Analysis of surface myoelectric signals by linear prediction method

The article presents a proposal to use linear prediction method for a quick analysis of surface myoelectric (EMG) signals. The spectra obtained with the linear prediction (LP) and Fourier methods were compared. The LP method allows for a precise determination of the location and amplitude of the spectrum maximum and observation of changes in muscle tension and contraction phases. EMG spectra of brachial biceps during flexion and extension of the forearm by four adults were analyzed. The optimal width of the time window for the averaging of motor unit action potentials that allows for the observation of changes during contraction was established. It has been found that maximum spectrum during flexion has a significantly higher frequency and amplitude than during the extension of the forearm

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Biomedical Signal and Image Processing

Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of which are MATLAB based

Whole body vibration: stimulus characteristics and acute neuromuscular responses

Whole body vibration (WBV) delivers a stimulus to the body via an oscillating platform and remains a relatively new area of research. Several applications of WBV stimuli have been developed as strength training and rehabilitation modalities, but inconsistent results have been published. There is little knowledge underpinning the mechanisms to explain the elicited neuromuscular responses to WBV and a wide range of WBV parameters across the literature. As a result, safe and effective protocols are yet to be established or validated. The aim of this current research was to investigate: the electromyography (EMG) and explosive performance responses to varying WBV frequencies; the effect of WBV data analysis techniques; and the influence of external factors on WBV stimulus and neuromuscular responses. Three main studies were completed: 1. An individualised response of both EMG and jump performance appears to exist dependent on vertical WBV frequency, in trained participants. This is in spite of no overall frequency dependent effect of EMG or performance responses across participants as a group. The influence of the role of expectancy effect appears minimal following this particular WBV protocol. 2. There was a significant effect of filter technique on EMG data recorded during vertical WBV. A tailored, WBV specific notch filter technique may offer an effective balance; excluding WBV noise artifacts without removing significant portions of valuable muscle signal EMG data. 3. The influence of external load on WBV acceleration output also appears minimal. Platform acceleration output was dependent on WBV frequency, as expected. Lower accelerations were recorded in superior body segments, suggesting a dampening mechanism, which was also proportionally dependent on frequency. EMG activity of upper and lower leg segments may differ in response to frequency, likely due to transmission distances involved. This may partially account for a potential dampening mechanism. In addition, a protocol to quantify WBV stimuli delivered by this particular WBV type illustrated significant differences in theoretical and actual parameters. This may explain not only the lack of overall explosive performance effect reported earlier; but also the inconsistent WBV literature. Future research should quantify WBV stimulus before investigating possible neuromuscular responses to individualised protocols, which may be assessed via EMG activity

Sex differences in the neural control of muscle

Sex-differences in muscle strength have been linked to differences in muscle size, involved limb, and daily activities. Early work has shown that sex-differences are greater in the upper compared to lower limb, making the upper limb an ideal model to investigate the best statistical approaches for sex comparison. Large differences in the upper limb reveals how biomechanical factors may impact neural control. Since males and females are more comparable with respect to strength in the lower limb, it allows for a determination of whether potential sex-differences in neural control exist without large differences in biomechanics. Understanding sex-differences allows for prescription of rehabilitation and training modalities, taking into account potential specificities in sex-related neuromuscular and musculoskeletal factors. The overall purpose was to examine neural and biomechanical differences that would account for sex-differences in neural control of muscle. Manuscript 1 examined normalization versus an ANCOVA to assess sex-differences. Sex-differences were seen in elbow flexor strength and rate of force development (RFD). Normalization by either maximum strength or neural factors couldn’t account for all sex-differences in RFD, resulting in an ambiguous interpretation. In contrast, both variables were able to be incorporated in an ANCOVA to determine their relative contribution. Manuscript 2 examined the effect of task familiarization and the contribution of maximum strength, twitch contraction time, muscle fiber condition velocity, and rate of muscle activation to sex-differences in the RFD during dorsiflexion. There were no significant differences between the sexes in muscle properties, but there were differences in neural control. Additionally, across days females exhibited a neural adaptation leading to an improvement in the RFD. Manuscript 3 directly assessed potential sex-differences in neural control during force gradation by recording motor unit activity during maximal and submaximal contractions. Females had less force steadiness (FS), which may have resulted from neural compensation for a less optimal pennation angle or a tendency towards greater joint laxity. Higher motor unit discharge rates and incidence of doublets may increase twitch force summation leading to a reduction in FS. Thus, biomechanical, not inherent sex-differences in neural drive led to neural compensation strategies manifesting as a difference in FS

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 observed chaotic data

Thesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2004Includes bibliographical references (leaves: 86)Text in English; Abstract: Turkish and Englishxii, 89 leavesIn this thesis, analysis of observed chaotic data has been investigated. The purpose of analyzing time series is to make a classification between the signals observed from dynamical systems. The classifiers are the invariants related to the dynamics. The correlation dimension has been used as classifier which has been obtained after phase space reconstruction. Therefore, necessary methods to find the phase space parameters which are time delay and the embedding dimension have been offered. Since observed time series practically are contaminated by noise, the invariants of dynamical system can not be reached without noise reduction. The noise reduction has been performed by the new proposed singular value decomposition based rank estimation method.Another classification has been realized by analyzing time-frequency characteristics of the signals. The time-frequency distribution has been investigated by wavelet transform since it supplies flexible time-frequency window. Classification in wavelet domain has been performed by wavelet entropy which is expressed by the sum of relative wavelet energies specified in certain frequency bands. Another wavelet based classification has been done by using the wavelet ridges where the energy is relatively maximum in time-frequency domain. These new proposed analysis methods have been applied to electrical signals taken from healthy human brains and the results have been compared with other studies

Modeling motor-evoked potentials from neural field simulations of transcranial magnetic stimulation

Objective To develop a population-based biophysical model of motor-evoked potentials (MEPs) following transcranial magnetic stimulation (TMS). Methods We combined an existing MEP model with population-based cortical modeling. Layer 2/3 excitatory and inhibitory neural populations, modeled with neural-field theory, are stimulated with TMS and feed layer 5 corticospinal neurons, which also couple directly but weakly to the TMS pulse. The layer 5 output controls mean motoneuron responses, which generate a series of single motor-unit action potentials that are summed to estimate a MEP. Results A MEP waveform was generated comparable to those observed experimentally. The model captured TMS phenomena including a sigmoidal input–output curve, common paired pulse effects (short interval intracortical inhibition, intracortical facilitation, long interval intracortical inhibition) including responses to pharmacological interventions, and a cortical silent period. Changes in MEP amplitude following theta burst paradigms were observed including variability in outcome direction. Conclusions The model reproduces effects seen in common TMS paradigms. Significance The model allows population-based modeling of changes in cortical dynamics due to TMS protocols to be assessed in terms of changes in MEPs, thus allowing a clear comparison between population-based modeling predictions and typical experimental outcome measures