sub-cortical deep regions with TMS, due to the fact that the induced field rapidly decays and loses focality with depth. This study's approach to overcome this difficulty was to combine ferromagnetic cores with a coil designed to induce an electric field that decays slowly. The efficacy of this approach was tested by using the FEM to calculate the field induced by this coil / core design in a realistically shaped head model. The results show that the core might make this coil even more suited for deep brain stimulation.The second problem that was tackled is related to the lack of knowledge about the dominant mechanisms through which the induced electric field excites neurons in TMS. In this work the electric field along lines, representing trajectories of actual cortical neurons, was calculated using the FEM. The neurons were embedded in a realistically shaped sulcus model, with a figure-8 coil placed above the model. The electric field was then incorporated into the cable equation. The solution of the latter allowed the determination of the site and threshold of activation of the neurons. The results highlight the importance of axonal terminations and bends and tissue heterogeneities on stimulation of neurons.The third problem that was studied concerns TMS of small animals and the lack of knowledge about the optimal geometry, size and orientation of the used coils. This was studied by using the FEM to calculate the electric field induced in a realistically shaped mouse model by several commercially available coils. The results showed that the smaller coils induced fields with higher magnitude, better focality, and smaller decay than the bigger coils.These results highlight the importance of numerical modelling in TMS, either in coil design, determination of basic neurophysiologic mechanisms or optimization of experimental procedures

Response of single spinal motoneurones to transcranial magnetic simulation in healthy subjects and patients with upper motor neurone disorders

The problem addressed by this study was: How does the human corticospinal tract influence the discharge of spinal motoneurones and what are the effects of neurological disease? The method employed was to study the firing probability of 78 tonically active single motor units of the upper limb following transcranial magnetic stimulation. This was performed in healthy subjects and in a group of patients with different upper motor neurone (UMN) disorders. The inducing current flowed in an anticlockwise direction through a circular coil which was positioned tangentially at the vertex. Two peaks were produced in the peri-stimulus time histogram. The primary peak (PP) had an onset latency in healthy subjects ranging from 13 ms (deltoid and biceps) to 31 ms (first dorsal interosseous muscle) (FDI) and had a short duration of 4.6 ±1.7 ms (mean ± SD). PP frequently consisted of 1-3 sub-peaks, with a mean intermodal interval of 1.4 ms for FDI and 2.9 ms for forearm and upper arm muscles. This interval probably reflects the maximal rise time of one in a sequence of excitatory postsynaptic potentials (EPSPs) at the motoneurone. An increase either in the interval between the stimulus and the preceding voluntary discharge, or in the intensity of stimulation, raised the probability of discharges occurring within PP and influenced their latency. The secondary peak (SP) had an onset latency in FDI ranging from 56-90 ms and a long duration of 20.9 ±12.0 ms. Evidence suggests that SP was caused by the rising phase of a late EPSP mediated via a pathway which included a peripheral afferent component. When compared with healthy subjects, PP in UMN patients was found to be either normal, absent, delayed and dispersed (by up to 28 ms and 21 ms, respectively) or found to consist of sub-peaks with abnormally long inter- modal intervals. These findings suggest specific mechanisms including cortical inexcitability, variable degrees of slowing in the velocity of propagation in descending fibres, frequency dependent conduction block, delay between EPSPs caused by the operation of more than one pathway and ineffective spatial or temporal summation at the spinal motoneurone

A Subject-Specific Multiscale Model of Transcranial Magnetic Stimulation

Transcranial magnetic stimulation (TMS) is a neuromodulation technique used to treat a variety of neurological disorders. While many types of neuromodulation therapy are invasive, TMS is an attractive alternative because it is noninvasive and has a very strong safety record. However, clinical use of TMS has preceded a thorough scientific understanding: its mechanisms of action remain elusive, and the spatial extent of modulation is not well understood. We created a subject-specific, multiscale computational model to gain insights into the physiological response during motor cortex TMS. Specifically, we developed an approach that integrates three main components: 1) a high-resolution anatomical MR image of the whole head with diffusion weighted MRI data; 2) a subject-specific, electromagnetic, non-homogeneous, anisotropic, finite element model of the whole head with a novel time-dependent solver; 3) a population of multicompartmental pyramidal cell neuron models. We validated the model predictions by comparing them to motor evoked potentials (MEPs) immediately following single-pulse TMS of the human motor cortex. This modeling approach contains several novel components, which in turn allowed us to gain greater insights into the interactions of TMS with the brain. Using this approach we found that electric field magnitudes within gray matter and white matter vary substantially with coil orientation. Our results suggest that 1) without a time-dependent, subject-specific, non-homogeneous, anisotropic model, loci of stimulation cannot be accurately predicted; 2) loci of stimulation depend upon biophysical properties and morphologies of pyramidal cells in both gray and white matter relative to the induced electric field. These results indicate that the extent of neuromodulation is more widespread than originally thought. Through medical imaging and computational modeling, we provide insights into the effects of TMS at a multiscale level, which would be unachievable by either method alone. Finally, our approach is amenable to clinical implementation. As a result, it could provide the means by which TMS parameters can be prescribed for treatment and a foundation for improving coil design

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Analysis of Simulated Electromyography (EMG) Signals Using Integrated Computer Muscle Model

Introduction Electromyography (EMG) is a technique used to study the activity of muscle through detection and analysis of the electrical signals generated during muscular contractions. Electromyographic activity is recorded from skeletal muscles to obtain information about their anatomy and physiology. Electromyography, in interplay with various anatomical techniques, provides the present knowledge of the structural organization and the nervous control of muscle. EMG is the prime source of information about the status of the neuromuscular system, and EMG has developed into a diagnostic tool that allows the clinician to follow changes in nerve and muscle caused by neuromuscular diseases. EMG provides both invasive and noninvasive means for the study of muscular functions [1, 2]. It is also useful in interpreting pathologic states of musculoskeletal or neuromuscular systems [3, 4]. In particular, EMG offers valuable information concerning the timing of muscular activity and its relative intensity [5, 6]. Standard EMG is typically recorded from fine wire or two surface electrodes placed at discrete sites over a muscle or muscle belly. Currently surface grid electrode EMG is widely used. The cell bodies of these neurons reside in the brainstem and spinal cord. The interfacing fiber between motor neuron and muscle is called axon. At the distal end, an axon divides 1 into many terminal branches. Each terminal branch innervates a group of muscle fibers. When a nerve signal approaches the end of an axon, it spreads out over all its terminal branches and stimulates all the muscle fibers supplied by them. So, all the excited muscle fibers contract almost simultaneously. Since they behave as a single functional unit, one nerve fiber and all the muscle fibers innervated by it are called a motor unit (MU) [7, 8]. Generally, the muscle fibers of a motor unit are distributed throughout muscle rather than being clustered together. The fine control of the muscle force is performed through the intricate mechanism and interaction of the brain and muscle. During contraction, these motor units are recruited systematically and the recruited motor units discharge in a train of pulses in a complex manner [9, 10]. The recorded EMG is the temporal summation of all the recruited motor unit action potential trains. Because movement is controlled by motor unit activity, an understanding of motor unit physiology can have a significant impact on the evaluation and treatment of movement disorders. The neuromuscular system is an intricate physiological organization of brain, nerve and muscle. These neural control properties are not well understood mostly because of the experimental difficulties in quantifying the neural input to the muscle. Moreover, the muscle itself is a complex system. It is necessary to address these complexities as accurately as possible. Understanding of these complex systems facilitates the understanding of EMG generation, which is a highly complex signal by itself

A finite element model for the investigation of surface EMG signals during dynamic contraction

A finite element (FE) model for the generation of single fiber action potentials (SFAPs) in a muscle undergoing various degrees of fiber shortening has been developed. The muscle is assumed to be fusiform with muscle fibers following a curvilinear path described by a Gaussian function. Different degrees of fiber shortening are simulated by changing the parameters of the fiber path and maintaining the volume of the muscle constant. The conductivity tensor is adapted to the muscle fiber orientation. At each point of the volume conductor, the conductivity of the muscle tissue in the direction of the fiber is larger than that in the transversal direction. Thus, the conductivity tensor changes point-by-point with fiber shortening, adapting to the fiber paths. An analytical derivation of the conductivity tensor is provided. The volume conductor is then studied with an FE approach using the analytically derived conductivity tensor (Mesin, Joubert, Hanekom, Merletti&Farina 2006). Representative simulations of SFAPs with the muscle at different degrees of shortening are presented. It is shown that the geometrical changes in the muscle, which imply changes in the conductivity tensor, determine important variations in action potential shape, thus affecting its amplitude and frequency content. The model is expanded to include the simulation of motor unit action potentials (MUAPs). Expanding the model was done by assigning each single fiber (SF) in the motor unit (MU) a random starting position chosen from a normal distribution. For the model 300 SFs are included in an MU, with an innervation zone spread of 12 mm. Only spatial distribution was implemented. Conduction velocity (CV) was the same for all fibers of the MU. Representative simulations for the MUAPs with the muscle at different degrees of shortening are presented. The influence of interelectrode distance and angular displacement are also investigated as well as the influence of the inclusion of the conductivity tensor. It has been found that the interpretation of surface electromyography during movement or joint angle change is complicated owing to geometrical artefacts i.e. the shift of the electrodes relative to the muscle fibers and also because of the changes in the conductive properties of the tissue separating the electrode from the muscle fibers. Detection systems and electrode placement should be chosen with care. The model provides a new tool for interpreting surface electromyography (sEMG) signal features with changes in muscle geometry, as happens during dynamic contractions.Dissertation (MEng (Bio-Engineering))--University of Pretoria, 2008.Electrical, Electronic and Computer EngineeringMEng (Bio-Engineering)unrestricte