Automated Channel Selection in High-Density sEMG for Improved Force Estimation

to the editor: The electromyogram (EMG) as an indirect measure of motor unit activity has proved invaluable in motor control studies. Increasingly, the application of frequency domain techniques to EMG signals has provided important insight into the structure and function of the human motor system.

In this paper, a method for selecting channels to improve estimated force using fast orthogonal search (FOS) has been proposed. Surface electromyogram (sEMG) signals acquired from linear surface electrode arrays, placed on the long head and short head of biceps brachii, and brachioradialis during isometric contractions are used to estimate force induced at the wrist using the FOS algorithm. The method utilizes principle component analysis (PCA) in the frequency domain to select the channels with the highest contribution to the first principal component (PC). Our analysis demonstrates that our proposed method is capable of reducing the dimensionality of the data (the number of channels was reduced from 21 to 9) while improving the accuracy of the estimated force.

Wearable e-health system, are frequently used for monitoring biomedical signals. These devices need to have advanced and applicable methods of feature selection and classifications for real time applications. Electromyogram (EMG) signal records the movement of the human muscle. EMG signal processing techniques aim to achieve the actual signal and among others, detect the state of signals related to positive and negative emotional expression. In our study, the data collected is from the facial muscle activity that is produced by the emotion of the facial expressions. The key challenge is in finding an accurate classification method of the measured signals. This paper investigates the promising techniques for the detection and classification of EMG signal using machine-learning theory. Here, we demonstrated Support Vector Machine (SVM) is an optimal method for classification of facial surface Electromyogram (sEMG) signal associated to pain dataset. The test results and the methods are able to analyze the patterns recognition of facial EMG signal classification. The result and the findings 99% accuracy with SVM method adds value on the classification algorithms of our EMG signal acquisitions platform.

Electromyogram (EMG) signals are biopotential signals, which occur as the result of electro-chemical circumstances dependent on contraction of muscles. In this study, a new method is presented to determine the motor unit action potentials of EMG signals recorded with surface electrodes. Compared to needle EMG signals, surface EMG signals have more active noise component. Results, which are gained by the process train including wavelet de-noising method for noise anlysis at first step and then ICA algorithms to provide independent components, form four different datas to determine the motor unit action potentials. Performed study, which is aimed to make ineffective the problems that make the analysis of EMG signals difficult and then to reach the desired results, provides simultaneous comparison possibility submitting four results. After the determination of the results, with the feedback between input and output, examination of accuracy ratio of results are possible and this examination eliminates the questions about the accuracy of the study.

This paper describes the estimation and analysis of variance distribution of surface electromyogram (EMG) signals based on a stochastic EMG model. With the assumption that EMG signals at a certain time follow Gaussian distribution, their variance is handled as a random variable that follows inverse gamma distribution, and noise superimposed onto this variance can be expressed accordingly. The paper proposes variance distribution estimation based on marginal likelihood maximization of EMG signals. A simulation experiment using artificially generated signals to verify its accuracy indicated that the method can estimate variance distribution with high accuracy for a wide range of variance distribution shaping. Analysis of variance distribution using measured EMG signals revealed the relationship between muscle force and variance distribution involving signal-dependent noise.

Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals

The aim of this study is to analyze Electromyogram (EMG) signals in Rapid Eye Movement (REM) sleep using different techniques to detect the level of normality and abnormality of normal and abnormal (patients with a lack of REM sleep atonia) subjects and predict the development of Parkinson’s disease in abnormal subjects. Quantitative elctromyogram (EMG) signal analysis in the frequency domain using classical power spectrum analysis techniques have been well documented over the past decade. Yet none of these [sic] work have been done on EMG during Rapid Eye Movement (REM) Stage of sleep. In this work three techniques for classifying chin movement via EMG signals during sleep is presented. Three methods (Autoregressive modeling, Cepstrum Analysis and Wavelet Analysis) for extracting features from EMG signal during sleep and a classification algorithm (Linear Discriminant Analysis (LDA)) were analyzed and compared. EMG data are used to detect and describe different disease processes affecting sleep. Rapid Eye Movement Behavior Disorder (RBD) is an example of EMG abnormality in which patients lose their muscle control while in REM stage of sleep resulting in physically acting out their dreams. An adaptive segmentation based on Recursive Least Square (RLS) algorithm was analyzed. This algorithm was used to segment the non-stationary EMG signal into locally stationary components, which were then autoregressive modeled using the Burg-Lattice method. The cepstral measurements described was used and applied to modify the coefficients computed from the autoregressive (AR) model. Yet due to the nature of the EMG, frequency analysis cannot be used to approximate a signal whose properties change over time. To address this problem a time varying feature representation is necessary for analysis to extract useful infomration from the signal. As a consequence Wavelet coefficients were computed using discrete and continuous wavelet transforms. Furthermore, the classification performance of the above three feature sets were investigated for the two classes (Normal and Abnormal). Results showed wavelet analysis compared to AR modeling and cepstrum analysis is a better assessment in finding EMG abnormalities during sleep. However, these methods may be useful in distinguishing EMG patterns that predict the emergence of Parkinson disease in humans.

The surface electromyogram (EMG) signal collected from multiple channels has frequently been investigated for use in controlling upper-limb prostheses. One common control method is EMG-based motion classification. Time and frequency features derived from the EMG have been investigated. We propose the use of EMG signal whitening as a preprocessing step in EMG-based motion classification. Whitening decorrelates the EMG signal, and has been shown to be advantageous in other EMG applications. In a ten-subject study of up to 11 motion classes and ten electrode channels, we found that whitening improved classification accuracy by approximately 5% when small window length durations (<100ms) were considered.

Cross-correlation between surface electromyogram (EMG) signals is commonly used as a means of quantifying EMG cross talk during voluntary activation. To examine the reliability of this method, the relationship between cross talk and the cross-correlation between surface EMG signals was examined by using model simulation. The simulation results illustrate an increase in cross talk with increasing subcutaneous fat thickness. The results also indicate that the cross-correlation function decays more rapidly with increasing distance from the active fibers than cross talk, which was defined as the normalized EMG amplitude during activation of a single muscle. The influence of common drive and short-term motor unit synchronization on the cross-correlation between surface EMG signals was also examined. While common drive did not alter the maximum value of the cross-correlation function, the correlation increased with increasing motor unit synchronization. It is concluded that cross-correlation analysis is not a suitable means of quantifying cross talk or of distinguishing between cross talk and coactivation during voluntary contraction. Furthermore, it is possible that a high correlation between surface EMG signals may reflect an association between motor unit firing times, for example due to motor unit synchronization.

The aim of this work was to reduce ECG artifacts from surface electromyogram (EMG) signals collected from lumbar muscles with the blind source separation technique based on independent component analysis (ICA). Using four EMG signals collected above erector spinal lumbar muscles from 27 subjects, the proposed method fail in separating the sources. However, when considering a single channel of EMG and the same one time-shifted by one sample, the FastICA allowed reducing the signal to ECG noise ratio.

Using electromyogram (EMG) signals as a human-machine interface to control the myoelectric prostheses is consummately advanced. Nevertheless, reported studies present high classification performance from the EMG signal captured in laboratory conditions. There is a very challenging suffix regarding untrained condition. Limb position change causes high variation in EMG signals resulting in high misclassification rate in the untrained position. The goal of this work is to indicate the most relevance EMG channel, which is robust to the effect of a limb position change. The EMG signals from four different object shapes with five different object placement positions were examined using linear discriminant analysis. To determine the best EMG channel subset of all possible combination of selective channels, we reduced the number of EMG channels from 12 to 3. The results have shown that the minimum classification error rate (2.09%) across five subjects can be achieved by use of eight optimal EMG channels. Additionally, our results suggest the EMG channel number 3 (Triceps brachii), 7 (Extensor digitorum communis), and 9 (Brachioradialis) as the most three emphasized channels that contain the rich information of hand grasping and generalize limb position change.

Many people have been exposed to lower extremity function losses due to neurological, pathological or traffic accidents. In the physical therapy and rehabilitation of these patients, treatment programs based on robotic systems have started to be preferred instead of conventional methods. In robotic gait rehabilitation, mobilized lower extremity exoskeletons such as Rewalk or un-mobilized lower extremity exoskeletons such as RoboGait are used. It is important to evaluate the rehabilitation process in patients with lower extremity problems. Measurement of surface electromyogram (EMG) signals during the treatment process give information about the functional activities of the muscles. Obtained information plays an important role in determining the intention of patient motion in musculoskeletal design and musculoskeletal activities of the musculoskeletal. Changes in muscle activation timing and amplitude during the use of lower extremity exoskeleton can be determined by analysis of EMG. In this study, muscles involved in walking movement during robotic rehabilitation were examined. The examined iliopsoas, gluteus maximus, gluteus medius muscles provide flexion, extension and abduction movements of the hip, while the medial gastrocnemius and tibialis anterior muscles perform flexion and dorsiflexion movements of the foot. During the gait, the knee joint patency is controlled by the Vastus Medialis and Biceps Femoris muscles. In this study, while 6 patients with lower limb dysfunction were walking on the RoboGait device, the muscle activation potentials obtained from 7 different muscle groups were transferred to the computer simultaneously and wirelessly and displayed in the Matlab environment. The EMG signals measured with the MicroCor Lab device are shaped according to the activation of the muscles during walking. The electrode placement plan is critical for the analysis of EMG signals, and an appropriate electrode placement plan was obtained as a result of the study. Examined measured signals by following with the electrode placement plan, the maximum gluteus and iliopsoas muscles responsible for the extension and flexion movements of the hips are more effective during walking. Gletous maximum muscle was found to be the most effective muscle in walking while the iliopsoas muscle group was involved in the first movement of the leg. As a result of this study, these findings will help to follow the development of the treatment process and to develop EMG controlled mobilized lower extremity exoskeletons.

This paper presents the estimation and analysis of surface electromyogram (EMG) signals during fatiguing contractions based on a stochastic EMG model. In the model, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances, facilitating the representation of the variance distribution of EMG signals. The paper proposes a continuous estimation method for variance distribution parameters using a sliding window, enabling the evaluation of the time-varying stochastic properties of EMG signals. Estimation experiments were conducted on six healthy young adults to analyze changes in EMG variance distribution with the progression of muscle fatigue. The results reveal the linear and nonlinear relationships between muscle fatigue and variance distribution parameters.

Electromyogram (EMG) signal is often processed offline, after its acquisition, using digital signal processing algorithms to extract muscle anatomical and physiological information. As most of the signal processing algorithms work on an adequate quality of the signals, thus quality checking of the EMG in real-time during its acquisition is of immense importance. In multi-channel sEMG signals, usually there are some noisy or bad channels. If the noise is of low level, it is of little concern but high level of noise can limit the usefulness of the EMG. To make sure acquisition of a good quality EMG signal in terms of SNR, one way to detect noisy channels is through visual inspection by an expert human operator, however visual inspection of multiple electrodes in real-time is not possible and is also expensive both in terms of time and cost. In this research study, we propose a novel method for automatic detection of noisy channels in multi-channel surface EMG signals based on statistical thresholding of several parameters. The results of the proposed method are in perfect agreement with the ground truth for simulated EMG signals, with an accuracy of 98.6%.

A three-electrode human-computer interaction system, based on digital processing of the Electromyogram (EMG) signal, is presented. This system can effectively help disabled individuals paralyzed from the neck down to interact with computers or communicate with people through computers using point-and-click graphic interfaces. The three electrodes are placed on the right frontalis, the left temporalis and the right temporalis muscles in the head, respectively. The signal processing algorithm used translates the EMG signals during five kinds of facial movements (left jaw clenching, right jaw clenching, eyebrows up, eyebrows down, simultaneous left & right jaw clenching) into five corresponding types of cursor movements (left, right, up, down and left-click), to provide basic mouse control. The classification strategy is based on three principles: the EMG energy of one channel is typically larger than the others during one specific muscle contraction; the spectral characteristics of the EMG signals produced by the frontalis and temporalis muscles during different movements are different; the EMG signals from adjacent channels typically have correlated energy profiles. The algorithm is evaluated on 20 pre-recorded EMG signal sets, using Matlab simulations. The results show that this method provides improvements and is more robust than other previous approaches.