Publications
789 results found
Jiang X, Liu X, Fan J, et al., 2021, Enhancing IoT security via cancelable HD-sEMG-based biometric authentication password, encoded by gesture, IEEE Internet of Things Journal, Vol: 8, Pages: 16535-16547, ISSN: 2327-4662
Enhancing information security via reliable user authentication in wireless body area network (WBAN)-based Internet of Things (IoT) applications has attracted increasing attention. The noncancelability of traditional biometrics (e.g. fingerprint) for user authentication increases the privacy disclosure risks once the biometric template is exposed, because users cannot volitionally create a new template. In this work, we propose a cancelable biometric modality based on high-density surface electromyogram (HD-sEMG) encoded by hand gesture password, for user authentication. HD-sEMG signals (256 channels) were acquired from the forearm muscles when users performed a prescribed gesture password, forming their biometric token. Thirty four alternative hand gestures in common daily use were studied. Moreover, to reduce the data acquisition and transmission burden in IoT devices, an automatically generated password-specific channel mask was employed to reduce the number of active channels. HD-sEMG biometrics were also robust with reduced sampling rate, further reducing power consumption. HD-sEMG biometrics achieved a low equal error rate (EER) of 0.0013 when impostors entered a wrong gesture password, as validated on 20 subjects. Even if impostors entered the correct gesture password, the HD-sEMG biometrics still achieved an EER of 0.0273. If the HD-sEMG biometric template was exposed, users could cancel it by simply changing it to a new gesture password, with an EER of 0.0013. To the best of our knowledge, this is the first study to employ HD-sEMG signals under common daily hand gestures as biometric tokens, with training and testing data acquired on different days.
Nuccio S, Del Vecchio A, Casolo A, et al., 2021, Deficit in knee extension strength following anterior cruciate ligament reconstruction is explained by a reduced neural drive to the vasti muscles, JOURNAL OF PHYSIOLOGY-LONDON, Vol: 599, Pages: 5103-5120, ISSN: 0022-3751
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- Citations: 20
Casolo A, Del Vecchio A, Balshaw TG, et al., 2021, Behavior of motor units during submaximal isometric contractions in chronically strength-trained individuals, JOURNAL OF APPLIED PHYSIOLOGY, Vol: 131, Pages: 1584-1598, ISSN: 8750-7587
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- Citations: 8
Sturma A, Hruby LA, Boesendorfer A, et al., 2021, Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers, Jove-Journal of Visualized Experiments, ISSN: 1940-087X
Targeted Muscle Reinnervation (TMR) improves the biological control interface for myoelectric prostheses after above-elbow amputation. Selective activation of muscle units is made possible by surgically re-routing nerves, yielding a high number of independent myoelectric control signals. However, this intervention requires careful patient selection and specific rehabilitation therapy. Here a rehabilitation protocol is presented for high-level upper limb amputees undergoing TMR, based on an expert Delphi study. Interventions before surgery include detailed patient assessment and general measures for pain control, muscle endurance and strength, balance, and range of motion of the remaining joints. After surgery, additional therapeutic interventions focus on edema control and scar treatment and the selective activation of cortical areas responsible for upper limb control. Following successful reinnervation of target muscles, surface electromyographic (sEMG) biofeedback is used to train the activation of the novel muscular units. Later on, a table-top prosthesis may provide the first experience of prosthetic control. After fitting the actual prosthesis, training includes repetitive drills without objects, object manipulation, and finally, activities of daily living. Ultimately, regular patient appointments and functional assessments allow tracking prosthetic function and enabling early interventions if malfunctioning.
Martinez-Valdes E, Negro F, Arvanitidis M, et al., 2021, Pain-induced changes in motor unit discharge depend on recruitment threshold and contraction speed., J Appl Physiol (1985), Vol: 131, Pages: 1260-1271
At high forces, the discharge rates of lower- and higher-threshold motor units (MU) are influenced in a different way by muscle pain. These differential effects may be particularly important for performing contractions at different speeds since the proportion of lower- and higher-threshold MUs recruited varies with contraction velocity. We investigated whether MU discharge and recruitment strategies are differentially affected by pain depending on their recruitment threshold (RT), across a range of contraction speeds. Participants performed ankle dorsiflexion sinusoidal-isometric contractions at two frequencies (0.25 and 1 Hz) and two modulation amplitudes [5% and 10% of the maximum voluntary contraction (MVC)] with a mean target torque of 20%MVC. High-density surface electromyography recordings from the tibialis anterior muscle were decomposed and the same MUs were tracked across painful (hypertonic saline injection) and nonpainful conditions. Torque variability, mean discharge rate (MDR), DR variability (DRvar), RT, and the delay between the cumulative spike train and the resultant torque output (neuromechanical delay, NMD) were assessed. The average RT was greater at faster contraction velocities (P = 0.01) but was not affected by pain. At the fastest contraction speed, torque variability and DRvar were reduced (P < 0.05) and MDR was maintained. Conversely, MDR decreased and DRvar and NMD increased significantly during pain at slow contraction speeds (P < 0.05). These results show that reductions in contraction amplitude and increased recruitment of higher-threshold MUs at fast contraction speeds appear to compensate for the inhibitory effect of nociceptive inputs on lower-threshold MUs, allowing the exertion of fast submaximal contractions during pain.NEW & NOTEWORTHY Pain induces changes in motor performance, motor unit recruitment, and rate coding behavior that varies across different contraction speeds. Here we show that that pain reduces motor unit
Luft M, Klepetko J, Muceli S, et al., 2021, Proof of concept for multiple nerve transfers to a single target muscle, eLife, Vol: 10, Pages: 1-16, ISSN: 2050-084X
Surgical nerve transfers are used to efficiently treat peripheral nerve injuries, neuromas,phantom limb pain, or improve bionic prosthetic control. Commonly, one donor nerve is transferredto one target muscle. However, the transfer of multiple nerves onto a single target muscle mayincrease the number of muscle signals for myoelectric prosthetic control and facilitate the treatmentof multiple neuromas. Currently, no experimental models are available. This study describes anovel experimental model to investigate the neurophysiological effects of peripheral double nervetransfers to a common target muscle. In 62 male Sprague-Dawleyrats, the ulnar nerve of the antebrachiumalone (n=30) or together with the anterior interosseus nerve (n=32) was transferred to reinnervatethe long head of the biceps brachii. Before neurotization, the motor branch to the biceps’long head was transected at the motor entry point. Twelve weeks after surgery, muscle responseto neurotomy, behavioral testing, retrograde labeling, and structural analyses were performed toassess reinnervation. These analyses indicated that all nerves successfully reinnervated the targetmuscle. No aberrant reinnervation was observed by the originally innervating nerve. Our observationssuggest a minimal burden for the animal with no signs of functional deficit in daily activities orauto-mutilationin both procedures. Furthermore, standard neurophysiological analyses for nerveand muscle regeneration were applicable. This newly developed nerve transfer model allows for thereliable and standardized investigation of neural and functional changes following the transfer ofmultiple donor nerves to one target muscle.
Aliakbaryhosseinabadi S, Dosen S, Savic AM, et al., 2021, Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis, JOURNAL OF NEURAL ENGINEERING, Vol: 18, ISSN: 1741-2560
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- Citations: 4
Chen C, Yu Y, Sheng X, et al., 2021, Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time, JOURNAL OF NEURAL ENGINEERING, Vol: 18, ISSN: 1741-2560
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- Citations: 22
Veselic S, Zito C, Farina D, 2021, Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control, Frontiers in Neurorobotics, Vol: 15
Physical 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.
Ibáñez J, Angeli CA, Harkema SJ, et al., 2021, Recruitment order of motor neurons promoted by epidural stimulation in individuals with spinal cord injury., Journal of Applied Physiology, Vol: 131, Pages: 1100-1110, ISSN: 1522-1601
Spinal cord epidural stimulation (scES) combined with activity-based training can promote motor function recovery in individuals with motor complete spinal cord injury (SCI). The characteristics of motor neuron recruitment, which influence different aspects of motor control, are still unknown when motor function is promoted by scES. Here, we enrolled five individuals with chronic motor complete SCI implanted with an scES unit to study the recruitment order of motor neurons during standing enabled by scES. We recorded high-density electromyography (HD-EMG) signals on the vastus lateralis muscle and inferred the order of recruitment of motor neurons from the relation between amplitude and conduction velocity of the scES-evoked EMG responses along the muscle fibers. Conduction velocity of scES-evoked responses was modulated over time, whereas stimulation parameters and standing condition remained constant, with average values ranging between 3.0 ± 0.1 and 4.4 ± 0.3 m/s. We found that the human spinal circuitry receiving epidural stimulation can promote both orderly (according to motor neuron size) and inverse trends of motor neuron recruitment, and that the engagement of spinal networks promoting rhythmic activity may favor orderly recruitment trends. Conversely, the different recruitment trends did not appear to be related with time since injury or scES implant, nor to the ability to achieve independent knees extension, nor to the conduction velocity values. The proposed approach can be implemented to investigate the effects of stimulation parameters and training-induced neural plasticity on the characteristics of motor neuron recruitment order, contributing to improve mechanistic understanding and effectiveness of epidural stimulation-promoted motor recovery after SCI.NEW & NOTEWORTHY After motor complete spinal cord injury, the human spinal cord receiving epidural stimulation can promote both orderly and inverse trends o
Free D, Syndergaard I, Pigg A, et al., 2021, Intermuscular Coherence Within the Upper Limb in Persons With Essential Tremor, Publisher: WILEY, Pages: S588-S588, ISSN: 0885-3185
Tanzarella S, Muceli S, Santello M, et al., 2021, Synergistic Organization of Neural Inputs from Spinal Motor Neurons to Extrinsic and Intrinsic Hand Muscles, JOURNAL OF NEUROSCIENCE, Vol: 41, Pages: 6878-6891, ISSN: 0270-6474
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- Citations: 17
Caillet AHD, Phillips ATM, Farina D, et al., 2021, Mathematical Relationships between Spinal Motoneuron Properties, Publisher: Cold Spring Harbor Laboratory
<jats:p>Our understanding of the behaviour of alpha-motoneurons (MNs) in mammals partly relies on our knowledge of the relationships between MN membrane properties, such as MN size, resistance, rheobase, capacitance, time constant, axonal conduction velocity and afterhyperpolarization period. Based on scattered but converging evidence, current experimental studies and review papers qualitatively assumed that some of these MN properties are related. In this systematic meta-analysis, we reprocessed the data from 40 experimental studies in adult cat, rat and mouse MN preparations in vivo to empirically derive mathematical relations between experimentally measured MN electrophysiological and anatomical properties. These mathematical relationships provide the first validated framework of MN-specific morphometric and electrophysiological properties that reproduce published experimental mammal data in vivo. These relationships moreover support the classic description of a MN as a membrane equivalent electrical circuit and describe for the first time the association between MN size and MN membrane capacitance and time constant. The obtained relationships finally indicate that motor units are recruited in order of increasing MN size, muscle unit size, MN rheobase, unit force recruitment thresholds and tetanic forces, but underlines that MN size and recruitment order may not be related to motor unit type.</jats:p>
Bräcklein M, Ibáñez J, Barsakcioglu DY, et al., 2021, The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal
<jats:title>Abstract</jats:title><jats:p>Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces provide a promising basis for human motor augmentation by extracting potential high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurones to effectively increase the number of degrees-of-freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N=7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU control based on independent synaptic inputs to single MUs is not a simple to learn control strategy.</jats:p>
Germer CM, Farina D, Elias LA, et al., 2021, Surface EMG crosstalk quantified at the motor unit population level for muscles of the hand, thigh, and calf, Journal of Applied Physiology, Vol: 131, Pages: 808-820, ISSN: 1522-1601
Crosstalk is an important source of error in interpreting surface electromyography (EMG) signals. Here, we aimed at characterizing crosstalk for three groups of synergistic muscles by the identification of individual motor unit action potentials. Moreover, we explored whether spatial filtering (single and double differential) of the EMG signals influences the level of crosstalk. Three experiments were conducted. Participants (total twenty-five) performed isometric contractions at 10% of the maximal voluntary contraction (MVC) with digit muscles and knee extensors, and at 30% MVC with plantar flexors. High-density surface EMG signals were recorded and decomposed into motor unit spike trains. For each muscle, we quantified the crosstalk induced to neighboring muscles and the level of contamination by the nearby muscle activity. We also estimated the influence of crosstalk on the EMG power spectrum and intermuscular correlation. Most motor units (80%) generated significant crosstalk signals to neighboring muscle EMG in monopolar recording mode, but this proportion decreased with spatial filtering (50% and 42% for single and double differential, respectively). Crosstalk induced overestimations of intermuscular correlation and has a small effect on the EMG power spectrum, which indicates that crosstalk is not reduced with high-pass temporal filtering. Conversely, spatial filtering diminished the crosstalk magnitude and the overestimations of intermuscular correlation, confirming to be an effective and simple technique to reduce crosstalk. This paper presents a new method for the identification and quantification of crosstalk at the motor unit level and clarifies the influence of crosstalk on EMG interpretation for muscles with different anatomy.
Mouchoux J, Carisi S, Dosen S, et al., 2021, Artificial perception and semiautonomous control in myoelectric hand prostheses increases performance and decreases effort, IEEE Transactions on Robotics, Vol: 37, Pages: 1298-1312, ISSN: 1552-3098
Dexterous control of upper limb prostheses with multiarticulated wrists/hands is still a challenge due to the limitations of myoelectric man–machine interfaces. Multiple factors limit the overall performance and usability of these interfaces, such as the need to control degrees of freedom sequentially and not concurrently, and the inaccuracies in decoding the user intent from weak or fatigued muscles. In this article, we developed a novel man–machine interface that endows a myoelectric prosthesis (MYO) with artificial perception, estimation of user intention, and intelligent control (MYO–PACE) to continuously support the user with automation while preparing the prosthesis for grasping. We compared the MYO–PACE against state-of-the-art myoelectric control (pattern recognition) in laboratory and clinical tests. For this purpose, eight able-bodied and two amputee individuals performed a standard clinical test consisting of a series of manipulation tasks (portion of the SHAP test), as well as a more complex sequence of transfer tasks in a cluttered scene. In all tests, the subjects not only completed the trials faster using the MYO–PACE but also achieved more efficient myoelectric control. These results demonstrate that the implementation of advanced perception, context interpretation, and autonomous decision-making into active prostheses improves control dexterity. Moreover, it also effectively supports the user by speeding up the preshaping phase of the movement and decreasing muscle use.
McManus L, Lowery M, Merletti R, et al., 2021, Consensus for experimental design in electromyography (CEDE) project: Terminology matrix, Journal of Electromyography and Kinesiology, Vol: 59, ISSN: 1050-6411
Consensus on the definition of common terms in electromyography (EMG) research promotes consistency in the EMG literature and facilitates the integration of research across the field. This paper presents a matrix developed within the Consensus for Experimental Design in Electromyography (CEDE) project, providing definitions for terms used in the EMG literature. The definitions for physiological and technical terms that are common in EMG research are included in two tables, with key information on each definition provided in a comment section. A brief outline of some basic principles for recording and analyzing EMG is included in an appendix, to provide researchers new to EMG with background and context for understanding the definitions of physiological and technical terms. This terminology matrix can be used as a reference to aid researchers new to EMG in reviewing the EMG literature.
Tottrup L, Atashzar SF, Farina D, et al., 2021, Altered evoked low-frequency connectivity from SI to ACC following nerve injury in rats, JOURNAL OF NEURAL ENGINEERING, Vol: 18, ISSN: 1741-2560
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- Citations: 1
Savic AM, Aliakbaryhosseinabadi S, Blicher JU, et al., 2021, Online control of an assistive active glove by slow cortical signals in patients with amyotrophic lateral sclerosis, JOURNAL OF NEURAL ENGINEERING, Vol: 18, ISSN: 1741-2560
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- Citations: 4
Caillet A, Phillips ATM, Farina D, et al., 2021, Development of a high-density EMG-driven Hill-type muscle model, XXVII Congress of the International Society of Biomechanics, Publisher: International Society of Biomechanics
Farina D, Mohammadi A, Adali T, et al., 2021, Signal Processing for Neurorehabilitation and Assistive Technologies [From the Guest Editors], IEEE Signal Processing Magazine, Vol: 38, Pages: 5-7, ISSN: 1053-5888
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- Citations: 5
Holobar A, Farina D, 2021, Noninvasive Neural Interfacing with Wearable Muscle Sensors: Combining Convolutive Blind Source Separation Methods and Deep Learning Techniques for Neural Decoding, IEEE Signal Processing Magazine, Vol: 38, Pages: 103-118, ISSN: 1053-5888
Neural interfacing is essential for advancing our fundamental understanding of movement neurophysiology and for developing human-machine interaction systems. This can be achieved at different levels of the central nervous system (CNS) and peripheral nervous system (PNS); however, direct neural interfaces with brain and nerve tissues face important challenges and are currently limited to clinical cases of severe motor impairment. Recent advances in electronics and signal processing for recording and analyzing surface electromyographic (sEMG) signals allow for a radically new way of establishing human interfaces by reverse engineering the neural information embedded in the electrical activity of skeletal muscles. This approach provides a window into the spiking activity of motor neurons in the spinal cord. In this article, we present a brief overview of neural interfaces and discuss the properties of multichannel sEMG in comparison to other CNS and PNS recording modalities. We then describe signal processing approaches for neural interfacing from sEMG, with a focus on recent breakthroughs in convolutive blind source separation (BSS) methods and deep learning techniques. When combined, these approaches establish unique noninvasive human-machine interfaces for neurotechnologies, with applications in medical devices and large-scale consumer electronics.
Jung MK, Muceli S, Rodrigues C, et al., 2021, Intramuscular EMG-driven musculoskeletal modelling: towards implanted muscle interfacing in spinal cord injury patients, IEEE Transactions on Biomedical Engineering, Vol: 69, Pages: 63-74, ISSN: 0018-9294
OBJECTIVE: Surface EMG-driven modelling has been proposed as a means to control assistive devices by estimating joint torques. Implanted EMG sensors have several advantages over wearable sensors but provide a more localized information on muscle activity, which may impact torque estimates. Here, we tested and compared the use of surface and intramuscular EMG measurements for the estimation of required assistive joint torques using EMG driven modelling. METHODS: Four healthy subjects and three incomplete spinal cord injury (SCI) patients performed walking trials at varying speeds. Motion capture marker trajectories, surface and intramuscular EMG, and ground reaction forces were measured concurrently. Subject-specific musculoskeletal models were developed for all subjects, and inverse dynamics analysis was performed for all individual trials. EMG-driven modelling based joint torque estimates were obtained from surface and intramuscular EMG. RESULTS: The correlation between the experimental and predicted joint torques was similar when using intramuscular or surface EMG as input to the EMG-driven modelling estimator in both healthy individuals and patients. CONCLUSION: We have provided the first comparison of non-invasive and implanted EMG sensors as input signals for torque estimates in healthy individuals and SCI patients. SIGNIFICANCE: Implanted EMG sensors have the potential to be used as a reliable input for assistive exoskeleton joint torque actuation.
Ibanez Pereda J, Alessandro DV, John C R, et al., 2021, Only the fastest corticospinal fibers contribute to beta corticomuscular coherence, The Journal of Neuroscience, Vol: 41, Pages: 4867-4879, ISSN: 0270-6474
Human corticospinal transmission is commonly studied using brain stimulation. However, this approach is biased to activity in the fastest conducting axons. It is unclear whether conclusions obtained in this context are representative of volitional activity in mild-to-moderate contractions. An alternative to overcome this limitation may be to study the corticospinal transmission of endogenously generated brain activity. Here we investigate in humans (N=19; of either sex), the transmission speeds of cortical beta rhythms (∼20Hz) traveling to arm (first dorsal interosseous) and leg (tibialis anterior) muscles during tonic mild contractions. For this purpose, we propose two improvements for the estimation of cortico-muscular beta transmission delays. First, we show that the cumulant density (cross-covariance) is more accurate than the commonly-used directed coherence to estimate transmission delays in bidirectional systems transmitting band-limited signals. Second, we show that when spiking motor unit activity is used instead of interference electromyography, cortico-muscular transmission delay estimates are unaffected by the shapes of the motor unit action potentials. Applying these improvements, we show that descending cortico-muscular beta transmission is only 1-2ms slower than expected from the fastest corticospinal pathways. In the last part of our work, we show results from simulations using estimated distributions of the conduction velocities for descending axons projecting to lower motoneurons (from macaque histological measurements) to suggest two scenarios that can explain fast cortico-muscular transmission: either only the fastest corticospinal axons selectively transmit beta activity, or else the entire pool does. The implications of these two scenarios for our understanding of corticomuscular interactions are discussed.
Hug F, Avrillon S, Del Vecchio A, et al., 2021, Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators, Journal of Electromyography and Kinesiology, Vol: 58, Pages: 102548-102548, ISSN: 1050-6411
There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons × 11 contractions/muscles × 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.
Natalie M-K, Ibanez Pereda J, Dario F, 2021, Towards a mechanistic approach for the development of non-invasive brain-computer interfaces for motor rehabilitation, The Journal of Physiology, Vol: 599, Pages: 2361-2374, ISSN: 0022-3751
Brain‐computer interfaces (BCIs) designed for motor rehabilitation use brain signals associated with motor‐processing states to guide neuroplastic changes in a state‐dependent manner. These technologies are uniquely positioned to induce targeted and functionally relevant plastic changes in the human motor nervous system. However, while several studies have shown that BCI‐based neuromodulation interventions may improve motor function in patients with lesions in the central nervous system, the neurophysiological structures and processes targeted with the BCI interventions have not been identified. In this review, we first summarize current knowledge of the changes in the central nervous system associated with learning new motor skills. Then, we propose a classification of current BCI paradigms for plasticity induction and motor rehabilitation based on the expected neural plastic changes promoted. This classification proposes four paradigms based on two criteria: the plasticity induction methods and the brain states targeted. The existing evidence regarding the brain circuits and processes targeted with these different BCIs is discussed in detail. The proposed classification aims to serve as a starting point for future studies trying to elucidate the underlying plastic changes following BCI interventions.
Farina D, Mrachacz-Kersting N, 2021, Brain-computer interfaces and plasticity of the human nervous system, JOURNAL OF PHYSIOLOGY-LONDON, Vol: 599, Pages: 2349-2350, ISSN: 0022-3751
Sturma A, Hruby LA, Boesendorfer A, et al., 2021, Prosthetic embodiment and body image changes in patients undergoing bionic reconstruction following brachial plexus injury, Frontiers in Neurorobotics, Vol: 15, Pages: 1-10, ISSN: 1662-5218
Brachial plexus injuries with multiple-root involvement lead to severe and long-lasting impairments in the functionality and appearance of the affected upper extremity. In cases, where biologic reconstruction of hand and arm function is not possible, bionic reconstruction may be considered as a viable clinical option. Bionic reconstruction, through a careful combination of surgical augmentation, amputation, and prosthetic substitution of the functionless hand, has been shown to achieve substantial improvements in function and quality of life. However, it is known that long-term distortions in the body image are present in patients with severe nerve injury as well as in prosthetic users regardless of the level of function. To date, the body image of patients who voluntarily opted for elective amputation and prosthetic reconstruction has not been investigated. Moreover, the degree of embodiment of the prosthesis in these patients is unknown. We have conducted a longitudinal study evaluating changes of body image using the patient-reported Body Image Questionnaire 20 (BIQ-20) and a structured questionnaire about prosthetic embodiment. Six patients have been included. At follow up 2.5–5 years after intervention, a majority of patients reported better BIQ-20 scores including a less negative body evaluation (5 out of 6 patients) and higher vital body dynamics (4 out of 6 patients). Moreover, patients described a strong to moderate prosthesis embodiment. Interestingly, whether patients reported performing bimanual tasks together with the prosthetic hand or not, did not influence their perception of the prosthesis as a body part. In general, this group of patients undergoing prosthetic substitution after brachial plexus injury shows noticeable inter-individual differences. This indicates that the replacement of human anatomy with technology is not a straight-forward process perceived in the same way by everyone opting for it.
Aliakbaryhosseinabadi 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: 1746-8094
Motor 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.
Ting 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: 1-6
Action 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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