Electromyographic Characteristics Research Articles

СРАВНИТЕЛЬНЫЙ АНАЛИЗ ХАРАКТЕРИСТИК ХОДЬБЫ В УСЛОВИЯХ КОСМИЧЕСКОГО ПОЛЕТА: АКТИВНЫЙ И ПАССИВНЫЙ РЕЖИМЫ РАБОТЫ БЕГУЩЕЙ ДОРОЖКИ

Walking and running on treadmill BD-2 in the active and passive modes are the core of the Russian countermeasure system on board the International space station (ISS). In the active mode, the belt moves owing to the electric drive and in the passive mode the cosmonaut makes the belt move with the help of leg force. One of the primary goals of this investigation is better understanding of the physiological aspects of these training sessions. Purpose of the investigation is comparative analysis of biomechanical and electromyographic characteristics of walking in long-term space missions (SM) using the BD-2 active and passive modes. The investigation involved 22 cosmonauts at the age of 45.7 ± 4.7 yrs. in the course of 115- to 340-day missions to the ISS. The cosmonauts performed the locomotion test monthly (no less than 4 times). Two sessions of the investigation were conducted prior to launch and two sessions on days 8 and 12 in the period of post-flight recovery. Protocol of the locomotion test (3-min walking) was performed twice, i.e. in the active and passive modes. Walking biomechanics was assessed using the podogram recorded with pressure cells built in sensing insoles. Electromyographic activity of the next four muscles was measured simultaneously: anterior tibial muscle, soleus, lateral gastrocnemius and quadriceps muscle of the thigh. Results demonstrate greater values of support reactions and electromyographic activity of the gravity-dependent soleus m. in the passive mode as compared to the active mode in all test sessions. This can be, probably, explained by a tighter foot contact with the treadmill belt in the passive mode and, as a consequence, increased intensity of support afferentation signals. It is notable that this difference grows in microgravity.

Needle electromyography abnormalities in the upper trapezius muscle in neuromuscular disorders.

: Needle electromyography (EMG) abnormalities in the trapezius muscle (TM) can be seen in neuromuscular disorders. The aim was to determine the characteristics of needle EMG abnormalities observed in the TM in neuromuscular disorders. The data of patients who applied to the Clinical Neurophysiology Laboratory of University of Health Sciences Adana City Training and Research Hospital between December 2018 and October 2021 were reviewed. Polio survivors, amyotrophic lateral sclerosis (ALS) patients, patients with sensorimotor polyneuropathy, patients with spinal cord lesions involving C2/C3/C4 segments, patients with spinal accessory nerve (SAN) lesions, neuralgic amyotrophy (NA) patients, and patients with myopathy were included. Needle EMG findings of the upper TM of the patients were analyzed. Positive sharp waves, fibrillation potentials, fasciculation potentials, myotonic discharges, and motor unit action potential (MUAP) changes were considered needle EMG abnormalities. Eighty-one polio survivors, 23 ALS patients, 39 patients with sensorimotor polyneuropathy, 10 patients with cervical spinal lesions, eight NA patients, seven patients with SAN lesions, and three patients with myopathy were included in the study. Fifteen (65.2%) ALS patients, 18 (22.2%) polio survivors, three (30%) patients with cervical spinal lesions, two (5.1%) patients with sensorimotor neuropathy, one (12.5%) NA patient, seven (100%) patients with SAN lesions, and two (66.7%) patients with myopathies had at least one needle EMG abnormality in the TM. Fasciculation potentials in the TM were seen in 10 (43.5%) ALS patients. In four patients with SAN lesions and one polio survivor, MUAP could not be obtained from the TM. There may be more frequent needle EMG abnormalities, particularly in ALS patients and patients with SAN lesions. Since the number of patients with myopathy included in this study was low, it is difficult to comment on the needle EMG features of the TM for these patients. In addition, this study indicated that fasciculation potentials in the TM are typical in ALS patients and that MUAP may not be obtained from the TM in patients with SAN lesions.

Amperometric Measurements by a Novel Aerosol Jet Printed Flexible Sensor for Wearable Applications

In the last years, increasing interest has been addressed on wearable devices for the continuous monitoring of biochemical profiles of patients and athletes. Among various markers, lactate represents one of the most interesting to deepen the knowledge on fatigue processes, in combination with traditional electromyographic features. In this work, we propose a new, low-cost, rapid, and flexible electrochemical amperometric lactate carbon biosensor fabricated by the novel technique of aerosol jet printing (AJP). The developed biosensors were combined with a paper-based microfluidic system and a chitosan-based stable functionalization to allow a long-term continuous monitoring of human daily activities. The sensor performances were evaluated by static and dynamic electrochemical tests in buffered media. The limit of detection is 11 ± 2 mM for the static setup and of 15 ± 6 mM for the dynamic setup, while sensitivity is 0.02 and 0.04 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> /mM, respectively, with single sigma standard deviation lower than 8%. Increasing and decreasing concentration steps of lactate induces comparable response times below 30 s. These results obtained in vitro under controlled laboratory conditions represent a promising starting point to pursue with ex-vivo and in vivo validation for the future development of stand-alone wearable patches for noninvasive lactate measurements.

Predicting postural control adaptation measuring EEG, EMG, and center of pressure changes: BioVRSea paradigm.

Introduction: Postural control is a sensorimotor mechanism that can reveal neurophysiological disorder. The present work studies the quantitative response to a complex postural control task. Methods: We measure electroencephalography (EEG), electromyography (EMG), and center of pressure (CoP) signals during a virtual reality (VR) experience called BioVRSea with the aim of classifying different postural control responses. The BioVRSea paradigm is based on six different phases where motion and visual stimulation are modulated throughout the experiment, inducing subjects to a different adaptive postural control strategy. The goal of the study is to assess the predictability of those responses. During the experiment, brain activity was recorded from a 64-channel EEG, muscle activity was determined with six wireless EMG sensors placed on lower leg muscles, and individual movement measured by the CoP. One-hundred and seventy-two healthy individuals underwent the BioVRSea paradigm and 318 features were extracted from each phase of the experiment. Machine learning techniques were employed to: (1) classify the phases of the experiment; (2) assess the most notable features; and (3) identify a quantitative pattern for healthy responses. Results: The results show that the EEG features are not sufficient to predict the distinct phases of the experiment, but they can distinguish visual and motion onset stimulation. EMG features and CoP features, when used jointly, can predict five out of six phases with a mean accuracy of 74.4% (±8%) and an AUC of 0.92. The most important feature to identify the different adaptive strategies is the Squared Root Mean Distance of points on Medio-Lateral axis (RDIST_ML). Discussion: This work shows the importance and the feasibility of a quantitative evaluation in a complex postural control task and demonstrates the potential of EEG, CoP, and EMG for assessing pathological conditions. These predictive systems pave the way for developing an objective assessment of pathological behavior PC responses. This will be a first step in identifying individual disorders and treatment options.

An improved feature selection approach using global best guided Gaussian artificial bee colony for EMG classification

Electromyography (EMG) measures muscle relaxes or contractions during muscular activity through EMG signal. It plays a vital role in identifying muscle-related problems for clinical diagnosis. This paper presents an efficient EMG feature selection technique for classifying 17 different prosthetic hand movements recorded from 11 subjects. Two variants of the Artificial Bee Colony (ABC) algorithm, namely: i) Global Best Guided ABC (GbestABC) and ii) Gaussian ABC (GABC), are employed to propose an Improved Artificial Bee Colony (ABC) algorithm called Global Best Guided Gaussian ABC (GGABC) for solving global optimization problems. GbestABC performs better in the exploitation phase, whereas GABC performs better in the exploration phase in searching for the optimal solutions. The proposed GGABC takes advantage of GbestABC and GABC to counterbalance basic ABC's exploitation and exploration capability. Further, a binary version of GGABC known as binary GGABC (BGGABC) is developed to solve binary optimization problems and select optimal EMG signal classification features. Extensive experiments are carried out in three phases: i) GGABC for global optimization problems ii) BGGABC for EMG feature selection problems with other meta-heuristic-based competitors iii) BGGABC for EMG feature selection problems with well-known filter based techniques. K-nearest neighbor (KNN) classifier is used in experiments to validate and investigate the effectiveness of the proposed algorithm. Experimental result shows that the BGGABC-based EMG feature selection achieved 94.13% average classification accuracy and 97.06% best classification accuracy. Obtained results confirm that the proposed algorithm outperforms or is competitive with state-of-the-art algorithms in EMG feature selection and classification.

A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal.

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesion from five Macaca fasicularis monkeys. The proposed classifier is based on a CNN using filtered segmented EMG signals from the pre- and post-lesion periods as inputs, while the kNN is designed using four hand-crafted EMG features. The results suggest that the CNN provides a promising classification technique for TSCI, compared to conventional machine learning classification. The kNN with hand-crafted EMG features classified the pre- and post-lesion EMG data with an F-measure of 89.7% and 92.7% for the left- and right-side muscles, respectively, while the CNN with the EMG segments classified the data with an F-measure of 89.8% and 96.9% for the left- and right-side muscles, respectively. Finally, the proposed deep learning classification model (CNN), with its learning ability of high-level features using EMG segments as inputs, shows high potential and promising results for use as a TSCI classification system. Future studies can confirm this finding by considering more subjects.

Study on diagnosis and treatment of lumbar disc herniation and related factors based on dynamic electromyography

To analyze dynamic electromyography characteristics and related factors of lumbar back muscle activity in patients with lumbar disc herniation, and to clarify the clinical significance of dynamic electromyography in the diagnosis and treatment of patients with lumbar disc herniation(LDH). From September 2014 to March 2021, 40 patients with lumbar disc herniation(LDH group) were detected by surface electromyography telemeter. There were 14 males and 26 females, aged from 20 to 61 years old, with an average of(40.68±10.56) years old, the course of illness was from 1 to 120 months, with an average of (17.75±27.56) months. In addition, 12 normal people were recruited as the control group. There were 2 males and 10 females. The age ranged from 24 to 53 years old, with an average of(36.50±10.30) years old. All subjects were subjected to dynamic electromyographic tests of the subthoracic erector spinae, lumbar erector spinae, and multifidus muscles during static standing and trunk flexion and extension. Compare the EMG activity data (average EMG amplitude, median frequency, original EMG graph) of the tested muscles between patients with lumbar disc herniation and normal people, and analyze the correlation between the general data of patients with lumbar disc herniation and the tested muscle EMG data. When standing still, the average electromyographic amplitude of the erector spinal muscle of the right and left thoracic segments of the subjects in the LDH group increased compared with the control group, and the difference was significant(P<0.05). In the trunk flexion and extension, the average electromyographic amplitude of the right and left proximal thoracic erector spinae, the right left lumbar erector spinae, and the right left multifidus muscle of the subjects in the LDH group are all larger than the control group, and the difference was significant(P<0.05). In the trunk flexion and extension, the median frequencies of the right left proximal thoracic erector spinae、the right left lumbar erector spinae, and the right left multifidus muscle of the subjects in the LDH group were all larger than the normal control group, and the difference was significant (P<0.05). During trunk flexion and extension, the original electromyographic patterns of subjects in the LDH group were significantly different from those in the control group. During the maintenance of the maximum trunk flexion of the subjects in the LDH group, there was a high level of electromyographic activity of the lower back muscles, and the electromyographic static signals that should appear regularly in the original signal could not be distinguished. When the trunk was flexed and extended, had gender, age, weight and height of subjects in the LDH group were not significantly correlated with the average EMG amplitude and median frequency of bilateral proximal thoracic, lumbar erector spinae and bilateral multifidus muscles respectively(P>0.05). Patients with lumbar disc herniation have characteristic surface EMG changes in the back muscles that are different from those of normal people. These features can more objectively reflect the patient's muscle condition and can be an effective indicator for the diagnosis and treatment effect evaluation of patients with lumbar disc herniation. It can be seen that surface electromyography is not only a detection method, it can be considered in the routine diagnosis and treatment plan of LDH to guide clinical work.

Dynamic gripping force estimation and reconstruction in EMG-based human-machine interaction

Electromyography (EMG) can reveal the state of muscle activity in advance, therefore, it has been widely used in human–machine interaction (HMI) to predict human intention. Force estimation from EMG signals is acknowledged as an important research topic in HMI. In order to develop a simple and smooth HMI system, it is necessary to estimate the dynamic force effectively and smoothly from a small number of EMG electrodes. In this paper, we have proposed an EMG-based dynamic force reconstruction scheme applied in HMI system. A deep neural prediction network using one-dimensional convolutional structure has been proposed to learn the complex EMG features automatically from three-channel EMG signals. This model was applied in our interactive system to estimate dynamic force and reconstruct it on a robotic gripper for precise EMG-based robot control. Our proposed model outperformed the two-dimensional convolutional neural network (CNN) method and feature-based linear regression. And it can meet the requirement of online interaction. The offline and online tests have shown good estimation performance with R2 of 0.99 and 0.83, respectively. The average prediction speed has reached 115.5 μs per sample. The system has avoided tedious feature extraction process and has demonstrated dynamic recognition in real time which can further advance various prosthesis and assistive robotic applications in the future.

Retrospective study on clinical manifestation, thigh MRI and electrophysiology characteristics of immune-mediated necrotizing myopathy

Objective: To summarize the clinical, thigh magnetic resonance (tMRI) and electromyographic (EMG) characteristics in patients with immune-mediated necrotizing myopathy (IMNM). Methods: A total of 32 IMNM patients who were admitted to the Department of Neurology from April 2019 to April 2021 were enrolled at the First Medical Centre of Chinese PLA General Hospital. According to the type of antibody, the patients were divided into anti-SRP antibody positive (SRP+) group, anti-HMGCR antibody positive (HMGCR+) group and seronegative (SN) group. The gender, age, course of disease, myositis antibodies, extramuscular manifestations, EMG were collected and analyzed among three groups. The characteristics of skeletal muscle were assessed by tMRI inflammatory edema and fat infiltration scores. Analysis of variance, Kruskal-Wallis test and Chi-square test were used to compare the differences in different clinical characteristics and tMRI scores among the three groups. When there was a statistical difference among the three groups, the comparison between the two groups was corrected by the Bonferroni method. Result: (1) Of the 32 patients, 20 were females (62.5%).The median age of onset was 47±14 years, 25 (78.1%) patients had an acute or subacute course.There were 17 (53.1%) with SRP+, 8 (25.0%) with HMGCR+, and 7 (21.9%) with MSAs (myositis specific antibodies) negative. Anti-Ro52 antibody was the most common combined antibody (12/32, 37.5%), among which 10 were in SRP+group.(2) The CK of all patients were elevated, median was 5 948 (4 229, 7 664) U/L. There was no statistical difference of MMT scores among three groups. The proximal limb score was lower than distal limb (P<0.01). The axial muscle score was lower than the distal limb score (P<0.05).(3) Extramuscular manifestations of HMGCR+ group were lower than those of the other two groups (12.5% vs. 71.4% and 76.5%, P<0.017). Rash (60.0% vs.14.3%, P<0.05) and interstitial pulmonary diseases (70.0% vs. 14.3%, P<0.05) were more common in patients with anti-SRP coexistence with anti-Ro52 than those with isolated anti-SRP. Connective tissue disease was more common in SN group (57.1% vs. 11.8% and 0, P<0.017).(4) tMRI showed fascial edema of SN group was more obvious than that of the other two groups (P<0.017). There was no statistical difference in the degree of fat infiltration and inflammatory edema among three groups, but SRP+ group had more cases of early fat infiltration.(5) Myotonic potentials (25.0% vs. 0 and 0, P<0.017) and compound repetitive discharges (CRDs) (50.0% vs. 5.9% and 0, P<0.017) were common in HMGCR+ group. Proteomic analysis found significantly different expressed proteins in skeletal muscle of patients with myotonic potentials or CRDs were associated with cytoskeleton, cell junction and extracellular matrix. Conclusion: IMNM with pure anti-SRP antibody positive and anti-HMGCR positive were mainly affected by skeletal muscles. Those who were co-positive for anti-SRP antibody and anti-Ro52 antibody had more extramuscular manifestations, which might be a special subtype of SRP+ group. This study proposed for the first time that myofascial inflammatory edema is an early sign of SN-IMNM injury. EMG of HMGCR+group were more prone to myotonia potential and CRDs.

A resistive force correlated electromyogram feature selection method for muscle strength prediction

The need for standardization of muscle strength measurement techniques has been of interest over many years. The relationship between Electromyography (EMG) and strength of isometric muscle contraction provides an alternative method of a more natural means of analyzing muscle strength. Quadriceps and Erector Spinae muscle groups are considered in this study. Muscle specific isometric protocol is followed where dynamometer resisted force and EMG are simultaneously acquired. A three level feature selection process based on the measured force and EMG is proposed. The first and second level selection is based on visible shape similarity and statistical Spearmans’ correlation respectively between the resisted force and EMG features, followed by feature ranking using Laplacian Score. A very weak but statistically insignificant (p > 0.05) correlation was observed for SSI and Log features for both Quadriceps (‘SSI’ ρ = 0.04, ‘LOG’ ρ = 0.13) and Erector Spinae (‘SSI’ ρ = 0.08, ‘LOG’ ρ = 0.002) muscle groups. The proposed feature set including IEMG, RMS, WL, and FE consistently top ranked in Laplacian score for all the muscles. Fuzzy C-means clustering method is used to classify low, moderate and strong muscle strength levels. In comparison to the popular feature set proposed by Hudgin, the performance of the proposed feature set revealed a better cluster separation (SI score > 0.5, DB Index < 0.5, CH Index > 100). The choice of three of clusters for low, moderate and strong muscle strength using the proposed feature set has also resulted in a low Hartington’s index (<3). The proposed feature set proves to be more robust in muscle strength classification using surface electromyogram signals.

Analysis and Classification of Muscular Paralysis Disease using Electromyography Signal with Machine Learning

Electrical activity of the muscles is characterized by Electromyography (EMG) signals. The EMG signal analysis form the basis for the diagnosis of muscular paralysis. The EMG signals from amyotrophic lateral sclerosis (ALS), and Myopathy are considered to analyze the paralysis. The statistical analysis of EMG signals from ALS, Myopathy, and Normal conditions, aid in the analysis and classification of paralysis. This work intends to analyze and classify the paralysis and normal conditions using EMG features extracted in time and frequency domains. Twelve statistical features are extracted from the EMG signals considered. Machine Learning techniques and deep learning techniques (DLT) are employed to perform the classification. multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), gradient boosting (GB), and nearest neighbor (NN) classifier models are used for the classification. The accuracy of the classifiers is calculated. The accuracy values obtained are 72% for MLP, 73% for SVM, 72% for RF, 71% for GB, and 69% for NN. The performance accuracy is better in SVM compared to other classifiers.

Application of Surface Electromyography in Exercise Fatigue: A Review.

Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

A reverse genetics and genomics approach to gene paralog function and disease: Myokymia and the juxtaparanode

The leucine-rich glioma-inactivated (LGI) family consists of four highly conserved paralogous genes, LGI1-4, that are highly expressed in mammalian central and/or peripheral nervous systems. LGI1 antibodies are detected in subjects with autoimmune limbic encephalitis and peripheral nerve hyperexcitability syndromes (PNHSs) such as Isaacs and Morvan syndromes. Pathogenic variations of LGI1 and LGI4 are associated with neurological disorders as disease traits including familial temporal lobe epilepsy and neurogenic arthrogryposis multiplex congenita 1 with myelin defects, respectively. No human disease has been reported associated with either LGI2 or LGI3. We implemented exome sequencing and family-based genomics to identify individuals with deleterious variants in LGI3 and utilized GeneMatcher to connect practitioners and researchers worldwide to investigate the clinical and electrophysiological phenotype in affected subjects. We also generated Lgi3-null mice and performed peripheral nerve dissection and immunohistochemistry to examine the juxtaparanode LGI3 microarchitecture. As a result, we identified 16 individuals from eight unrelated families with loss-of-function (LoF) bi-allelic variants in LGI3. Deep phenotypic characterization showed LGI3 LoF causes a potentially clinically recognizable PNHS trait characterized by global developmental delay, intellectual disability, distal deformities with diminished reflexes, visible facial myokymia, and distinctive electromyographic features suggestive of motor nerve instability. Lgi3-null mice showed reduced and mis-localized Kv1 channel complexes in myelinated peripheral axons. Our data demonstrate bi-allelic LoF variants in LGI3 cause a clinically distinguishable disease trait of PNHS, most likely caused by disturbed Kv1 channel distribution in the absence of LGI3.

Incremental Learning and Fault-Tolerant Classifier for Myoelectric Pattern Recognition Against Multiple Bursting Interferences

Bursting interference that causes a sudden and significant change in surface electromyography (sEMG) characteristics, can reduce the stability and security of myoelectric assistive robots. Current adaptation strategies for progressive-generated interference are incapable of dealing with bursting interference. To address this problem, an incremental learning and fault-tolerant classifier (ILFTC) was proposed by combining a Gaussian mixture model (GMM) ensemble and linear discriminant analysis (LDA), in conjunction with online update and marginalization schemes. Subsequently, an ILFTC-based myoelectric pattern recognition (MPR) strategy was developed to improve the robustness of MPR against multiple interferences, including outlier motion and missing/fault data owing to electrode loosening. Experiments on hand/wrist motions were conducted to validate the anti-interference performance of the ILFTC. Experimental results showed that the ILFTC could effectively resist the two types of bursting interference and produce a significant improvement in the recognition performance over traditional classifiers, as well as the methods presented in previous studies. The results show that the proposed method has the potential to enhance the robustness of myoelectric assistive robots.

COVID-19 and Guillain-Barré syndrome: A single-center prospective case series with a 1-year follow-up.

Single reports of Guillain-Barré syndrome (GBS) have been reported worldwide during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. While case reports are likely to be biased toward uncommon clinical presentations, systematic assessment of prospective series can highlight the true clinical features and spectrum. In this prospective, observational study, we included all consecutive patients who developed GBS. In patients with SARS-CoV-2 infection as antecedent, the time-gap between the infection and GBS onset had to be ≤30 days. The referral was a neurological University Research Hospital, in the Italian Region more severely involved by the pandemic, and hospitalizing both COVID+ and non-COVID neurological diseases. Clinical, laboratory, cerebrospinal fluid, and electromyographic features of GBS diagnosed between March 2020 and March 2021 were compared to a retrospective series of GBS diagnosed between February 2019 and February 2020 (control population). Nasopharyngeal swab was still positive at GBS onset in 50% of patients. Mild-to-moderate COVID-related pneumonia, as assessed by X-ray (6 patients) or X-ray plus computerized tomography (2 patients) co-occurred in 6 of 10 patients. GBS diagnosed during the pandemic period, including 10 COVID-GBS and 10 non–COVID-GBS, had higher disability on admission (P = .032) compared to the GBS diagnosed between February 2019 and 2020, possibly related to later hospital referral in the pandemic context. Compared to non–COVID-GBS (n = 10) prospectively diagnosed in the same period (March 2020–2021), post–COVID-GBS (n = 10) had a higher disability score on admission (P = .028), lower sum Medical Research Council score (P = .022) and lymphopenia (P = .025), while there were no differences in GBS subtype/variant, severity of peripheral involvement, prognosis and response to treatment. Cerebrospinal fluid search for SARS-CoV-2 RNA and antiganglioside antibodies were negative in all COVID+ patients. Temporal clustering of cases, coinciding with the waves of the pandemic, and concomitant reduction of the incidence of COVID-negative GBSs may indicate a role for SARS-CoV-2 infection in the development of GBS, although the association may simply be related to a bystander effect of systemic inflammation; lack of prevalence of specific GBS subtypes in post–COVID-GBS also support this view. GBS features and prognosis are not substantially different compared to non–COVID-GBS.

Identifying Interaction Forces Via EMG Under Changing Motion Dynamics.

Musculoskeletal injuries can severely inhibit performance of activities of daily living. In order to regain function, rehabilitation is often required. Assistive rehabilitation devices can be used to increase arm mobility by guiding therapeutic exercises or assisting with motion. Electromyography (EMG) may be able to provide an intuitive interface between the patient and the device if appropriate classification models allow smart systems to relate these signals to the desired device motion. Unfortunately, the accuracy of pattern recognition models classifying motion in constrained laboratory environments significantly drops when used for detecting dynamic unconstrained movements. The objectives of this study were to quantity how various motion factors affect arm muscle activations during dynamic motion, and to use these motion factors and EMG signals for detecting interaction forces between the person and the environment during motion. The results quantity how EMG features change significantly with variations in arm positions, interaction forces, and motion velocities. The results also show that pattern recognition models were able to detect intended characteristics of motion based solely on EMG signals. Prediction of force was improved from 73.77% correct to 79.17% accuracy during elbow flexion-extension by properly selecting the features, and providing measurable arm position and velocity information as additional inputs to a linear discriminant analysis model.

Incremental Adaptive Gesture Classifier for Upper Limb Prostheses

Myoelectric pattern recognition is widely used to control upper limb prostheses. However, the non-stationary characteristics of electromyography (EMG) signals, caused by physiological changes (e.g. muscle fatigue) or non-physiological changes (e.g. the electrode- skin impedance), hinder the use of prostheses and deteriorate the performance of the myoelectric control system. In this paper, a set of robust features is proposed to be integrated with adaptive learning techniques in order to improve the myoelectric performance. Four types of features are proposed, namely the H, HI, AI, and AIH features. The H features correspond to the histogram-oriented gradient (HOG) algorithm of the High-Density surface Electromyography map (HD-sEMG map). On the other hand, the HI features are generated by combining the H features and the intensity feature that is evaluated from the HD-sEMG map. AI is the intensity features calculated from the segmented HD-sEMG maps constructed in the individual channel. Finally, AIH features are obtained by combining the H features and AI features. Offline and online adaptive tests are conducted to evaluate the proposed features. The results show that employing the proposed AI features with an adaptive classifier improves the classification accuracy from 91.58% to 97.2% in online classification setups. Results also show that the AI features are more robust against noise than TD features. The average classification accuracy is reduced by 0.7% when additive White Gaussian noise is applied, in comparison to 5.8% reduction in accuracy when TD features is used. The results confirm the robustness of the proposed features extracted from the HD-sEMG map.

Distinctive physiological muscle synergy patterns define the Box and Block Task execution as revealed by electromyographic features.

Stroke survivors experience muscular pattern alterations of the upper limb that decrease their ability to perform daily-living activities. The Box and Block test (BBT) is widely used to assess the unilateral manual dexterity. Although BBT provides insights into functional performance, it returns limited information about the mechanisms contributing to the impaired movement. This study aims at exploring the BBT by means of muscle synergies analysis during the execution of BBT in a sample of 12 healthy participants with their dominant and non-dominant upper limb. Results revealed that: (i) the BBT can be described by 1 or 2 synergies; the number of synergies (ii) does not differ between dominant and non-dominant sides and (iii) varies considering each phase of the task; (iv) the transfer phase requires more synergies. Clinical Relevance- This preliminary study characterizes muscular synergies during the BBT task in order to establish normative patterns that could assist in understanding the neuromuscular demands and support future evaluations of stroke deficits.

Dual-Layer Ranking Feature Selection Method Based on Statistical Formula for Driver Fatigue Detection of EMG Signals

Electromyography (EMG) signals are one of the most studied inputs for driver drowsiness detection systems. As the number of EMG features available can be daunting, finding the most significant and minimal subset features is desirable. Hence, a simplified feature selection method is necessary. This work proposed a dual-layer ranking feature selection algorithm based on statistical formula f EMG signals for driver fatigue detection. In the beginning, in the first layer, 21 filter algorithms were calculated to rank 47 sets of EMG features (25 time-domain and 9 frequency-domain) and applied to six classifiers. Then, in the second layer, all the ranks were re-ranked based on the statistical formula (average, median, mode and variance). The classification performance of all rankings was compared along with the number of features. The highest classification accuracy achieved was 95% for 12 features using the Average Statistical Rank (ASR) and LDA classifier. It is conclusive that a combination of features from the time domain and frequency domain can deliver better performance compared to a single domain feature. Concurrently, the statistical rank ASR performed better than the single filter rank by reducing the number of features. The proposed model can be a benchmark for the enhanced feature selection method for EMG driver fatigue signal.

A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteers.

The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model's prediction accuracy on the Earable device's classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings.