Publications
789 results found
Yeung D, Farina D, Vujaklija I, 2019, Can Multi-DoF Training Improve Robustness of Muscle Synergy Inspired Myocontrollers?, Pages: 665-670
Non-negative Matrix Factorization (NMF) has been effective in extracting commands from surface electromyography (EMG) for the control of upper-limb prostheses. This approach enables Simultaneous and Proportional Control (SPC) over multiple degrees-of-freedom (DoFs) in a minimally supervised way. Here, like with other myoelectric approaches, robustness remains essential for clinical adoption, with device donning/doffing being a known cause for performance degradation. Previous research has demonstrated that NMF-based myocontrollers, trained on just single-DoF activations, permit a certain degree of user adaptation to a range of disturbances. In this study, we compare this traditional NMF controller with its sparsity constrained variation that allows initialization using both single and combined-DoF activations (NMF-C). The evaluation was done on 12 able bodied participants through a set of online target-reaching tests. Subjects were fitted with an 8-channel bipolar EMG setup, which was shifted by 1cm in both transversal directions throughout the experiments without system retraining. In the baseline condition NMF performed somewhat better than NMFC, but it did suffer more following the electrode repositioning, making the two perform on par. With no significant difference present across the conditions, results suggest that there is no immediate advantage from the naïve inclusion of more comprehensive training sets to the classic synergy-inspired implementation of SPC.
Yeung D, Farina D, Vujaklija I, 2019, Directional Forgetting for Stable Co-Adaptation in Myoelectric Control., Sensors (Basel), Vol: 19
Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.
Dideriksen JL, Farina D, 2019, Amplitude cancellation influences the association between frequency components in the neural drive to muscle and the rectified EMG signal, PLoS Computational Biology, Vol: 15, ISSN: 1553-734X
The rectified surface EMG signal is commonly used as an estimator of the neural drive to muscles and therefore to infer sources of synaptic input to motor neurons. Loss of EMG amplitude due to the overlap of motor unit action potentials (amplitude cancellation), however, may distort the spectrum of the rectified EMG and thereby its correlation with the neural drive. In this study, we investigated the impact of amplitude cancelation on this correlation using analytical derivations and a computational model of motor neuron activity, force, and the EMG signal. First, we demonstrated analytically that an ideal rectified EMG signal without amplitude cancellation (EMGnc) is superior to the actual rectified EMG signal as estimator of the neural drive to muscle. This observation was confirmed by the simulations, as the average coefficient of determination (r2) between the neural drive in the 1–30 Hz band and EMGnc (0.59±0.08) was matched by the correlation between the rectified EMG and the neural drive only when the level of amplitude cancellation was low (<40%) at low contraction levels (<5% of maximum voluntary contraction force; MVC). This correlation, however, decreased linearly with amplitude cancellation (r = -0.83) to values of r2 <0.2 at amplitude cancellation levels >60% (contraction levels >15% MVC). Moreover, the simulations showed that a stronger (i.e. more variable) neural drive implied a stronger correlation between the rectified EMG and the neural drive and that amplitude cancellation distorted this correlation mainly for low-frequency components (<5 Hz) of the neural drive. In conclusion, the results indicate that amplitude cancellation distorts the spectrum of the rectified EMG signal. This implies that valid use of the rectified EMG as an estimator of the neural drive requires low contraction levels and/or strong common synaptic input to the motor neurons.
Del Vecchio A, Negro F, Holobar A, et al., 2019, You are as fast as your motor neurons: speed of recruitment and maximal discharge of motor neurons determine the maximal rate of force development in humans, The Journal of Physiology, Vol: 597, Pages: 2445-2456, ISSN: 1469-7793
KEY POINTS: We propose and validate a method for accurately identifying the activity of populations of motor neurons during contractions at maximal rate of force development in humans. The behaviour of the motor neuron pool during rapid voluntary contractions in humans is presented. We show with this approach that the motor neuron recruitment speed and maximal motor unit discharge rate largely explains the individual ability in generating rapid force contractions. The results also indicate that the synaptic inputs received by the motor neurons before force is generated dictate human potential to generate force rapidly. This is the first characterization of the discharge behaviour of a representative sample of human motor neurons during rapid contractions. ABSTRACT: During rapid contractions, motor neurons are recruited in a short burst and begin to discharge at high frequencies (up to >200 Hz). In the present study, we investigated the behaviour of relatively large populations of motor neurons during rapid (explosive) contractions in humans, applying a new approach to accurately identify motor neuron activity simultaneous to measuring the rate of force development. The activity of spinal motor neurons was assessed by high-density electromyographic decomposition from the tibialis anterior muscle of 20 men during isometric explosive contractions. The speed of motor neuron recruitment and the instantaneous motor unit discharge rate were analysed as a function of the impulse (the time-force integral) and the maximal rate of force development. The peak of motor unit discharge rate occurred before force generation and discharge rates decreased thereafter. The maximal motor unit discharge rate was associated with the explosive force variables, at the whole population level (r2 = 0.71 ± 0.12; P < 0.001). Moreover, the peak motor unit discharge and maximal rate of force variables were correlated with an estimate of the suprasp
Kapelner T, Vujaklija I, Jiang N, et al., 2019, Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses., J Neuroeng Rehabil, Vol: 16
BACKGROUND: Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. METHODS: We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. RESULTS: The regression approach using neural features outperformed regression on classic global EMG features (average R2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). CONCLUSIONS: These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control.
Farina D, Aszmann O, 2019, Bionic limbs: clinical reality and academic promises., Sci Transl Med, Vol: 6, Pages: 257ps12-257ps12
Three recent articles in Science Translational Medicine (Tan et al. and Ortiz-Catalan et al., this issue; Raspopovic et al., 5 Feb 2014 issue, 222ra19) present neuroprosthetic systems in which sensory information is delivered through direct nerve stimulation while controlling an action of the prosthesis--in all three cases, arm and hand movement. We discuss such sensory-motor integration and other key issues in prosthetic reconstruction, with an emphasis on the gap existing between clinically available systems and more advanced, custom-designed academic systems. In the near future, osseointegration, implanted muscle, and nerve electrodes for decoding and stimulation may be components of prosthetic systems for clinical use, available to a large patient population.
Barsotti M, Dupan S, Vujaklija I, et al., 2019, Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications, IEEE Robotics and Automation Letters, Vol: 4, Pages: 217-223
© 2018 IEEE. A hand impairment can have a profound impact on the quality of life. This has motivated the development of dexterous prosthetic and orthotic devices. However, their control with neuromuscular interfacing remains challenging. Moreover, existing myocontrol interfaces typically require an extensive calibration. We propose a minimally supervised, online myocontrol system for proportional and simultaneous finger force estimation based on ridge regression using only individual finger tasks for training. We compare the performance of this system when using two feature sets extracted from high-density electromyography (EMG) recordings: EMG linear envelope (ENV) and non-linear EMG to muscle activation mapping (ACT). Eight intact-limb participants were tested using online target reaching tasks. On average, the subjects hit 85% ± 9% and 91% ± 11% of single finger targets with ENV and ACT features, respectively. The hit rate for combined finger targets decreased to 29% ± 16% (ENV) and 53% ± 23% (ACT). The non-linear transformation (ACT) therefore improved the performance, leading to higher completion rate and more stable control, especially for the non-trained movement classes (better generalization). These results demonstrate the feasibility of proportional multiple finger control in intact subjects by regression on non-linear EMG features with a minimal training set of single finger tasks.
Pereira HM, Schlinder-DeLap B, Keenan KG, et al., 2019, Oscillations in neural drive and age-related reductions in force steadiness with a cognitive challenge., J Appl Physiol (1985), Vol: 126, Pages: 1056-1065
A cognitive challenge when imposed during a low-force isometric contraction will exacerbate sex- and age-related decreases in force steadiness, but the mechanism is not known. We determined the role of oscillations in the common synaptic input to motor units on force steadiness during a muscle contraction with a concurrent cognitive challenge. Forty-nine young adults (19-30 yr; 25 women, 24 men) and 36 old adults (60-85 yr; 19 women, 17 men) performed a cognitive challenge (counting backward by 13) during an isometric elbow flexion task at 5% of maximal voluntary contraction. Single-motor units were decomposed from high-density surface EMG recordings. For a subgroup of participants, motor units were matched during control and cognitive challenge trials, so the same motor unit was analyzed across conditions. Reduced force steadiness was associated with greater oscillations in the synaptic input to motor units during both control and cognitive challenge trials ( r = 0.45-0.47, P < 0.01). Old adults and young women showed greater oscillations in the common synaptic input to motor units and decreased force steadiness when the cognitive challenge was imposed, but young men showed no change across conditions (session × age × sex, P < 0.05). Oscillations in the common synaptic input to motor units is a potential mechanism for altered force steadiness when a cognitive challenge is imposed during low-force contractions in young women and old adults. NEW & NOTEWORTHY We found that oscillations in the common synaptic input to motor units were associated with a reduction in force steadiness when a cognitive challenge was imposed during low-force contractions of the elbow flexor muscles in young women and old men and women but not young men. Age- and sex-related muscle weakness was associated with these changes.
Chen C, Chai G, Guo W, et al., 2019, Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses, JOURNAL OF NEURAL ENGINEERING, Vol: 16, ISSN: 1741-2560
Del Vecchio A, Casola A, Negro F, et al., 2019, The increase in muscle force after 4 weeks of strength training is mediated by adaptations in motor unit recruitment and rate coding, The Journal of Physiology, Vol: 597, Pages: 1873-1887, ISSN: 1469-7793
The strength of a muscle typically begins to increase after only a few sessions of strength training. This increase is usually attributed to changes in the neural drive to muscle as a result of adaptations at the cortical or spinal level. We investigated the change in the discharge characteristics of large populations of longitudinally tracked motor units in tibialis anterior before and after 4 weeks of strength training the ankle‐dorsiflexor muscles with isometric contractions. The adaptations exhibited by 14 individuals were compared with 14 control subjects. High‐density electromyogram grids with 128 electrodes recorded the myoelectric activity during isometric ramp contractions to the target forces of 35%, 50% and 70% of maximal voluntary force. The motor unit recruitment and derecruitment thresholds, discharge rate, interspike intervals and estimates of synaptic inputs to motor neurons were assessed. The normalized recruitment‐threshold forces of the motor units were decreased after strength training (P < 0.05). Moreover, discharge rate increased by 3.3 ± 2.5 pps (average across subjects and motor units) during the plateau phase of the submaximal isometric contractions (P < 0.001). Discharge rates at recruitment and derecruitment were not modified by training (P < 0.05). The association between force and motor unit discharge rate during the ramp‐phase of the contractions was also not altered by training (P < 0.05). These results demonstrate for the first time that the increase in muscle force after 4 weeks of strength training is the result of an increase in motor neuron output from the spinal cord to the muscle.
Roche AD, Lakey B, Mendez I, et al., 2019, Clinical Perspectives in Upper Limb Prostheses: An Update, CURRENT SURGERY REPORTS, Vol: 7, ISSN: 2167-4817
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Yao L, Sheng X, Mrachacz-Kersting N, et al., 2019, Sensory Stimulation Training for BCI System Based on Somatosensory Attentional Orientation., IEEE Trans Biomed Eng, Vol: 66, Pages: 640-646
In this study, we propose a sensory stimulation training (SST) approach to improve the performance of a brain-computer interface (BCI) based on somatosensory attentional orientation (SAO). In this BCI, subjects imagine the tactile sensation and maintain the attention on the corresponding hand as if there was a tactile stimulus on the wrist skin. Twenty BCI naïve subjects were recruited and randomly divided into a Control-Group and an SST-Group. In the Control-Group, subjects performed left hand and right hand SAO tasks in six consecutive runs (with 40 trials in each run), divided into three blocks with each having two runs. For the SST-Group, two runs included real tactile stimulation to the left or right hand (SST training block), between the first two (Pre-SST block) and the last two SAO runs (Post-SST block). Results showed that the SST-Group had a significantly improved performance of 9.4% between the last block and the first block after SST training (F(2,18) = 11.11, p = 0.0007); in contrast, no significant difference was found in the Control-Group between the first, second, and the last block (F(2,18) = 2.07, p = 0.1546), indicating no learning effect. The tactile sensation-induced oscillatory dynamics were similar to those induced by SAO. In the SST-Group, R2 discriminative information was enhanced around the somatosensory cortex due to the real sensory stimulation as compared with that in the Control-Group. Since the SAO mental task is inherently an internal process, the proposed SST method is meant as an adjuvant to SAO to facilitate subjects in achieving an initial SAO-based BCI control.
Muceli S, Poppendieck W, Hoffmann K, et al., 2019, A thin-film multichannel electrode for muscle recording and stimulation in neuroprosthetics applications, Journal of Neural Engineering, Vol: 16, ISSN: 1741-2552
Objective. We propose, design and test a novel thin-film multichannel electrode that can be used for both recording from and stimulating a muscle in acute implants. Approach. The system is built on a substrate of polyimide and contains 12 recording and three stimulation sites made of platinum. The structure is 420 µm wide, 20 µm thick and embeds the recording and stimulation contacts on the two sides of the polyimide over an approximate length of 2 cm. We show representative applications in healthy individuals as well as tremor patients. The designed system was tested by a psychometric characterization of the stimulation contacts in six tremor patients and three healthy individuals determining the perception threshold and current limit as well as the success rate in discriminating elicited sensations (electrotactile feedback). Also, we investigated the possibility of using the intramuscular electrode for reducing tremor in one patient by electrical stimulation delivered with timing based on the electromyographic activity recorded with the same electrode. Main results. In the tremor patients, the current corresponding to the perception threshold and the current limit were 0.7 ± 0.2 and 1.4 ± 0.7 mA for the wrist flexor muscles and 0.4 ± 0.2 and 1.5 ± 0.7 mA for the extensors. In one patient, closed-loop stimulation resulted in a decrease of the tremor power >50%. In healthy individuals the perception threshold and current limits were 0.9 ± 0.6 and 2.1 ± 0.6 mA for the extensor carpi radialis muscle. The subjects could distinguish four or six stimulation patterns (two or three stimulation sites × two stimulation current amplitudes) with true positive rate >80%
Mrachacz-Kersting N, Farina D, 2019, Modulation of Cortical Excitability with BCI for Stroke Rehabilitation
Here we present the possibility of inducing significant neuroplasticity as assessed by non-invasive transcranial magnetic stimulation (TMS) using a unique Brain-Computer Interface (BCI) build on known mechanisms of memory and learning. This BCI associates in time the cortical signals generated when a stroke patient attempts to perform a movement, and the artificial production of that movement. As for healthy participants, both chronic and sub-Acute patients show neuroplastic changes following exposure to this BCI, that is accompanied by significant improvements in function as assessed by clinical scales. The relatively short duration of each intervention session, the fact that it does not require user training or residual muscle activity makes this a viable tool for the clinical setting and my pave the way for future BCIs in the clinic.
Muceli S, Bergmeister KD, Hoffmann K-P, et al., 2019, Decoding motor neuron activity from epimysial thin-film electrode recordings following targeted muscle reinnervation, Journal of Neural Engineering, Vol: 16, ISSN: 1741-2552
Objective. Surface electromyography (EMG) is currently used as a control signal for active prostheses in amputees who underwent targeted muscle reinnervation (TMR) surgery. Recent research has shown that it is possible to access the spiking activity of spinal motor neurons from multi-channel surface EMG. In this study, we propose the use of multi-channel epimysial EMG electrodes as an interface for decoding motor neurons activity following TMR. Approach. We tested multi-channel epimysial electrodes (48 detection sites) built with thin-film technology in an animal model of TMR. Eight animals were tested 12 weeks after reinnervation of the biceps brachii lateral head by the ulnar nerve. We identified the position of the innervation zone and the muscle fiber conduction velocity of motor units decoded from the multi-channel epimysial recordings. Moreover, we characterized the pick-up volume by the distribution of the motor unit action potential amplitude over the epimysium surface. Main results. The electrodes provided high quality signals with average signal-to-noise ratio >30 dB across 95 identified motor units. The motor unit action potential amplitude decreased with increasing distance of the electrode from the muscle fibers (P 0.001). The decrease was more pronounced for bipolar compared to monopolar derivations. The average muscle fiber conduction velocity was 2.46 ± 0.83 m s−1. Most of the neuromuscular junctions were close to the region where the nerve was neurotized, as observed from the EMG recordings and imaging data. Significance. These results show that epimysial electrodes can be used for selective recordings of motor unit activities with a pick-up volume that included the entire muscle in the rat hindlimb. Epimysial electrodes can thus be used for detecting motor unit activity in muscles with specific fascicular territories associated to different functions following TMR surgery.
Aman M, Sporer ME, Gstoettner C, et al., 2019, Bionic hand as artificial organ: Current status and future perspectives., Artif Organs, Vol: 43, Pages: 109-118
Even though the hand comprises only 1% of our body weight, about 30% of our central nervous systems (CNS) capacity is related to its control. The loss of a hand thus presents not only the loss of the most important tool allowing us to interact with our environment, but also leaves a dramatic sensory-motor deficit that challenges our CNS. Reconstruction of hand function is therefore not only an essential part of restoring body integrity and functional wholeness but also closes the loop of our neural circuits diminishing phantom sensation and neural pain. If biology fails to restore meaningful function, today we can resort to complex mechatronic replacement that have functional capabilities that in some respects even outperform biological alternatives, such as hand transplantation. As with replantation and transplantations, the challenge of bionic replacement is connecting the target with the CNS to achieve natural and intuitive control. In recent years, we have developed a number of strategies to improve neural interfacing, signal extraction, interpretation and stable mechanical attachment that are important parts of our current research. This work gives an overview of recent advances in bionic reconstruction, surgical refinements over technological interfacing, skeletal fixation, and modern rehabilitation tools that allow quick integration of prosthetic replacement.
Bergmeister KD, Aman M, Muceli S, et al., 2019, Peripheral nerve transfers change target muscle structure and function, Science Advances, Vol: 5, Pages: 1-9, ISSN: 2375-2548
Selective nerve transfers surgically rewire motor neurons and are used in extremity reconstruction to restore muscle function or to facilitate intuitive prosthetic control. We investigated the neurophysiological effects of rewiring motor axons originating from spinal motor neuron pools into target muscles with lower innervation ratio in a rat model. Following reinnervation, the target muscle's force regenerated almost completely, with the motor unit population increasing to 116% in functional and 172% in histological assessments with subsequently smaller muscle units. Muscle fiber type populations transformed into the donor nerve's original muscles. We thus demonstrate that axons of alternative spinal origin can hyper-reinnervate target muscles without loss of muscle force regeneration, but with a donor-specific shift in muscle fiber type. These results explain the excellent clinical outcomes following nerve transfers in neuromuscular reconstruction. They indicate that reinnervated muscles can provide an accurate bioscreen to display neural information of lost body parts for high-fidelity prosthetic control.
Sartori M, Durandau G, van der Kooij H, et al., 2019, Multi-scale modelling of the human neuromuscular system for symbiotic human-machine motor interaction, Biosystems and Biorobotics, Pages: 167-170
Advances in neurophysiology are enabling understanding the neural processing underlying human movement, i.e. the recruitment of spinal motor neurons and the transmission of the resulting neural drive to the innervated muscle fibers. Similarly, advances in musculoskeletal modeling are enabling understanding movement mechanics at the level of muscle forces. However, despite detailed knowledge at the individual neural and musculoskeletal levels, our understanding of the neuro-mechanical interplay underlying movement is still limited. This paper presents recent techniques for probing the activity of spinal motor neuron pools as well as how this translates into musculoskeletal mechanical function. We then translate this in the context of robotic exoskeletons for establishing a class of human-machine interfaces that can open a window into human neuromuscular states. This represents an important step for the creation of symbiotic exoskeletons.
Dupan SSG, Vujaklija I, De Vitis G, et al., 2019, HD-EMG to Assess Motor Learning in Myoelectric Control, Biosystems and Biorobotics, Pages: 1131-1135
Online myoelectric control involves two types of adaptation: computational adaptation, in which the controller learns to associate muscle patterns with performed forces; and behavioural adaptation, where the users learn the new interface, and adapt their motor control strategies based on the errors they observe. In order to study the behavioural motor learning during online myoelectric control, twelve able-bodied participants performed single and 2-finger presses through force and myoelectric control. Myoelectric control was obtained with linear ridge regression, and was based on a training set only containing single finger presses. The distance between muscle patterns of force and EMG control trials indicated that motor learning leads to changes in neural drive, even on the trained presses. This suggests that motor learning is an integral part of myoelectric control, where the ability of the user to learn the EMG-to-force mapping impacts the overall performance of the myoelectric controller.
Stevenson AJT, Jørgensen HRM, Severinsen KE, et al., 2019, Brain State-Dependent Peripheral Nerve Stimulation for Plasticity Induction in Stroke Patients, Biosystems and Biorobotics, Pages: 1066-1070
Artificial activation of peripheral afferent fibers, with the resulting sensory feedback timed to arrive at the peak negativity of the movement-related cortical potential, induces significant increases in the excitability of cortical projections to the target muscle in healthy individuals and chronic stroke patients. In the currently ongoing study, we applied this associative brain-computer interface paradigm to sub-acute stroke patients. Compared to a sham group, where the peripheral electrical stimulation intensity was below the activation threshold of the sensory afferents, the associative intervention group displayed substantial increases in corticospinal excitability to the target muscle (tibialis anterior).
Dosen S, Patel GK, Castellini C, et al., 2019, A Novel Physiologically-Inspired Method for Myoelectric Prosthesis Control Using Pattern Classification, Biosystems and Biorobotics, Pages: 1017-1021
The contemporary myoelectric prostheses are advanced mechatronic systems, but human-machine interfacing for robust control of these devices is still an open challenge. We present a novel method for the recognition of user intention based on pattern classification which is inspired by the natural coordination of multiple muscles during hand and wrist motions. The coordinated muscle activation produces a characteristic distribution of the amplitude features of the electromyography signals, and the novel method establishes the class boundaries to capture this natural distribution. The method has been tested in healthy subjects operating a prosthesis during a challenging functional task (bottle grasping, turning and releasing). The novel approach outperformed the commonly used benchmark (linear discriminant analysis), while using shorter training and fewer features. Further developments can, therefore, lead to a method that is suitable for practical implementation and allows robust and efficient control.
Merletti R, Holobar A, Farina D, 2019, Mathematical techniques for noninvasive muscle signal analysis and interpretation, Encyclopedia of Biomedical Engineering, Pages: 95-111, ISBN: 9780128048290
The surface electromyogram (sEMG) is the summation of the signals generated by the motor units that are activated during a muscle contraction. Mathematical techniques are used to unravel the information contained in the signal to obtain physiologically relevant data concerning the central and the peripheral neuromuscular system. Five sections describe the sEMG and its applications, the principles of sEMG generation and detection, the signal conditioning techniques, the spatial and temporal properties of the signal, and the information that can be extracted from it. The contribution is mainly focused on the nature of the signal, its filtering in space and time, the extraction of amplitude and spectral features, myoelectric manifestations of muscle fatigue, estimation of muscle fiber conduction velocity, and sEMG decomposition into the constituent action potential trains. Basic knowledge of muscle electrophysiology and signal processing concepts is assumed.
Dupan SSG, Vujaklija I, Stachaczyk MK, et al., 2019, Online simultaneous myoelectric finger control, Biosystems and Biorobotics, Pages: 72-76
State-of-the-art prosthetic hands allow separate control of all digits. Restoring natural hand use with these systems requires simultaneous and proportional control of all fingers. Regression algorithms might be able to predict any combination of degrees of freedom after training them separately. However, to the best of our knowledge, this has yet to be shown online. Twelve able-bodied participants were instructed to reach predefined target forces representing either single or combined finger presses, following a system training session consisting of only individual finger presses. Myoelectric control was implemented using linear ridge regression. The results demonstrated that myoelectric control allowed participants to reach both single finger, and combination targets, with hit rates of 88% and 54% respectively. These findings suggest that simultaneous control of multiple fingers is possible, even when these movements are not included in the training set.
Mrachacz-Kersting N, Dosen S, Aliakbaryhosseinabadi S, et al., 2019, Brain-State Dependent Peripheral Nerve Stimulation for Plasticity Induction Targeting Upper-Limb, Biosystems and Biorobotics, Pages: 1061-1065
Brain-computer interfaces have increasingly found applications within the rehabilitation of lost motor function in stroke patients. Most studies have targeted upper limb muscles and used sensorimotor rhythms as the control signal. In a series of studies, we have introduced an associative BCI modeled on known theories of memory and learning that implements the movement related cortical potential (MRCP) as a way to control an external device that provides afferent generated feedback to the user’s brain at the time of the peak negative phase of the MRCP. In its application to lower limb muscles it demonstrates significant plasticity induction that requires no user training. In the current study, we tested if this associative BCI is effective when targeting upper limb muscles. Further, we explored if there is a difference when the MRCP is generated as part of a simple (wrist extension) versus a complex (reach and grasp) movement.
Sartori M, Durandau G, Dosen S, et al., 2019, Decoding phantom limb neuro-mechanical function for a new paradigm of mind-controlled bionic limbs, Biosystems and Biorobotics, Pages: 54-57
Mind controlled bionic limbs promise to replace mechanical function of lost biological extremities and restore amputees’ motor capacity. State of the art approaches use machine learning for establishing a mapping function between electromyography (EMG) and joint kinematics. However, current approaches require frequent recalibration with lack of robustness, thus providing control paradigms that are sensitive to external conditions. This paper presents an alternative method based on the authors’ recent findings. That is, a biomimetic decoder comprising a computational model that explicitly synthesizes the dynamics of the musculoskeletal system as controlled by EMG-derived neural activation signals.
Mrachacz-Kersting N, Aliakbaryhosseinabadi S, Jiang N, et al., 2019, The Efficacy of a Real-Time vs an Offline Associative Brain-Computer-Interface, Biosystems and Biorobotics, Pages: 893-896
An associative brain-computer-interface (BCI) that correlates a peripherally generated afferent volley with the peak negativity (PN) of the movement related cortical potential (MRCP) induces plastic changes in the human motor cortex. The aim of the current study was to compare the effectiveness of this intervention when the MRCP PN time is pre-determined from a training data set (BCIoffline), or detected online (BCIonline). Ten healthy participants completed both interventions in randomized order. The mean peak-to-peak motor evoked potential (MEP) amplitudes were significantly larger 30 min after (277 ± 72 µV) the BCI interventions compared to pre-intervention MEPs (233 ± 64 µV) regardless of intervention type and stimulation intensity (p = 0.029). These results provide further strong support for the associative nature of the associative BCI but also suggest that they likely differ to the associative long-term potentiation protocol they were modelled on in the exact sites of plasticity.
Murphy S, Durand M, Negro F, et al., 2019, The relationship between blood flow and motor unit firing rates in response to fatiguing exercise post-stroke, Frontiers in Physiology, Vol: 10
We quantified the relationship between the change in post-contraction blood flow with motor unit firing rates and metrics of fatigue during intermittent, sub-maximal fatiguing contractions of the knee extensor muscles after stroke. Ten chronic stroke survivors (>1-year post-stroke) and nine controls participated. Throughout fatiguing contractions, the discharge timings of individual motor units were identified by decomposition of high-density surface EMG signals. After five consecutive contractions, a blood flow measurement through the femoral artery was obtained using an ultrasound machine and probe designed for vascular measurements. There was a greater increase of motor unit firing rates from the beginning of the fatigue protocol to the end of the fatigue protocol for the control group compared to the stroke group (14.97 ± 3.78% vs. 1.99 ± 11.90%, p = 0.023). While blood flow increased with fatigue for both groups (p = 0.003), the magnitude of post-contraction blood flow was significantly greater for the control group compared to the stroke group (p = 0.004). We found that despite the lower magnitude of muscle perfusion through the femoral artery in the stroke group, blood flow has a greater impact on peripheral fatigue for the control group; however, we observed a significant correlation between change in blood flow and motor unit firing rate modulation (r2 = 0.654, p = 0.004) during fatigue in the stroke group and not the control group (r2 = 0.024, p < 0.768). Taken together, this data showed a disruption between motor unit firing rates and post-contraction blood flow in the stroke group, suggesting that there may be a disruption to common inputs to both the reticular system and the corticospinal tract. This study provides novel insights in the relationship between the hyperemic response to exercise and motor unit firing behavior for poststroke force production and may provide new approaches for recovery by improving both blood flow and musc
Mrachacz-Kersting N, Stevenson AJT, Jørgensen HRM, et al., 2019, Brain state-dependent stimulation boosts functional recovery following stroke, Annals of Neurology, Vol: 85, Pages: 84-95, ISSN: 0364-5134
OBJECTIVE: Adjuvant protocols devised to enhance motor recovery in subacute stroke patients have failed to show benefits with respect to classic therapeutic interventions. Here, we evaluate the efficacy of a novel brain state-dependent intervention based on known mechanisms of memory and learning that is integrated as part of the weekly rehabilitation program in subacute stroke patients. METHODS: Twenty-four hospitalized subacute stroke patients were randomly assigned to 2 intervention groups: (1) the associative group received 30 pairings of a peripheral electrical nerve stimulus (ES) such that the generated afferent volley arrived precisely during the most active phase of the motor cortex as patients attempted to perform a movement; and (2) in the control group, the ES intensity was too low to generate a stimulation of the nerve. Functional (including the lower extremity Fugl-Meyer assessment [LE-FM; primary outcome measure]) and neurophysiological (changes in motor evoked potentials [MEPs]) assessments were performed prior to and following the intervention period. RESULTS: The associative group significantly improved functional recovery with respect to the control group (median [interquartile range] LE-FM improvement = 6.5 [3.5-8.25] and 3 [0.75-3], respectively; p = 0.029). Significant increases in MEP amplitude were seen following all sessions in the associative group only (p ≤ 0.006). INTERPRETATION: This is the first evidence of a clinical effect of a neuromodulatory intervention in the subacute phase of stroke. This was evident with relatively few repetitions in comparison to available techniques, making it a clinically viable approach. The results indicate the potential of the proposed neuromodulation system in daily clinical routine for stroke rehabilitation.
Xu L, Negro F, Rabotti C, et al., 2019, Investigation of The Neural Drive During Vibration Exercise by High-density Surface-electromyography, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 1944-1947, ISSN: 1557-170X
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Ting J, Del Vecchio A, Friedenberg D, et al., 2019, A wearable neural interface for detecting and decoding attempted hand movements in a person with tetraplegia, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 1930-1933, ISSN: 1557-170X
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- Citations: 10
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