Beamforming for powerline interference in large sensor arrays

This paper shows how to use beamforming to remove the power-line interference (PLI) in large surface electromyography (sEMG) sensor array or high-density sEMG. The method exploits the highly correlated nature of the different sources of interference, being part of the same electrical grid, and their narrow frequency bands. The idea is to use a very narrow pass-band filter around 50 or 60 Hz to get signals with high PLI content before applying a spatial filtering by principal component analysis (PCA). This way, beamforming are done on the frequency bands where PLI are presents. Also, it ensures that even if the PLI has a smaller overall power than the desired signal, it will be easily found as the most powerful component of the decomposition. The PLI can then be removed from the signal. With trivial modification, harmonics of the PLI can also be removed. The approach was used in the context of muscle behavior analyses of low back pain patients using a sEMG array of 64 sensors. The performances of the filter are studied by experimental and semi-empirical methods. Compared to the usual notch filter, an improvement of up 10 dB is found

Motor adaptations to trunk perturbation: effects of experimental back pain and spinal tissue creep

In complex anatomical systems, such as the trunk, motor control theories suggest that many motor solutions can be implemented to achieve a similar goal. Although reflex mechanisms act as a stabilizer of the spine, how the central nervous system uses trunk redundancy to adapt neuromuscular responses under the influence of external perturbations, such as experimental pain or spinal tissue creep, is still unclear. The aim of this study was to identify and characterize trunk neuromuscular adaptations in response to unexpected trunk perturbations under the influence of spinal tissue creep and experimental back pain. Healthy participants experienced a repetition of sudden external trunk perturbations in two protocols: 1) 15 perturbations before and after a spinal tissue creep protocol and 2) 15 perturbations with and without experimental back pain. Trunk neuromuscular adaptations were measured by using high-density electromyography to record erector spinae muscle activity recruitment patterns and a motion analysis system. Muscle activity reflex attenuation was found across unexpected trunk perturbation trials under the influence of creep and pain. A similar area of muscle activity distribution was observed with or without back pain as well as before and after creep. No change of trunk kinematics was observed. We conclude that although under normal circumstances muscle activity adaptation occurs throughout the same perturbations, a reset of the adaptation process is present when experiencing a new perturbation such as experimental pain or creep. However, participants are still able to attenuate reflex responses under these conditions by using variable recruitment patterns of back muscles. NEW & NOTEWORTHY The present study characterizes, for the first time, trunk motor adaptations with high-density surface electromyography when the spinal system is challenged by a series of unexpected perturbations. We propose that the central nervous system is able to adapt neuromuscular responses by using a variable recruitment pattern of back muscles to maximize the motor performance, even under the influence of pain or when the passive structures of the spine are altere

HOG and pairwise SVMs for neuromuscular activity recognition using instantaneous HD-sEMG images

The concept of neuromuscular activity recognition using instantaneous high-density surface electromyography (HD-sEMG) image opens up new avenues for the development of more fluid and natural muscle-computer interfaces. The state-of-the-art methods for instantaneous HD-sEMG image recognition achieve prominent performance using a computationally intensive deep convolutional networks (ConvNet) classifier, while very low performance is reported using the conventional classifiers. However, the conventional classifiers such as Support Vector Machines (SVM) can surpass ConvNet at producing optimal classification if well-behaved feature vectors are provided. This paper studies the question of extracting distinctive feature sets, thus propose to use Histograms of Oriented Gradient (HOG) as unique features for robust neuromuscular activity recognition, adopting pair wise SVMs as the classification scheme. The experimental results proved that the HOG represents unique features inside the instantaneous HD-sEMG image and fine-tuning the hyper- parameter of the pair wise SVMs, the recognition accuracy comparable to the more complex state of the art methods can be achieved

S-Convnet: A shallow convolutional neural network architecture for neuromuscular activity recognition using instantaneous high-density surface EMG images

The recent progress in recognizing low-resolution instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep convolutional neural network (ConvNet) architecture and complex training schemes for HD-sEMG image recognition, which requires learning of ˃5.63 million(M) training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training dataset, as a result, it makes high-end resource-bounded and computationally expensive. To overcome this problem, we propose S-ConvNet models, a simple yet efficient framework for learning instantaneous HD-sEMG images from scratch using random-initialization. Without using any pre-trained models, our proposed S-ConvNet demonstrate very competitive recognition accuracy to the more complex state of the art, while reducing learning parameters to only ≈ 2M and using ≈ 12 × smaller dataset. The experimental results proved that the proposed S-ConvNet is highly effective for learning discriminative features for instantaneous HD-sEMG image recognition, especially in the data and high-end resource-constrained scenarios

Pattern recognition based on HD-sEMG spatial features extraction for an efficient proportional control of a robotic arm.

To enable an efficient alternative control of an assistive robotic arm using electromyographic (EMG) signals, the control method must simultaneously provide both the direction and the velocity. However, the contraction variations of the forearm muscles, used to proportionally control the device’s velocity using a regression method, can disturb the accuracy of the classification used to estimate its direction at the same time. In this paper, the original set of spatial features takes advantage of the 2D structure of an 8 × 8 high-density surface EMG (HD-sEMG) sensor to perform a high accuracy classification while improving the robustness to the contraction variations. Based on the HD-sEMG sensor, different muscular activity images are extracted by applying different spatial filters. In order to characterize their distribution specific to each movement, instead of the EMG signals’ amplitudes, these muscular images are divided in sub-images upon which the proposed spatial features, such as the centers of the gravity coordinates and the percentages of influence, are computed. These features permits to achieve average accuracies of 97% and 96.7% to detect respectively 16 forearm movements performed by a healthy subject with prior experience with the control approach and 10 movements by ten inexperienced healthy subjects. Compared with the time-domain features, the proposed method exhibits significant higher accuracies in presence of muscular contraction variations, requires less training data and is more robust against the time of use. Furthermore, two fine real-time tasks illustrate the potential of the proposed approach to efficiently control a robotic arm

Force distribution within spinal tissues during posterior to anterior spinal manipulative therapy: a secondary analysis

Background: Previous studies observed that the intervertebral disc experiences the greatest forces during spinal manipulative therapy (SMT) and that the distribution of forces among spinal tissues changes as a function of the SMT parameters. However, contextualized SMT forces, relative to the ones applied to and experienced by the whole functional spinal unit, is needed to understand SMT's underlying mechanisms. Aim: To describe the percentage force distribution between spinal tissues relative to the applied SMT forces and total force experienced by the functional unit. Methods: This secondary analysis combined data from 35 fresh porcine cadavers exposed to a simulated 300N SMT to the skin overlying the L3/L4 facet joint via servo-controlled linear motor actuator. Vertebral kinematics were tracked optically using indwelling bone pins. The functional spinal unit was then removed and mounted on a parallel robotic platform equipped with a 6-axis load cell. The kinematics of the spine during SMT were replayed by the robotic platform. By using serial dissection, peak and mean forces induced by the simulated SMT experienced by spinal structures in all three axes of motion were recorded. Forces experienced by spinal structures were analyzed descriptively and the resultant force magnitude was calculated. Results: During SMT, the functional spinal unit experienced a median peak resultant force of 36.4N (IQR: 14.1N) and a mean resultant force of 25.4N (IQR: 11.9N). Peak resultant force experienced by the spinal segment corresponded to 12.1% of the total applied SMT force (300N). When the resultant force experienced by the functional spinal unit was considered to be 100%, the supra and interspinous ligaments experienced 0.3% of the peak forces and 0.5% of the mean forces. Facet joints and ligamentum flavum experienced 0.7% of the peak forces and 3% of the mean forces. Intervertebral disc and longitudinal ligaments experienced 99% of the peak and 96.5% of the mean forces. Conclusion: In this animal model, a small percentage of the forces applied during a posterior-to-anterior SMT reached spinal structures in the lumbar spine. Most SMT forces (over 96%) are experienced by the intervertebral disc. This study provides a novel perspective on SMT force distribution within spinal tissues

Variation of corticospinal excitability during kinesthetic illusion induced by musculotendinous vibration

Abtract Despite being studied for more than 50 years, the neurophysiological mechanisms underlying vibration (VIB)-induced kinesthetic illusions are still unclear. The aim of this study was to investigate how corticospinal excitability tested by transcranial magnetic stimulation (TMS) is modulated during VIB-induced illusions. Twenty healthy adults received vibration over wrist flexor muscles (80 Hz, 1 mm, 10 s). TMS was applied over the primary motor cortex representation of wrist extensors at 120% of resting motor threshold in four random conditions (10 trials/condition): baseline (without VIB), 1 s, 5 s, and 10 s after VIB onset. Means of motor-evoked potential (MEP) amplitudes and latencies were calculated. Statistical analysis found a significant effect of conditions (stimulation timings) on MEP amplitudes (P = 0.035). Paired-comparisons demonstrated lower corticospinal excitability during VIB at 1 s compared with 5 s (P = 0.025) and 10 s (P = 0.003), although none of them differed from baseline values. Results suggest a time-specific modulation of corticospinal excitability in muscles antagonistic to those vibrated, i.e., muscles involved in the perceived movement. An early decrease of excitability was observed at 1 s followed by a stabilization of values near baseline at subsequent time points. At 1 s, the illusion is not yet perceived or not strong enough to upregulate corticospinal networks coherent with the proprioceptive input. Spinal mechanisms, such as reciprocal inhibition, could also contribute to lower the corticospinal drive of nonvibrated muscles in short period before the illusion emerges. Our results suggest that neuromodulatory effects of VIB are likely time-dependent, and that future work is needed to further investigate underlying mechanisms. NEW & NOTEWORTHY The modulation of corticospinal excitability when perceiving a vibration (VIB)-induced kinesthetic illusion evolves dynamically over time. This modulation might be linked to the delayed occurrence and progressive increase in strength of the illusory perception in the first seconds after VIB start. Different spinal/cortical mechanisms could be at play during VIB, depending on the tested muscle, presence/absence of an illusion, and the specific timing at which corticospinal drive is tested pre/post VIB

Muscle Activity Distribution Features Extracted from HD sEMG to Perform Forearm Pattern Recognition

An efficient pattern recognition system based exclusively on forearm surface Electromyographic (sEMG) signals is proposed to provide a more intuitive control of a robotic arm used by some of the disabled. The main contribution of this paper is the use of an original set of features characterizing the muscle activity distribution obtained with high-density sEMG (HD sEMG) sensors. Contrary to simple sEMG, HD sEMG can produce muscle activity images with spatial distributions that differ according to forearm movement. In order to translate this distribution, the proposed set of features includes the center of gravity, the mean amplitude and the percentage of influence computed in each HD sEMG image divided in sub-images. Based on these features, the recognition system locates nine forearm movements with high classification accuracies (99.23%). The results in terms of the number of learning data, the image resolutions (spatial filtering) and the number of sub-images demonstrate the potential of the proposed recognition system and its good performance-complexity trade-off