Electromyogram (EMG) Signal Analysis: Extraction of a Novel EMG Feature and Optimal Root Difference of Squares (RDS) Processing in Additive Noise

Electromyogram signals generated by human muscles can be measured on the surface of the skin and then processed for use in applications such as prostheses control, kinesiology and diagnostic medicine. Most EMG applications extract an estimate of the EMG amplitude, defined as the time-varying standard deviation of EMG, EMGσ. To improve the quality of EMGσ, additional signal processing techniques, such as whitening, noise reduction and additional signal features can be incorporated into the EMGσ processing. Implementation of these additional processing techniques improve the quality of the processed signal but at the cost of increased computational complexity and required calibration contractions. Whitening filters are employed to temporally decorrelate data so that the samples are statistically independent. Different types of whitening filters, linear and adaptive, and their performance have been previously studied in (Clancy and Hogan) and (Clancy and Farry). The linear filter fails at low effort levels and the adaptive filter requires a calibration every time electrodes are removed and reapplied. With the goal of avoiding the disadvantages of the previous whitening filter approaches, the first signal processing technique studied herein developed a universal fixed whitening filter using the ensemble mean of the power spectrum density of EMG recordings from the 64 subjects available in an existing data set. Performance of the EMG to torque model with the universal fixed whitening filter was computed to be 4.8% maximum voluntary contraction (MVC); this is comparable to the 4.84 %MVC error computed for the adaptive whitening filter. The universal fixed whitening filter preserves the performance of the adaptive filter but need not be calibrated for each electrode. To optimize noise reduction, the second signal processing technique studied derived analytical models using the resting EMG data. The probability density function of the rest contractions was observed to be very close to a Gaussian distribution, showing only a 1.6% difference when compared to a Gaussian distribution. Once the models were developed, they were used to prove that the optimal subtraction of the noise variance is to compute the root of the difference between the signal squared and noise variance (RDS). If this result would lead to a negative value, it must be set to zero; EMGσ cannot contain negative components. Once the RDS was proven to be the optimal noise subtraction, it was implemented on 0% MVC and 50% MVC data. The RDS processing has a considerable impact on lower level contractions (0% MVC), but not on higher level contractions (50% MVC), as expected. The third signal processing technique involved the creation of a new EMG feature from four individual signal features. Different techniques were used to combine EMGσ, zero crossings (ZC), slope sign changes (SSC) and waveform length (WL) into a single new EMG feature that would be used in an end application, such as the modeling of torque about the elbow or prosthesis control. The new EMG feature was developed to reduce the variance of the traditional EMGσ only feature and to eliminate the need for calibration contractions. Five different methods of combination were attempted, but none of the new EMG features improved performance in EMG to torque model

Estimation of Impedance About the

In performing manual tasks, muscles are voluntarily contracted in order to produce force and orient the limb in the desired direction. Many occupational tasks are associated with frequent musculoskeletal disorders. In tasks involving skilful manipulation, very frequently the forces are focused on the upper limb and neck. Upper extremity cumulative trauma disorders are among the more common worker related injuries. These muscle disorders may be related to repetitive exertions, excessive muscle loads and extreme postures. One of the major challenges is to quantify the muscle load and researchers have tried various measures to quantify muscle load. Joint mechanical impedance can be a robust method to quantify muscle load. Joint mechanical impedance characterizes the dynamic torque-angle relationship of the joint. Joint impedance has been measured by earlier researchers, for limited tasks, by imparting force (or angle) perturbations on the joint and relating resultant angular (or force) changes. The joint impedance gives a quantitative measure related to muscle co-contraction level. Measurement of the mechanical impedance at the workplace may provide useful information relevant to the understanding of upper limb disorders. Electromyogram (EMG) is the electrical activity of the muscle. Usually, an estimate of the EMG amplitude is obtained from the raw waveform recorded from the surface of the skin. EMG amplitude estimates can be used to non-invasively estimate torque about joints. Presently, there exists no means by which mechanical impedance can be estimated non-invasively (i.e., without external perturbations). Therefore, we proposed the use of EMG to noninvasively estimate the joint mechanical impedance. Our objective in this project was to determine the extent to which surface EMG can be used to estimate mechanical impedance. Simulation studies were first performed to understand the extent to which this tool could be useful and to determine methods to be used for the experiment. The simulations were followed by evaluating and estimating mechanical impedance using data collected from one experimental subject. Simulations helped to devise processing techniques for the measured signals and also to determine the length of data to be collected. Low pass filters for derivatives (used in the development of impedance estimates) were designed. Subtracting out a polynomial was the best approach to attenuate a low frequency drift (artifact) that occurs in torque measurements. Thirty seconds of data provided impedance estimates with a relative error of 5% when EMG amplitude estimates with SNR of 15 were used. Experimental data from constant-posture, slowly force-varying background torque level showed that the elbow joint system behaved like a second order linear system between 2 Hz and 10 Hz. Co-contraction by subjects during experiments caused impedance estimates to be unexpectedly high even at low background torque. Further experiments would need to be conducted with the subjects being instructed to avoid co-contraction

Advanced Electromyogram Signal Processing with an Emphasis on Simplified, Near-Optimal Whitening

Estimates of the time-varying standard deviation of the surface EMG signal (EMGσ) are extensively used in the field of EMG-torque estimation. The use of a whitening filter can substantially improve the accuracy of EMGσ estimation by removing the signal correlation and increasing the statistical bandwidth. However, a subject-specific whitening filter which is calibrated to each subject, is quite complex and inconvenient. To solve this problem, we first calibrated a 60th-order “Universal” FIR whitening filter by using the ensemble mean of the inverse of the square root of the power spectral density (PSD) of the noise-free EMG signal. Pre-existing data from elbow contraction of 64 subjects, providing 512 recording trials were used. The test error on an EMG-torque task based on the “Universal” FIR whitening filter had a mean error of 4.80% maximum voluntary contraction (MVC) with a standard deviation of 2.03% MVC. Meanwhile the subject-specific whitening filter had performance of 4.84±1.98% MVC (both have a whitening band limit at 600 Hz). These two methods had no statistical difference. Furthermore, a 2nd-order IIR whitening filter was designed based on the magnitude response of the “Universal” FIR whitening filter, via the differential evolution algorithm. The performance of this IIR whitening filter was very similar to the FIR filter, with a performance of 4.81±2.12% MVC. A statistical test showed that these two methods had no significant difference either. Additionally, a complete theory of EMG in additive measured noise contraction modeling is described. Results show that subtracting the variance of whitened noise by computing the root difference of the square (RDS) is the correct way to remove noise from the EMG signal

Optimal Electromyogram Modeling and Processing During Active Contractions and Rest

The standard deviation of surface EMG (EMGσ) is often related to muscle force; the accuracy of EMGσ estimation is valuable for many application areas such as clinical biomechanics, prostheses control and sports medicine. Numerous researchers have developed methods to optimize EMGσ estimation. Whitening the EMG signal has been proved to improve the statistical efficiency of EMGσ estimation, but conventional linear whitening filters fail at low contraction level. An adaptive whitening filter was proposed by Clancy and Farry[14]. This technique has a better performance than prior whitening methods, however, the adaptive whitening filter needs to be calibrated each time electrodes are applied, which increase the complexity of the implementation. We designed a universal whitening filter which can omit most calibration steps for the adaptive whitening filter in future use. We used the ensemble mean of the power spectrum of 512 EMG recordings to form a general shape of a fixed whitening filter that can applied on any EMG signal. The test error on an EMG-torque task based on universal whitening over 512 subjects had a mean error of 4.80% maximum voluntary contraction (MVC) and standard deviation (std) of 2.03% MVC, compared with an original adaptive whitening filter error of 4.84±1.98% MVC. Additionally, the rest contraction modeling hasn’t received enough attention. Existing RMS estimates of EMGσ subtract noise in either the amplitude or power domain. These procedures have never been modeled analytically. We show that power domain noise subtraction is optimal. But rest contractions which are processed using power domain noise subtraction only estimate a zero-valued EMGσ approximately 50% of the time, which is undesirable in prosthesis-control. The prosthesis has a 50% possibility to slowly drift based on the current RMS estimator. We propose a new estimator to improve the zero-amplitude estimation probability during rest. We used 512 rest contraction recordings to validate the probability distribution of rest EMG signal showing that it only has 1.6% difference compared with Gaussian distribution. We also evaluated the percent of zero-valued EMGσ estimates using power domain noise subtraction and our new estimator, matching experimental findings to the theoretic predictions

Simulasi Perbandingan Filter Savitzky Golay dan Filter Low Pass Butterworth pada Orde Ketiga Sebagai Pembatal Kebisingan

Nowadays, the Information and Communication Technology (ICT) is rapidly developed. It also trigs the development of other research field such as social science research. But in the development of it, there are a continues problem that has been discovered over 30 years, noise. Over the years, many ways have been created for example Savitzky – Golay (SG) and Low Pass (LP) Butterworth filter. In order to use SG filter, two parameters which are the order and the window length should be determined by trial and error. On the other hand, LP Butterworth filter also needs two parameters to be operated which are the order and the cut off frequency. This research focuses on comparing the performance of third order LP Butterworth filter and third order SG filter by finding the gap between filtered signal and the original signal through the simulation by using MATLAB. This research is supported by the journals and books references. Also, the data of this research is presented by the table and graph. According to this research, founded that both filters have a significant impact to smoothing the noisy signal. compare to LP Butterworth filter, SG filter has better performance. It is proven by SG filter only has 5% gap to the original signal where LP Butterworth filter has a slightly bigger gap, 8.82%

Analysis of sEMG on biceps brachii and brachioradialis in static conditions: Effect of joint angle and contraction level

Despite several previous investigations, the direct correlation between the elbow joint angle and the activities of related muscles is still an unresolved topic. The sEMG signals were recorded from biceps brachii (6x8 electrodes, 10mm IED, d=3mm) and brachioradialis (1x8 electrodes, 5mm IED, d=3mm) of ten subjects. The subjects were asked to perform isometric elbow flexion at five joint angles with four contraction levels with respect to the maximum contraction (MVC) at that joint angle. The RMS values of biceps brachii (BB) and brachioradialis (BR) are computed within 500ms epoch and averaged over the muscle’s active region. These values increase along as force increases regardless the joint angle. Concerning the different joint angle, we found that as the arm extended, the RMS values of seven subjects decreased, while the RMS values of three subjects increased. This behavior suggests different strategies of muscle contribution to the task in different subjects but may also be attributed to the technical issues discussed in Chapter 2 - 7. Prior to this investigation, several issues related to the sEMG signals recording and processing were evaluated. Analysis on the effect of different elbow joint angle on the position of the innervations zone (IZ) of biceps brachii muscle indicates that the IZ shifts distally 24±9mm as the subjects extend their arms. Thus to assure sEMG signal recording, a grid of electrodes is selected instead of bipolar electrodes. The issue of spatial aliasing, which has not been addressed before, was studied. Greater electrode’s diameter implies higher spatial low pass filtering effect which gives an advantage as anti-aliasing filter in space. On the other hand, this low pass filtering effect increase the error on the power for the single sEMG image (d=10mm, 10mm IED) to 3±13.5% compared to the continuous image. Larger IED introduces RMS estimation error up to ±18% for the single sEMG image (15mm IED). However, taking the mean of a group of maps, the error of the mean is negligible (<3%). Furthermore, the envelope of the rectified EMG has been investigated. Five digital low pass filters (Butterworth, Chebyshev, Inverse Chebyshev, and Elliptic) with five different orders, four cut off frequencies and one or bi-directional filtering were tested using simulated sEMG interference signals. The results show that different filters are optimal for different applications. Power line interference is one of the sources of impurity of the sEMG signals. Notch filter, spectral interpolation, adaptive filter, and adaptive noise canceller with phase locked loop were compared. Another factor that affects the amplitude of sEMG is the subcutaneous layer thickness (ST). Higher contraction level and greater elbow joint angle lead to thinner ST. RMS values tend to decrease for thicker ST at a rate of 1.62 decade/decade

A real time frequency analysis of the electroencephalogram using Labview

The use of the Electroencephalogram (EEG) for diagnosis of brain related diseases is becoming a popular technique in the clinical and research environment. To achieve accurate reading of EEG, signal representation and classification becomes extremely important. The goal of this project was to develop a basic software program for acquiring and online processing of the electrical activity recorded from the brain. A program was developed using the LabVIEW programming software by National Instruments. Basic hardware components recorded the EEG signal and a software component divided the data into delta, theta, alpha and beta bands in the frequency domain. Emphasis was placed on critical programming parameters such as sampling rate, filtering, windowing and FFT. The developed software was implemented in an already existing experimental paradigm that studies classical conditioning response. To prove the validity and accuracy of the system, a pilot experiment was conducted where EEG was recorded from six subjects. Data showed that as the subject learns, continuous theta activity is observed. Performance and testing of the EEG system demonstrated that the on line processing of EEG could be used in a variety of other applications where neural activity is involved such as classifying sleep stages in patients, discriminating various mental tasks, recording continuous EEG activity in neonatals with brain dysfunction etc

A Study of Myoelectric Signal Processing

This dissertation of various aspects of electromyogram (EMG: muscle electrical activity) signal processing is comprised of two projects in which I was the lead investigator and two team projects in which I participated. The first investigator-led project was a study of reconstructing continuous EMG discharge rates from neural impulses. Related methods for calculating neural firing rates in other contexts were adapted and applied to the intramuscular motor unit action potential train firing rate. Statistical results based on simulation and clinical data suggest that performances of spline-based methods are superior to conventional filter-based methods in the absence of decomposition error, but they unacceptably degrade in the presence of even the smallest decomposition errors present in real EMG data, which is typically around 3-5%. Optimal parameters for each method are found, and with normal decomposition error rates, ranks of these methods with their optimal parameters are given. Overall, Hanning filtering and Berger methods exhibit consistent and significant advantages over other methods. In the second investigator-led project, the technique of signal whitening was applied prior to motion classification of upper limb surface EMG signals previously collected from the forearm muscles of intact and amputee subjects. The motions classified consisted of 11 hand and wrist actions pertaining to prosthesis control. Theoretical models and experimental data showed that whitening increased EMG signal bandwidth by 65-75% and the coefficients of variation of temporal features computed from the EMG were reduced. As a result, a consistent classification accuracy improvement of 3-5% was observed for all subjects at small analysis durations (\u3c 100 ms). In the first team-based project, advanced modeling methods of the constant posture EMG-torque relationship about the elbow were studied: whitened and multi-channel EMG signals, training set duration, regularized model parameter estimation and nonlinear models. Combined, these methods reduced error to less than a quarter of standard techniques. In the second team-based project, a study related biceps-triceps surface EMG to elbow torque at seven joint angles during constant-posture contractions. Models accounting for co-contraction estimated that individual flexion muscle torques were much higher than models that did not account for co-contraction