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Journal articleAliakbaryhosseinabadi S, Lontis R, Farina D, et al., 2021,
Effect of motor learning with different complexities on EEG spectral distribution and performance improvement
, Biomedical Signal Processing and Control, Vol: 66, ISSN: 1746-8094Motor learning can improve movement performance and behavioral measurements, such as reaction time, by inducing brain plasticity. In this study, we investigated the effect of training with different task complexity on Electroencephalographic (EEG) signals. Two types of training (‘simple’ and ‘complex’) were performed by two groups of healthy volunteers. The complex training group (CTG) performed a trace tracking task using their dominant foot and the simple training group (STG) executed repetitive ankle dorsiflexion in the training phase. Frequency analysis was performed to study the effect of training on EEG signals. In addition, the coherence between paired-channels investigated to represent changes in brain region connectivity. Results revealed that the power in the Beta (15−31 Hz) was significantly reduced while gamma band power (32−80 Hz) was significantly enhanced in the CTG compared to the STG mainly in the frontal, central and centro-parietal channels. Theta power was also increased after training in fronto-central channel. Moreover, performance variations were mainly correlated to the beta and gamma power changes. Finally, the connectivity of gamma and beta band increased significantly particularly between frontal and central region in CTG while connectivity score of theta and delta band decreased after training. These findings confirm that training-induced brain plasticity depends on the complexity of the task, more complexity.
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Conference paperTing JJ, Del Vecchio A, Verma N, et al., 2021,
Detecting and decoding spiking activity from sample populations of single motor neurons using wearable sensors
, Integrated Sensors for Biological and Neural Sensing, Publisher: SPIE, Pages: 1-6Action potentials generated by motor neurons in the brain and spinal cord carry information about intended and ongoing movement. These biopotentials are typically measured with sensors placed in close proximity to the neurons, providing a direct readout of motor output. For people that have become paralyzed due to spinal cord injury, such readouts can be used to form control signals for operating assistive devices, such as robotic arms and exoskeletons. While most methods for measuring the firing activity of single neurons rely on electrodes that are implanted in the brain, the requirement for surgery poses a barrier to widespread use. Here, we demonstrate that a wearable sensor array can detect residual motor unit activity in muscles paralyzed after severe cervical spinal cord injury. Despite generating no observable hand movement, the volitional recruitment of motor neurons below the level of injury was observed across attempted movements of individual fingers and overt wrist and elbow movements. Subgroups of motor units were coactive during flexion or extension phases of the task. Single digit movement intentions were classified offline from the EMG power (root-mean-square) or motor unit firing rates. Median classification accuracy was 76% when using the root-mean-square of the EMG and 76.5% when using motor unit firing rates. This study provides the first demonstration of a wearable interface for recording and decoding firing rates of motor neurons below the level of spinal cord injury.
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Journal articleGuo W, Ma C, Wang Z, et al., 2021,
Long exposure convolutional memory network for accurate estimation of finger kinematics from surface electromyographic signals.
, Journal of Neural Engineering, Vol: 18, Pages: 1-12, ISSN: 1741-2552OBJECTIVE: Estimation of finger kinematics is an important function of an intuitive human-machine interface, such as gesture recognition. Here, we propose a novel deep learning method, named Long Exposure Convolutional Memory Network (LE-ConvMN), and use it to proportionally estimate finger joint angles through surface electromyographic (sEMG) signals. APPROACH: We use a convolution structure to replace the neuron structure of traditional Long Short-Term Memory (LSTM) networks, and use the long exposure data structure which retains the spatial and temporal information of the electrodes as input. The Ninapro database, which contains continuous finger gestures and corresponding sEMG signals was used to verify the efficiency of the proposed deep learning method. The proposed method was compared with LSTM and Sparse Pseudo-input Gaussian Process (SPGP) on this database to predict the 10 main joint angles on the hand based on sEMG. The correlation coefficient (CC) was evaluated using the three methods on eight healthy subjects, and all the methods adopted the root mean square (RMS) features. MAIN RESULTS: The experimental results showed that the average CC, RMSE, NRMSE of the proposed LE-ConvMN method (0.82±0.03,11.54±1.89,0.12±0.013) was significantly higher than SPGP (0.65±0.05, p＜0.001; 15.51±2.82, p＜0.001; 0.16±0.01, p＜0.001) and LSTM (0.64±0.06, p＜0.001; 14.77±3.21, p＜0.001; 0.15±0.02, p=＜0.001). Furthermore, the proposed real-time-estimation method has a computation cost of only approximately 82 ms to output one state of ten joints (average value of 10 tests on TitanV GPU). SIGNIFICANCE: The proposed LE-ConvMN method could efficiently estimate the continuous movement of fingers with sEMG, and its performance is significantly superior to two established deep learning methods.
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Journal articleEnoka RM, Farina D, 2021,
Force Steadiness: From Motor Units to Voluntary Actions.
, Physiology (Bethesda), Vol: 36, Pages: 114-130Voluntary actions are controlled by the synaptic inputs that are shared by pools of spinal motor neurons. The slow common oscillations in the discharge times of motor units due to these synaptic inputs are strongly correlated with the fluctuations in force during submaximal isometric contractions (force steadiness) and moderately associated with performance scores on some tests of motor function. However, there are key gaps in knowledge that limit the interpretation of differences in force steadiness.
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Journal articleTing JE, Vecchio AD, Sarma D, et al., 2021,
Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array
<jats:title>Abstract</jats:title> <jats:p>Motor neurons in the brain and spinal cord convey information about motor intent that can be extracted and interpreted to control assistive devices, such as computers, wheelchairs, and robotic manipulators. However, most methods for measuring the firing activity of single neurons rely on implanted microelectrodes. Although intracortical brain-computer interfaces (BCIs) have been shown to be safe and effective, the requirement for surgery poses a barrier to widespread use. Here, we demonstrate that a wearable sensor array can detect residual motor unit activity in paralyzed muscles after severe cervical spinal cord injury (SCI). Despite generating no observable hand movement, volitional recruitment of motor neurons below the level of injury was observed across attempted movements of individual fingers and overt wrist and elbow movements. Subgroups of motor units were coactive during flexion or extension phases of the task. Single digit movement intentions were classified offline from the EMG power (RMS) or motor unit firing rates with median classification accuracies >75% in both cases. Simulated online control of a virtual hand was performed with a binary classifier to test feasibility of real time extraction and decoding of motor units. The online decomposition algorithm extracted motor units in 1.2 ms, and the firing rates predicted the correct digit motion 88 ± 24% of the time. This study provides the first demonstration of a wearable interface for recording and decoding firing rates of motor neurons below the level of injury in a person with tetraplegia after motor complete SCI.</jats:p> <jats:sec> <jats:title>Significance Statement</jats:title> <jats:p>A wearable electrode array and machine learning methods were used to record and decode myoelectric signals and motor unit firing in paralyzed musc
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Journal articleHug F, Del Vecchio A, Avrillon S, et al., 2021,
Muscles from the same muscle group do not necessarily share common drive: evidence from the human triceps surae.
, Journal of applied physiology (Bethesda, Md. : 1985), Vol: 130, Pages: 342-354, ISSN: 1522-1601It has been proposed that movements are produced through groups of muscles, or motor modules, activated by common neural commands. However, the neural origin of motor modules is still debated. Here, we used complementary approaches to determine: 1) whether three muscles of the same muscle group [soleus, gastrocnemius medialis (GM), and gastrocnemius lateralis (GL)] are activated by a common neural drive, and 2) whether the neural drive to GM and GL could be differentially modified by altering the mechanical requirements of the task. Eighteen human participants performed an isometric standing heel raise and submaximal isometric plantarflexions (10%, 30%, and 50% of maximal effort). High-density surface electromyography recordings were decomposed into motor unit action potentials and coherence analysis was applied on the motor unit spike trains. We identified strong common drive to each muscle but minimal common drive between the muscles. Further, large between-muscle differences were observed during the isometric plantarflexions, such as a delayed recruitment time of GL compared with GM and soleus motor units and opposite time-dependent changes in the estimates of neural drive to muscles during the torque plateau. Finally, the feet position adopted during the heel-raise task (neutral vs. internally rotated) affected only the GL neural drive with no change for GM. These results provide conclusive evidence that not all anatomically defined synergist muscles are controlled by strong common neural drive. Independent drive to some muscles from the same muscle group may allow for more flexible control to comply with secondary goals such as joint stabilization.NEW & NOTEWORTHY In this study, we demonstrated that the three muscles composing the human triceps surae share minimal common drive during isometric contractions. Our results suggest that reducing the number of effectively controlled degrees of freedom may not always be the strategy used by the central nervous sys
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Journal articleBraecklein M, Ibanez J, Barsakcioglu DY, et al., 2021,
Towards human motor augmentation by voluntary decoupling beta activity in the neural drive to muscle and force production
, Journal of Neural Engineering, Vol: 18, ISSN: 1741-2560Objective. Effective human motor augmentation should rely on biological signals that can be volitionally modulated without compromising natural motor control. Approach. We provided human subjects with real-time information on the power of two separate spectral bands of the spiking activity of motor neurons innervating the tibialis anterior muscle: the low-frequency band (<7 Hz), which is directly translated into natural force control, and the beta band (13–30 Hz), which is outside the dynamics of the neuromuscular system. Main Results. Subjects could gain control over the powers in these two bands to navigate a cursor towards specific targets in a 2D space (experiment 1) and to up- and down-modulate beta activity while keeping steady force contractions (experiment 2). Significance. Results indicate that beta projections to the spinal motor neuron pool can be voluntarily controlled partially decoupled from natural muscle contractions and, therefore, they could be valid control signals for implementing effective human motor augmentation platforms.
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Journal articleClarke AK, Atashzar SF, Vecchio AD, et al., 2021,
Deep learning for robust decomposition of high-density surface EMG signals
, IEEE Transactions on Biomedical Engineering, Vol: 68, Pages: 526-534, ISSN: 0018-9294Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the separation matrix is blindly estimated on a signal interval, it is also possible to apply the same matrix to subsequent signal segments. Nonetheless, the trained separation matrices are sub-optimal in noisy conditions and require that incoming data undergo computationally expensive whitening. One unexplored alternative is to instead use the paired HD-sEMG signal and BSS output to train a model to predict MU activations within a supervised learning framework. A gated recurrent unit (GRU) network was trained to decompose both simulated and experimental unwhitened HD-sEMG signal using the output of the gCKC algorithm. The results on the experimental data were validated by comparison with the decomposition of concurrently recorded intramuscular EMG signals. The GRU network outperformed gCKC at low signal-to-noise ratios, proving superior performance in generalising to new data. Using 12 seconds of experimental data per recording, the GRU performed similarly to gCKC, at rates of agreement of 92.5% (84.5%-97.5%) and 94.9% (88.8%-100.0%) respectively for GRU and gCKC against matched intramuscular sources.
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Journal articleAvrillon S, Del Vecchio A, Farina D, et al., 2021,
Individual differences in the neural strategies to control the lateral and medial head of the quadriceps during a mechanically constrained task.
, Journal of applied physiology (Bethesda, Md. : 1985), Vol: 130, Pages: 269-281, ISSN: 1522-1601The interindividual variability in the neural drive sent from the spinal cord to muscles is largely unknown, even during highly constrained motor tasks. Here, we investigated individual differences in the strength of neural drive received by the vastus lateralis (VL) and vastus medialis (VM) during an isometric task. We also assessed the proportion of common neural drive within and between these muscles. Twenty-two participants performed a series of submaximal isometric knee extensions at 25% of their peak torque. High-density surface electromyography recordings were decomposed into motor unit action potentials. Coherence analyses were applied on the motor unit spike trains to assess the degree of neural drive that was shared between motor neurons. Six participants were retested ∼20 mo after the first session. The distribution of the strength of neural drive between VL and VM varied between participants and was correlated with the distribution of normalized interference electromyography (EMG) signals (r > 0.56). The level of within- and between-muscle coherence varied across individuals, with a significant positive correlation between these two outcomes (VL: r = 0.48; VM: r = 0.58). We also observed a large interindividual variability in the proportion of muscle-specific drive, that is, the drive unique to each muscle (VL range: 6%-83%, VM range: 6%-86%). All the outcome measures were robust across sessions, providing evidence that the individual differences did not depend solely on the variability of the measures. Together, these results demonstrate that the neural strategies to control the VL and VM muscles widely vary across individuals, even during a constrained task.NEW & NOTEWORTHY We observed that the distribution of the strength of neural drive between the vastus lateralis and vastus medialis during a single-joint isometric task varied across participants. Also, we observed that the proportion of ne
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Conference paperUrh F, Strnad D, Clarke A, et al., 2021,
On the Need for Spatial Whitening of High-Density Surface Electromyograms in Motor Unit Identification by Neural Networks
, Pages: 915-922, ISSN: 1680-0737In recent years, many new decomposition methods for identification of motor unit (MU) firings from high-density surface electromyograms (HDEMG) have been developed, with recent attempts focused on the use of different neural networks (NN). In this study, we evaluated the need for HDEMG signal whitening in NN-based MU identification. For this purpose, we analyzed the learning efficiency of two different types of NN, namely dense NN and long short-term memory (LSTM) NN, on the same HDEMG signals, with and without spatial whitening applied to them. All the HDEMG signals used were simulated with advanced HDEMG simulator, providing a full control of MU firing patterns and MU characteristics in our test environment. Spatial whitening of HDEMG signals significantly improved the precision of MU identification, regardless of the type of NN tested. For dense NN, precision of identified MU increased from 32.2 ± 20.2% to 93.1 ± 8.7%, whereas miss rate decreased from 48.4 ± 23.9% to 12.0 ± 13.3% when whitening of HDEMG signals was employed. For LSTM NN the precision of MU identification increased from 59.7 ± 19.7% to 99.4 ± 2.0% whereas miss rate decreased from 43.1 ± 22.3% to 12.7 ± 9.7% with whitening.
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Journal articleRahimian E, Zabihi S, Asif A, et al., 2021,
FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography
, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 29, Pages: 1004-1015, ISSN: 1534-4320- Author Web Link
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- Citations: 85
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Journal articleVeselic S, Zito C, Farina D, 2021,
Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control.
, Front Neurorobot, Vol: 15, ISSN: 1662-5218Physical human-robot interaction (pHRI) enables a user to interact with a physical robotic device to advance beyond the current capabilities of high-payload and high-precision industrial robots. This paradigm opens up novel applications where a the cognitive capability of a user is combined with the precision and strength of robots. Yet, current pHRI interfaces suffer from low take-up and a high cognitive burden for the user. We propose a novel framework that robustly and efficiently assists users by reacting proactively to their commands. The key insight is to include context- and user-awareness in the controller, improving decision-making on how to assist the user. Context-awareness is achieved by inferring the candidate objects to be grasped in a task or scene and automatically computing plans for reaching them. User-awareness is implemented by facilitating the motion toward the most likely object that the user wants to grasp, as well as dynamically recovering from incorrect predictions. Experimental results in a virtual environment of two degrees of freedom control show the capability of this approach to outperform manual control. By robustly predicting user intention, the proposed controller allows subjects to achieve superhuman performance in terms of accuracy and, thereby, usability.
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Journal articleTottrup L, Atashzar SF, Farina D, et al., 2020,
Nerve injury decreases hyperacute resting-state connectivity between the anterior cingulate and primary somatosensory cortex in anesthetized rats
, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 28, Pages: 2691-2698, ISSN: 1534-4320A better understanding of neural pain processing and of the development of pain over time, is critical to identify objective measures of pain and to evaluate the effect of pain alleviation therapies. One issue is, that the brain areas known to be related to pain processing are not exclusively responding to painful stimuli, and the neuronal activity is also influenced by other brain areas. Functional connectivity reflects synchrony or covariation of activation between groups of neurons. Previous studies found changes in connectivity days or weeks after pain induction. However, less in known on the temporal development of pain. Our objective was therefore to investigate the interaction between the anterior cingulate cortex (ACC) and primary somatosensory cortex (SI) in the hyperacute (minute) and sustained (hours) response in an animal model of neuropathic pain. Intra-cortical local field potentials (LFP) were recorded in 18 rats. In 10 rats the spared nerve injury model was used as an intervention. The intra-cortical activity was recorded before, immediately after, and three hours after the intervention. The interaction was quantified as the calculated correlation and coherence. The results from the intervention group showed a decrease in correlation between ACC and SI activity, which was most pronounced in the hyperacute phase but a longer time frame may be required for plastic changes to occur. This indicated that both SI and ACC are involved in hyperacute pain processing.
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Journal articleDel Vecchio A, Sylos-Labini F, Mondi V, et al., 2020,
Spinal motoneurons of the human newborn are highly synchronized during leg movements
, Science Advances, Vol: 6, ISSN: 2375-2548Motoneurons of neonatal rodents show synchronous activity that modulates the development of the neuromuscular system. However, the characteristics of the activity of human neonatal motoneurons are largely unknown. Using a noninvasive neural interface, we identified the discharge timings of individual spinal motoneurons in human newborns. We found highly synchronized activities of motoneurons of the tibialis anterior muscle, which were associated with fast leg movements. Although neonates’ motor units exhibited discharge rates similar to those of adults, their synchronization was significantly greater than in adults. Moreover, neonatal motor units showed coherent oscillations in the delta band, which is directly translated into force generation. These results suggest that motoneuron synchronization in human neonates might be an important mechanism for controlling fast limb movements, such as those of primitive reflexes. In addition to help revealing mechanisms of development, the proposed neural interface might monitor children at risk of developing motor disorders.
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Journal articleGardner M, Mancero Castillo C, Wilson S, et al., 2020,
A multimodal intention detection sensor suite for shared autonomy of upper-limb robotic prostheses
, Sensors, Vol: 20, ISSN: 1424-8220Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.
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Journal articleMartinez-Valdes E, Negro F, Falla D, et al., 2020,
Inability to increase the neural drive to muscle is associated with task failure during submaximal contractions.
, J Neurophysiol, Vol: 124, Pages: 1110-1121We investigated changes in motor unit (MU) behavior and vasti-muscle contractile properties during sustained submaximal fatiguing contractions with a new time-domain tracking technique to understand the mechanisms responsible for task failure. Sixteen participants performed a nonfatiguing 15-s isometric knee extension at 50% of the maximum voluntary (MVC) torque, followed by a 30% MVC sustained contraction until exhaustion. Two grids of 64 surface electromyography electrodes were placed over vastus medialis and lateralis. Signals were decomposed into MU discharge times and the MUs from the 30% MVC sustained contraction were followed until task failure by overlapping decomposition intervals. These MUs were then tracked between 50% and 30% MVC. During the sustained fatiguing contraction, MUs of the two muscles decreased their discharge rate until ∼40% of the endurance time, referred to as the reversal time, and then increased their discharge rate until task failure. This reversal in firing behavior predicted total endurance time and was matched by opposite changes in twitch force (increase followed by a decrease). Despite the later increase in MU firing rates, peak discharge rates at task failure did not reach the frequency attained during a nonfatiguing 50% MVC contraction. These results show that changes in MU firing properties are influenced by adjustments in contractile properties during the course of the contraction, allowing the identification of two phases. Nevertheless, the contraction cannot be sustained, possibly because of progressive motoneuron inhibition/decreased excitability, as the later increase in firing rate saturates at a much lower frequency compared with a higher-force nonfatiguing contraction.NEW & NOTEWORTHY Motor unit firing and contractile properties during a submaximal contraction until failure were assessed with a new tracking technique. Two distinct phases in firing behavior were observed, which compensated for changes in twitch ar
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Journal articleNuccio S, Del Vecchio A, Casolo A, et al., 2020,
Muscle fiber conduction velocity in the vastus lateralis and medialis muscles of soccer players after ACL reconstruction
, Scandinavian Journal of Medicine and Science in Sports, Vol: 30, Pages: 1976-1984, ISSN: 0905-7188The neural factors underlying the persistency of quadriceps weakness after anterior cruciate ligament reconstruction (ACLR) have been only partially explained. This study examined muscle fiber conduction velocity (MFCV) as an indirect parameter of motor unit recruitment strategies in the vastus lateralis (VL) and medialis (VM) muscles of soccer players with ACLR. High‐density surface electromyography (HDsEMG) was acquired from VL and VM in nine soccer players (22.7 ± 2.9 years; BMI: 22.08 ± 1.72 kg·m−2; 7.7 ± 2.2 months post‐surgery). Voluntary muscle force and the relative myoelectrical activity from the reconstructed and contralateral sides were recorded during linearly increasing isometric knee extension contractions up to 70% of maximal voluntary isometric force (MVIF). The relation of MFCV and force was examined by linear regression analysis at the individual subject level. The initial (intercept), peak (MFCV70), and rate of change (slope) of MFCV related to force were compared between limbs and muscles. The MVIF was lower in the reconstructed side than in the contralateral side (−%20.5; P < .05). MFCV intercept was similar among limbs and muscles (P > .05). MFCV70 and MFCV slope were lower in the reconstructed side compared to the contralateral for both VL (−28.5% and −10.1%, respectively; P < .001) and VM (−22.6% and −8.1%, respectively; P < .001). The slope of MFCV was lower in the VL than VM, but only in the reconstructed side (−12.4%; P < .001). These results suggest possible impairments in recruitment strategies of high‐threshold motor units (HTMUs) as well as deficits in sarcolemmal excitability, fiber diameter, and discharge rate of knee extensor muscles following ACLR.
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Journal articleAtashzar SF, Huang H-Y, Duca FD, et al., 2020,
Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction
, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 5, Pages: 5953-5960, ISSN: 2377-3766- Cite
- Citations: 13
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Journal articlePascual Valdunciel A, Gonzalez-Sanchez M, Muceli S, et al., 2020,
Intramuscular stimulation of muscle afferents attains prolonged tremor reduction in essential tremor patients.
, IEEE Transactions on Biomedical Engineering, Vol: PP, ISSN: 0018-9294This study proposes and clinically tests intramuscular electrical stimulation below motor threshold to achieve prolonged reduction of wrist flexion/extension tremor in Essential Tremor (ET) patients. The developed system consisted of an intramuscular thin-film electrode structure that included both stimulation and electromyography (EMG) recording electrodes, and a control algorithm for the timing of intramuscular stimulation based on EMG (closed-loop stimulation). Data were recorded from nine ET patients with wrist flexion/extension tremor recruited from the Gregorio Maran Hospital (Madrid, Spain). Patients participated in two experimental sessions comprising: 1) sensory stimulation of wrist flexors/extensors via thin-film multichannel intramuscular electrodes; and 2) surface stimulation of the nerves innervating the same target muscles. For each session, four of these patients underwent random 60-s trials of two stimulation strategies for each target muscle: 1) selective and adaptive timely stimulation (SATS) - based on EMG of the antagonist muscle; and 2) continuous stimulation (CON) of target muscles. Two patients underwent SATS stimulation trials alone while the other three underwent CON stimulation trials alone in each session. Kinematics of wrist, elbow, and shoulder, together with clinical scales, were used to assess tremor before, right after, and 24 h after each session. Intramuscular SATS achieved, on average, 32% acute (during stimulation) tremor reduction on each trial, while continuous stimulation augmented tremorgenic activity. Furthermore, tremor reduction was significantly higher using intramuscular than surface stimulation. Prolonged reduction of tremor amplitude (24 h after the experiment) was observed in four patients. These results showed acute and prolonged (24 h) tremor reduction using a minimally invasive neurostimulation technology based on SATS of primary sensory afferents of wrist muscles. This strategy might open the possibility of an alte
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Journal articleTanzarella S, Muceli S, Del Vecchio A, et al., 2020,
Non-invasive analysis of motor neurons controlling the intrinsic and extrinsic muscles of the hand
, Journal of Neural Engineering, Vol: 17, ISSN: 1741-2552OBJECTIVE: We present a non-invasive framework for investigating efferent commands to 14 extrinsic and intrinsic hand muscles. We extend previous studies (limited to a few muscles) on common synaptic input among pools of motor neurons in a large number of muscles. APPROACH: Seven subjects performed sinusoidal isometric contractions to complete seven types of grasps, with each finger and with three combinations of fingers in opposition with the thumb. High-density surface EMG (HD-sEMG) signals (384 channels in total) recorded from the 14 muscles were decomposed into the constituent motor unit action potentials. This provided a non-invasive framework for the investigation of motor neuron discharge patterns, muscle coordination and efferent commands of the hand muscles during grasping. Moreover, during grasping tasks, it was possible to identify common neural information among pools of motor neurons innervating the investigated muscles. For this purpose, principal component analysis (PCA) was applied to the smoothed discharge rates of the decoded motor units. MAIN RESULTS: We found that the first principal component (PC1) of the ensemble of decoded motor neuron spike trains explained a variance of (53.8 ± 10.5) % and was positively correlated with force (R=0.67 ± 0.01 across all subjects and tasks). By grouping the pools of motor neurons from extrinsic or intrinsic muscles, the PC1 explained a proportion of variance of (57.3 ± 10.8) % and (57.9 ± 11.8) %, respectively, and was correlated with force with R=0.61 ± 0.13 and 0.64 ± 0.12, respectively. SIGNIFICANCE: These observations demonstrate a low dimensional control of motor neurons across multiple muscles that can be exploited for extracting control signals in neural interfacing. The proposed framework was designed for hand rehabilitation perspectives, such as post-stroke rehabilitation and hand-exoskeleton control.
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