Quantitative Analysis of Activity Patterns in the Muscles of Mastication and Deglutition

Surface electromyograms (EMGs) were recorded from the masseter (Mass), one of the major muscles for chewing, and from the suprahyoid (SH) muscles, involved in swallowing. Activity patterns of these EMGs were analyzed with a TP method that was developed specifically to quantify muscle activity patterns. To compare individual EMG bursts in a participant with different amplitudes and active durations, the bursts were cumulatively integrated to standardize the amplitudes and active durations. Each TP value calculated by this method indicated a relative location of an EMG burst on a standardized time scale free from changes in the amplitudes and active durations. Both InP and DP values were derived from the TP values and also applied to the burst. A T50 value indicated the standardized time for half of the final cumulatively integrated EMG burst. Five groups of application samples were introduced to demonstrate the usefulness of the TP method in comparing activity patterns of the Mass and SH EMGs during chewing and swallowing, while participants were in different body positions and experiencing different tastes and textures of sample foods. Finally, limitations and perspectives of the TP method are discussed

Gesture Based Control and EMG Decomposition

This paper presents two probabilistic developments for use with Electromyograms (EMG). First described is a new-electric interface for virtual device control based on gesture recognition. The second development is a Bayesian method for decomposing EMG into individual motor unit action potentials. This more complex technique will then allow for higher resolution in separating muscle groups for gesture recognition. All examples presented rely upon sampling EMG data from a subject's forearm. The gesture based recognition uses pattern recognition software that has been trained to identify gestures from among a given set of gestures. The pattern recognition software consists of hidden Markov models which are used to recognize the gestures as they are being performed in real-time from moving averages of EMG. Two experiments were conducted to examine the feasibility of this interface technology. The first replicated a virtual joystick interface, and the second replicated a keyboard. Moving averages of EMG do not provide easy distinction between fine muscle groups. To better distinguish between different fine motor skill muscle groups we present a Bayesian algorithm to separate surface EMG into representative motor unit action potentials. The algorithm is based upon differential Variable Component Analysis (dVCA) [l], [2] which was originally developed for Electroencephalograms. The algorithm uses a simple forward model representing a mixture of motor unit action potentials as seen across multiple channels. The parameters of this model are iteratively optimized for each component. Results are presented on both synthetic and experimental EMG data. The synthetic case has additive white noise and is compared with known components. The experimental EMG data was obtained using a custom linear electrode array designed for this study

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 180 reports, articles and other documents introduced into the NASA scientific and technical information system in February 1985

Ventral root or dorsal root ganglion microstimulation to evoke hindlimb motor responses

Functional electrical stimulation is an important therapeutic tool for improving the quality of life of patients following spinal cord injury. Investigators have developed neural interfaces of varying invasiveness and implant location to stimulate neurons and evoke motor responses. Here we present an alternative interface with the ventral roots (VR) or dorsal root ganglia (DRG). We designed preliminary electrophysiology experiments to evaluate the performance of these interfaces, wherein we stimulated lumbar VR or DRG through a penetrating single-wire microelectrode while recording fixed endpoint force and bipolar electromyograms of hindlimb muscles. Data from rat experiments provided evidence for selectivity for target muscles, graded force recruitment, and nontrivial force magnitudes of up to 1 N. Electrophysiology experiments in cats produced similar results to those in rats. In addition, we developed a computational model to estimate the size and quantity of fibers recruited as a function of stimulus amplitude. This model confirmed electrophysiology results showing differences in the thresholds to detect activity in response to VR versus DRG stimulation. The model also provided insights into the mechanisms by which DRG stimulation is more likely to recruit smaller fibers than larger fibers. Finally, we discuss further work to develop and evaluate these potential interfaces

USSR Space Life Sciences Digest

Research in exobiology, life sciences technology, space biology, and space medicine and physiology, primarily using data gathered on the Salyut 6 orbital space station, is reported. Methods for predicting, diagnosing, and preventing the effects of weightlessness are discussed. Psychological factors are discussed. The effects of space flight on plants and animals are reported. Bioinstrumentation advances are noted

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

Deep learning for robust decomposition of high-density surface EMG signals

Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the separation matrix is blindly estimated on a signal interval, it is also possible to apply the same matrix to subsequent signal segments. Nonetheless, the trained separation matrices are sub-optimal in noisy conditions and require that incoming data undergo computationally expensive whitening. One unexplored alternative is to instead use the paired HD-sEMG signal and BSS output to train a model to predict MU activations within a supervised learning framework. A gated recurrent unit (GRU) network was trained to decompose both simulated and experimental unwhitened HD-sEMG signal using the output of the gCKC algorithm. The results on the experimental data were validated by comparison with the decomposition of concurrently recorded intramuscular EMG signals. The GRU network outperformed gCKC at low signal-to-noise ratios, proving superior performance in generalising to new data. Using 12 seconds of experimental data per recording, the GRU performed similarly to gCKC, at rates of agreement of 92.5% (84.5%-97.5%) and 94.9% (88.8%-100.0%) respectively for GRU and gCKC against matched intramuscular sources

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 141)

This special bibliography lists 267 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1975

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 233, June 1982

This bibliograhy lists 387 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1982