Publications
775 results found
Dimitrov H, Bull AMJ, Farina D, 2023, High-density EMG, IMU, kinetic, and kinematic open-source data for comprehensive locomotion activities., Sci Data, Vol: 10
Novel sensor technology enables new insights in the neuromechanics of human locomotion that were previously not possible. Here, we provide a dataset of high-density surface electromyography (HDsEMG) and high-resolution inertial measurement unit (IMU) signals, along with motion capture and force data for the lower limb of 10 healthy adults during multiple locomotion modes. The participants performed level-ground and slope walking, as well as stairs ascent/descent, side stepping gait, and stand-to-walk and sit-to-stand-to-walk, at multiple walking speeds. These data can be used for the development and validation of locomotion mode recognition and control algorithms for prosthetics, exoskeletons, and bipedal robots, and for motor control investigations.
Kashiwakura J, Sagastegui Alva PG, Mendez Guerra I, et al., 2023, Task-oriented design of a multi-degree of freedom upper limb prosthesis with integrated myocontrol and sensory feedback, IEEE Transactions on Medical Robotics and Bionics, Vol: 5, Pages: 1067-1081, ISSN: 2576-3202
Despite the progresses in upper limb prosthetic technologies of the past decades, there is still a large gap between the user needs and the available devices. Here, we describe the design and validation of a fully integrated, multi-degree of freedom upper limb prosthetic system designed on the basis of user survey studies. The system has five degrees of actuation, a combination of direct and under-actuated activation to produce the grasping/pinching force to perform activities of daily living, an active wrist, a closed-loop tactile biofeedback system, and simultaneous/proportional myoelectric control. The aforementioned features have been successfully integrated into a standalone prosthetic system. The system has been tested for its capacity to reproduce the human grasp when manipulating objects common in daily living. Moreover, the system underwent standardized clinical tests, showing a significant decrease in both shoulder and trunk compensatory movements with respect to a state-of-the-art commercial prosthesis.
Panchal M, Tanzarella S, Jung MK, et al., 2023, Mapping intrinsic and extrinsic muscle myoelectric activity during natural dynamic movements into finger and wrist kinematics using deep learning prediction models, IEEE Transactions on Human-Machine Systems, Vol: 53, Pages: 924-934, ISSN: 2168-2291
We investigate the use of high-density surface EMG (HDsEMG) recordings of intrinsic hand muscles, along with those from extrinsic muscles, on finger and wrist kinematic prediction performance. We incorporate these HDsEMG signals using a framework basedon a custom hybrid convolutional-recurrent deep learningmodel. Methods: Five healthy subjects performed a widevariety of motion tasks activating multiple degrees of freedom of the wrist and fingers. During the tasks, HDsEMG signals were recorded from extrinsic and intrinsic muscles of the hand while motion capture technology tracked the hand/wrist kinematics. A convolutional-recurrent model architecture was designed and trained on the recorded dataset, incorporating both residual connections as well as inception convolutional structures. Results: The proposed model led to greater regression accuracy over the simultaneous prediction of 12 joint angles (CC, MAE and RMSE of 0.850, 4.84 degrees and 11.2 degrees respectively) than previously proposed mapping models, when incorporating both intrinsic and extrinsic muscle signals. The inclusion ofboth sets of hand muscles also led to statistically greaterperformance than the same model trained on only extrinsic muscle data. Conclusion: We show accurate predictions of hand/wrist kinematics from combined extrinsic and intrinsic hand muscle myoelectric activity, using a convolutionalrecurrent hybrid deep learning model. This greater performance is replicated over several subjects and across multidegree of freedom motion tasks. Significance: Our developed system (electrode setup and deep neural networks) can be translated into a compact wearable interface in thefuture for medical as well as consumer applications.
Rohlén R, Lubel E, Grandi Sgambato B, et al., 2023, Spatial decomposition of ultrafast ultrasound images to identify motor unit activity - A comparative study with intramuscular and surface EMG., J Electromyogr Kinesiol, Vol: 73
The smallest voluntarily controlled structure of the human body is the motor unit (MU), comprised of a motoneuron and its innervated fibres. MUs have been investigated in neurophysiology research and clinical applications, primarily using electromyographic (EMG) techniques. Nonetheless, EMG (both surface and intramuscular) has a limited detection volume. A recent alternative approach to detect MUs is ultrafast ultrasound (UUS) imaging. The possibility of identifying MU activity from UUS has been shown by blind source separation (BSS) of UUS images, using optimal separation spatial filters. However, this approach has yet to be fully compared with EMG techniques for a large population of unique MU spike trains. Here we identify individual MU activity in UUS images using the BSS method for 401 MU spike trains from eleven participants based on concurrent recordings of either surface or intramuscular EMG from forces up to 30% of the maximum voluntary contraction (MVC) force. We assessed the BSS method's ability to identify MU spike trains from direct comparison with the EMG-derived spike trains as well as twitch areas and temporal profiles from comparison with the spike-triggered-averaged UUS images when using the EMG-derived spikes as triggers. We found a moderate rate of correctly identified spikes (53.0 ± 16.0%) with respect to the EMG-identified firings. However, the MU twitch areas and temporal profiles could still be identified accurately, including at 30% MVC force. These results suggest that the current BSS methods for UUS can accurately identify the location and average twitch of a large pool of MUs in UUS images, providing potential avenues for studying neuromechanics from a large cross-section of the muscle. On the other hand, more advanced methods are needed to address the convolutive and partly non-linear summation of velocities for recovering the full spike trains.
Caillet AH, Avrillon S, Kundu A, et al., 2023, Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units, eNeuro, Vol: 10, ISSN: 2373-2822
The spinal motor neurons are the only neural cells whose individual activity can be non-invasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IED). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5-83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56±14; range: 39-79) and the percentage of early recruited motor units within these samples (29±14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies.Significance StatementThe application of source separation methods to multi-channel EMG signals recorded with grids of electrodes enables users to accurately identify the activity of individual motor units. However, the design parameters of these grids have never been discussed. They are usually arbitrarily fixed, often bas
Mayer KM, Del Vecchio A, Eskofier BM, et al., 2023, Unsupervised neural decoding of signals recorded by thin-film electrode arrays implanted in muscles using autoencoding with a physiologically derived optimisation criterion, BIOMEDICAL SIGNAL PROCESSING AND CONTROL, Vol: 86, ISSN: 1746-8094
Wang H, Qi Y, Yao L, et al., 2023, A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, ISSN: 2162-237X
Levine J, Avrillon S, Farina D, et al., 2023, Two motor neuron synergies, invariant across ankle joint angles, activate the triceps surae during plantarflexion, JOURNAL OF PHYSIOLOGY-LONDON, ISSN: 0022-3751
Grandi Sgambato B, Hasbani M, Barsakcioglu D, et al., 2023, High performance wearable ultrasound as a human-machine interface for wrist and hand kinematic tracking, IEEE Transactions on Biomedical Engineering, Pages: 1-10, ISSN: 0018-9294
Objective: Non-invasive human machine interfaces (HMIs) have high potential in medical, entertainment, and industrial applications. Traditionally, surface electromyography (sEMG) has been used to track muscular activity and infer motor intention. Ultrasound (US) has received increasing attention as an alternative to sEMG-based HMIs. Here, we developed a portable US armband system with 24 channels and a multiple receiver approach, and compared it with existing sEMG- and US-based HMIs on movement intention decoding. Methods: US and motion capture data was recorded while participants performed wrist and hand movements of four degrees of freedom (DoFs) and their combinations. A linear regression model was used to offline predict hand kinematics from the US (or sEMG, for comparison) features. The method was further validated in real-time for a 3-DoF target reaching task. Results: In the offline analysis, the wearable US system achieved an average R2 of 0.94 in the prediction of four DoFs of the wrist and hand while sEMG reached a performance of R2=0.06 . In online control, the participants achieved an average 93% completion rate of the targets. Conclusion: When tailored for HMIs, the proposed US A-mode system and processing pipeline can successfully regress hand kinematics both in offline and online settings with performances comparable or superior to previously published interfaces. Significance: Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The wearable US system allowed for robust proportional and simultaneous control over multiple DoFs in both offline and online settings.
Zicher B, Ibáñez J, Farina D, 2023, Beta inputs to motor neurons do not directly contribute to volitional force modulation, The Journal of Physiology, Vol: 601, Pages: 3173-3185, ISSN: 0022-3751
Neural oscillatory activity in the beta band (13–30 Hz) is prominent in the brain and it is transmitted partly linearly to the spinal cord and muscles. Multiple views on the functional relevance of beta activity in the motor system have been proposed. Previous simulation work suggested that pools of spinal motoneurons (MNs) receiving a common beta input could demodulate this activity, transforming it into low-frequency neural drive that could alter force production in muscles. This may suggest that common beta inputs to muscles have a direct role in force modulation. Here we report the experimental average levels and ranges of common beta activity in spinal MNs projecting to single muscles and use a computational model of a MN pool to test if the experimentally observed beta levels in MNs can influence force. When beta was modelled as a continuous activity, the amplitude needed to produce non-negligible changes in force corresponded to beta representation in the MN pool that was far above the experimental observations. On the other hand, when beta activity was modelled as short-lived events (i.e. bursts of beta activity separated by intervals without beta oscillations), this activity approximated levels that could cause small changes in force with estimated average common beta inputs to the MNs compatible with the experimental observations. Nonetheless, bursting beta is unlikely to be used for force control due to the temporal sparsity of this activity. It is therefore concluded that beta oscillations are unlikely to contribute to the voluntary modulation of force.
Gholinezhad S, Farina D, Dosen S, et al., 2023, Encoding force modulation in two electrotactile feedback parameters strengthens sensory integration according to maximum likelihood estimation., Sci Rep, Vol: 13
Bidirectional human-machine interfaces involve commands from the central nervous system to an external device and feedback characterizing device state. Such feedback may be elicited by electrical stimulation of somatosensory nerves, where a task-relevant variable is encoded in stimulation amplitude or frequency. Recently, concurrent modulation in amplitude and frequency (multimodal encoding) was proposed. We hypothesized that feedback with multimodal encoding may effectively be processed by the central nervous system as two independent inputs encoded in amplitude and frequency, respectively, thereby increasing state estimate quality in accordance with maximum-likelihood estimation. Using an adaptation paradigm, we tested this hypothesis during a grasp force matching task where subjects received electrotactile feedback encoding instantaneous force in amplitude, frequency, or both, in addition to their natural force feedback. The results showed that adaptations in grasp force with multimodal encoding could be accurately predicted as the integration of three independent inputs according to maximum-likelihood estimation: amplitude modulated electrotactile feedback, frequency modulated electrotactile feedback, and natural force feedback (r2 = 0.73). These findings show that multimodal electrotactile feedback carries an intrinsic advantage for state estimation accuracy with respect to single-variable modulation and suggest that this scheme should be the preferred strategy for bidirectional human-machine interfaces with electrotactile feedback.
Ibanez Pereda J, Zicher B, Brown KE, et al., 2023, Standard intensities of transcranial alternating current stimulation over the motor cortex do not entrain corticospinal inputs to motor neurons, The Journal of Physiology, Vol: 601, Pages: 3187-3199, ISSN: 0022-3751
Transcranial alternating current stimulation (TACS) is commonly used to synchronise a cortical area and its outputs to the stimulus waveform, but evidence for this based on brain recordings in humans is challenging. The corticospinal tract transmits beta oscillations (~21Hz) from motor cortex to tonically contracted limb muscles linearly. Therefore, muscle activity may be used to measure the level of beta entrainment in the corticospinal tract due to TACS over motor cortex. Here, we assessed if TACS is able to modulate the neural inputs to muscles, which would provide indirect evidence for TACS-driven neural entrainment. In the first part of this study, we ran simulations of motor neuron (MN) pools receiving inputs from corticospinal neurons with different levels of beta entrainment. Results suggest that MNs are highly sensitive to changes in corticospinal beta activity. Then, we ran experiments on healthy human subjects (N=10) in which TACS (at 1mA) was delivered over the motor cortex at 21Hz (beta stimulation), or at 7Hz or 40Hz (control conditions) while the abductor digiti minimi or the tibialis anterior muscle were tonically contracted. Muscle activity was measured using high-density electromyography, which allowed us to decompose the activity of pools of motor units innervating the muscles. By analysing motor unit pool activity, we observed that none of the TACS conditions could consistently alter the spectral contents of the common neural inputs received by the muscles. These results suggest that 1mA-TACS over motor cortex given at beta frequencies does not entrain corticospinal activity.
Hug F, Avrillon S, Sarcher A, et al., 2023, Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi-joint isometric task, The Journal of Physiology, Vol: 601, Pages: 3201-3219, ISSN: 0022-3751
Movements are reportedly controlled through the combination of synergies that generate specific motor outputs by imposing an activation pattern on a group of muscles. To date, the smallest unit of analysis of these synergies has been the muscle through the measurement of its activation. However, the muscle is not the lowest neural level of movement control. In this human study (n = 10), we used a purely data-driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity during an isometric multi-joint task. Specifically, high-density surface electromyography recordings from six lower limb muscles were decomposed into motor neurons spiking activity. We analyzed these activities by identifying their common low-frequency components, from which networks of correlated activity to the motor neurons were derived and interpreted as networks of common synaptic inputs. The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). In addition, groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles – including distant muscles – received common inputs. Our study supports the theory that movements are produced through the control of small numbers of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy. We provide a new neural framework for a deeper understanding of the structure of common inputs to motor neurons.Abstract figure legend Ten participants performed an isometric multi-joint task, which consisted in producing force on an instrumented pedal. Adhesive grids of 64 electrodes were placed over six lower limb muscles (gastrocnemius medialis [GM] and lateralis [GL], vastus lateralis [VL] and medialis [VM], biceps femoris [BF], semit
Hug F, Avrillon S, Sarcher A, et al., 2023, Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi-joint isometric task, JOURNAL OF PHYSIOLOGY-LONDON, Vol: 601, Pages: 3201-3219, ISSN: 0022-3751
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- Citations: 7
Farina D, Enoka RM, 2023, Evolution of surface electromyography: from muscle electrophysiology towards neural recording and interfacing, Journal of Electromyography and Kinesiology, Vol: 71, Pages: 1-8, ISSN: 1050-6411
Surface electromyography (EMG) comprises a recording of electrical activity from the body surface generated by muscle fibres during muscle contractions. Its characteristics depend on the fibre membrane potentials and the neural activation signal sent from the motor neurons to the muscles. EMG has been classically used as the primary investigation tool in kinesiology studies in a variety of applications. More recently, surface EMG techniques have evolved from single-channel methods to high-density systems with hundreds of electrodes. High-density EMG recordings can be deconvolved to estimate the discharge times of spinal motor neurons innervating the recorded muscles, with algorithms that have been developed and validated in the last two decades. Within limits and with some variability across muscles, these techniques provide a non-invasive method to study relatively large populations of motor neurons in humans. Surface EMG is thus evolving from a peripheral measure of muscle electrical activity towards a neural recording and neural interfacing signal. These advances in technology have had a major impact on our fundamental understanding of the neural control of movement and have exposed new perspectives in neurotechnologies. Here we provide an overview and perspective of modern EMG technology, as derived from past achievements, and its impact in neurophysiology and neural engineering.
Farina D, Clarke A, 2023, Deep metric learning with locality sensitive mining for self-correcting source separation of neural spiking signals, IEEE Transactions on Cybernetics, Pages: 1-11, ISSN: 1083-4419
Automated source separation algorithms have become a central tool in neuroengineering and neuroscience, wherethey are used to decompose neurophysiological signal into its constituent spiking sources. However, in noisy or highly multivariaterecordings these decomposition techniques often make a largenumber of errors. Such mistakes degrade online human-machineinterfacing methods and require costly post-hoc manual cleaningin the offline setting. In this paper we propose an automated errorcorrection methodology using a deep metric learning (DML)framework, generating embedding spaces in which spiking eventscan be both identified and assigned to their respective sources.Furthermore, we investigate the relative ability of different DMLtechniques to preserve the intra-class semantic structure neededto identify incorrect class labels in neurophysiological time series.Motivated by this analysis, we propose locality sensitive mining,an easily implemented sampling-based augmentation to typicalDML losses which substantially improves the local semanticstructure of the embedding space. We demonstrate the utilityof this method to generate embedding spaces which can be usedto automatically identify incorrectly-labelled spiking events withhigh accuracy
Guo W, Jiang N, Farina D, et al., 2023, Multi-Attention Feature Fusion Network for Accurate Estimation of Finger Kinematics From Surface Electromyographic Signals, IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, Vol: 53, Pages: 512-519, ISSN: 2168-2291
Casolo A, Maeo S, Balshaw TG, et al., 2023, Non-invasive estimation of muscle fibre size from high-density electromyography, The Journal of Physiology, Vol: 601, Pages: 1831-11850, ISSN: 0022-3751
Because of the biophysical relation between muscle fibre diameter and the propagation velocity of action potentials along the muscle fibres, motor unit conduction velocity could be a non-invasive index of muscle fibre size in humans. However, the relation between motor unit conduction velocity and fibre size has been only assessed indirectly in animal models and in human patients with invasive intramuscular EMG recordings, or it has been mathematically derived from computer simulations. By combining advanced non-invasive techniques to record motor unit activity in vivo, i.e. high-density surface EMG, with the gold standard technique for muscle tissue sampling, i.e. muscle biopsy, here we investigated the relation between the conduction velocity of populations of motor units identified from the biceps brachii muscle, and muscle fibre diameter. We demonstrate the possibility of predicting muscle fibre diameter (R2 = 0.66) and cross-sectional area (R2 = 0.65) from conduction velocity estimates with low systematic bias (∼2% and ∼4% respectively) and a relatively low margin of individual error (∼8% and ∼16%, respectively). The proposed neuromuscular interface opens new perspectives in the use of high-density EMG as a non-invasive tool to estimate muscle fibre size without the need of surgical biopsy sampling. The non-invasive nature of high-density surface EMG for the assessment of muscle fibre size may be useful in studies monitoring child development, ageing, space and exercise physiology, although the applicability and validity of the proposed methodology need to be more directly assessed in these specific populations by future studies.
Del Vecchio A, Germer CM, Kinfe TM, et al., 2023, The Forces Generated by Agonist Muscles during Isometric Contractions Arise from Motor Unit Synergies, JOURNAL OF NEUROSCIENCE, Vol: 43, Pages: 2860-2873, ISSN: 0270-6474
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- Citations: 4
Farina D, Vujaklija I, Branemark R, et al., 2023, Toward higher-performance bionic limbs for wider clinical use, Nature Biomedical Engineering, Vol: 7, Pages: 473-485, ISSN: 2157-846X
Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual performance is substantially limited by current technologies for the interfacing of the robotic devices with the body and for transferring motor and sensory information bidirectionally between the prosthesis and the user. In this Perspective, we argue that direct skeletal attachment of bionic devices via osseointegration, the amplification of neural signals by targeted muscle innervation, improved prosthesis control via implanted muscle sensors and advanced algorithms, and the provision of sensory feedback by means of electrodes implanted in peripheral nerves, should all be leveraged towards the creation of a new generation of high-performance bionic limbs. These technologies have been clinically tested in humans, and alongside mechanical redesigns and adequate rehabilitation training should facilitate the wider clinical use of bionic limbs.
Maksymenko K, Clarke AK, Mendez Guerra I, et al., 2023, A myoelectric digital twin for fast and realistic modelling in deep learning., Nat Commun, Vol: 14
Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.
Maksymenko K, Clarke AK, Mendez Guerra I, et al., 2023, A myoelectric digital twin for fast and realistic modelling in deep learning, Nature Communications, Vol: 14, Pages: 1-15, ISSN: 2041-1723
Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.
Vujaklija I, IEEE Member, Ki Jung M, et al., 2023, Biomechanical analysis of body movements of myoelectric prosthesis users during standardized clinical tests, IEEE Transactions on Biomedical Engineering, Vol: 70, Pages: 789-799, ISSN: 0018-9294
Objective: The objective clinical evaluation of user's capabilities to handle their prosthesis is done using various tests which primarily focus on the task completion speed and do not explicitly account for the potential presence of compensatory motions. Given that the excessive body compensation is a common indicator of inadequate prosthesis control, tests which include subjective observations on the quality of performed motions have been introduced. However, these metrics are then influenced by the examiner's opinions, skills, and training making them harder to standardize across patient pools and compare across different prosthetic technologies. Here we aim to objectively quantify the severity of body compensations present in myoelectric prosthetic hand users and evaluate the extent to which traditional objective clinical scores are still able to capture them. Methods: We have instructed 9 below-elbow prosthesis users and 9 able-bodied participants to complete three established objective clinical tests: Box-and-Blocks-Test, Clothespin-Relocation-Test, and Southampton-Hand-Assessment-Procedure. During all tests, upper-body kinematics has been recorded. Results: While the analysis showed that there are some correlations between the achieved clinical scores and the individual body segment travel distances and average speeds, there were only weak correlations between the clinical scores and the observed ranges of motion. At the same time, the compensations were observed in all prosthesis users and, for the most part, they were substantial across the tests. Conclusion: The sole reliance on the currently available objective clinical assessment methods seems inadequate as the compensatory movements are prominent in prosthesis users and yet not sufficiently accounted for.
Free DB, Syndergaard I, Pigg AC, et al., 2023, Essential tremor accentuates the pattern of tremor-band coherence between upper limb muscles, JOURNAL OF NEUROPHYSIOLOGY, Vol: 129, Pages: 524-540, ISSN: 0022-3077
Martinez-Valdes E, Enoka RM, Holobar A, et al., 2023, Consensus for experimental design in electromyography (CEDE) project: Single motor unit matrix., J Electromyogr Kinesiol, Vol: 68
The analysis of single motor unit (SMU) activity provides the foundation from which information about the neural strategies underlying the control of muscle force can be identified, due to the one-to-one association between the action potentials generated by an alpha motor neuron and those received by the innervated muscle fibers. Such a powerful assessment has been conventionally performed with invasive electrodes (i.e., intramuscular electromyography (EMG)), however, recent advances in signal processing techniques have enabled the identification of single motor unit (SMU) activity in high-density surface electromyography (HDsEMG) recordings. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, provides recommendations for the recording and analysis of SMU activity with both invasive (needle and fine-wire EMG) and non-invasive (HDsEMG) SMU identification methods, summarizing their advantages and disadvantages when used during different testing conditions. Recommendations for the analysis and reporting of discharge rate and peripheral (i.e., muscle fiber conduction velocity) SMU properties are also provided. The results of the Delphi process to reach consensus are contained in an appendix. This matrix is intended to help researchers to collect, report, and interpret SMU data in the context of both research and clinical applications.
Nowak M, Vujaklija I, Sturma A, et al., 2023, Simultaneous and proportional real-time myocontrol of up to three degrees of freedom of the wrist and hand, IEEE Transactions on Biomedical Engineering, Vol: 70, Pages: 459-469, ISSN: 0018-9294
Achieving robust, intuitive, simultaneous and proportional control over multiple degrees of freedom (DOFs) is an outstanding challenge in the development of myoelectric prosthetic systems. Since the priority inmyoelectric prosthesis solutions is robustness and stability, their number of functions is usually limited. Objective: Here, we introduce a system for intuitive concurrent hand and wrist control, based on a robust feature-extraction protocol and machine-learning. Methods: Using the meanabsolute value of high-density EMG, we train a ridge-regressor (RR) on only the sustained portions of the single-DOF contractions and leverage the regressor’s inherent ability to provide simultaneous multi-DOF estimates. In this way, we robustly capture the amplitude information of the inputs while harnessing the power of the RR to extrapolate otherwise noisy and often overfitted estimations of dynamic portions of movements. Results: The real-time evaluation of the system on 13 able-bodied participants and an amputee shows that almost all single-DOF tasks could be reached (96% success rate), while at the same time users were able to complete most of the two-DOF (62%) and even some of the very challenging three-DOF tasks (37%). To further investigate the translational potential of the approach, we reduced the original 192-channel setup to a 16-channel configuration and the observed performance did not deteriorate. Notably, the amputee performed similarly well to the other participants, according to all considered metrics. Conclusion: This is the first real-time operated myocontrol system that consistently provides intuitive simultaneous and proportional control over 3-DOFs of wrist and hand, relying on only surface EMG signals from the orearm. Significance: Focusing on reduced complexity, a real-time test and the inclusion of an amputee in the study demonstrate the translational potential of the control system for future applications in prosthetic control.
Tereshenko V, Maierhofer U, Dotzauer DC, et al., 2023, Newly identified axon types of the facial nerve unveil supplemental neural pathways in the innervation of the face., J Adv Res, Vol: 44, Pages: 135-147
INTRODUCTION: Neuromuscular control of the facial expressions is provided exclusively via the facial nerve. Facial muscles are amongst the most finely tuned effectors in the human motor system, which coordinate facial expressions. In lower vertebrates, the extracranial facial nerve is a mixed nerve, while in mammals it is believed to be a pure motor nerve. However, this established notion does not agree with several clinical signs in health and disease. OBJECTIVES: To elucidate the facial nerve contribution to the facial muscles by investigating axonal composition of the human facial nerve. To reveal new innervation pathways of other axon types of the motor facial nerve. METHODS: Different axon types were distinguished using specific molecular markers (NF, ChAT, CGRP and TH). To elucidate the functional role of axon types of the facial nerve, we used selective elimination of other neuronal support from the trigeminal nerve. We used retrograde neuronal tracing, three-dimensional imaging of the facial muscles, and high-fidelity neurophysiological tests in animal model. RESULTS: The human facial nerve revealed a mixed population of only 85% motor axons. Rodent samples revealed a fiber composition of motor, afferents and, surprisingly, sympathetic axons. We confirmed the axon types by tracing the originating neurons in the CNS. The sympathetic fibers of the facial nerve terminated in facial muscles suggesting autonomic innervation. The afferent fibers originated in the facial skin, confirming the afferent signal conduction via the facial nerve. CONCLUSION: These findings reveal new innervation pathways via the facial nerve, support the sympathetic etiology of hemifacial spasm and elucidate clinical phenomena in facial nerve regeneration.
Pascual-Valdunciel A, Lopo-Martínez V, Beltrán-Carrero AJ, et al., 2023, Classification of kinematic and electromyographic signals associated with pathological tremor using machine and deep learning., Entropy (Basel, Switzerland), Vol: 25, Pages: 1-13, ISSN: 1099-4300
Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.
Tereshenko V, Maierhofer U, Dotzauer DC, et al., 2023, Axonal mapping of the motor cranial nerves, Frontiers in Neuroanatomy, Vol: 17
Basic behaviors, such as swallowing, speech, and emotional expressions are the result of a highly coordinated interplay between multiple muscles of the head. Control mechanisms of such highly tuned movements remain poorly understood. Here, we investigated the neural components responsible for motor control of the facial, masticatory, and tongue muscles in humans using specific molecular markers (ChAT, MBP, NF, TH). Our findings showed that a higher number of motor axonal population is responsible for facial expressions and tongue movements, compared to muscles in the upper extremity. Sensory axons appear to be responsible for neural feedback from cutaneous mechanoreceptors to control the movement of facial muscles and the tongue. The newly discovered sympathetic axonal population in the facial nerve is hypothesized to be responsible for involuntary control of the muscle tone. These findings shed light on the pivotal role of high efferent input and rich somatosensory feedback in neuromuscular control of finely adjusted cranial systems.
Shirzadi M, Marateb HRR, McGill KCC, et al., 2023, An Accurate and Real-Time Method for Resolving Superimposed Action Potentials in MultiUnit Recordings, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 70, Pages: 378-389, ISSN: 0018-9294
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