619 results found
Aliakbaryhosseinabadi S, Farina D, Mrachacz-Kersting N, 2020, Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis., J Neural Eng
The performance of brain-computer interface (BCI) systems is influenced by the user's mental state, such as attention diversion. In this study, we propose a novel online BCI system able to adapt with variations in the users' attention during real-time movement execution. Electroencephalography (EEG) signals were recorded from healthy participants and patients with Amyotrophic Lateral Sclerosis (ALS) while attention to the target task (a dorsiflexion movement) was drifted using an auditory oddball task. For each participant, the selected channels, classifiers and features from a training data set were used in the online mode to predict the attention status. For both healthy controls and patients, feedback to the user on attentional status reduced the amount of attention diversion. The findings presented here demonstrate successful monitoring of the users' attention in a fully online BCI system, and further, that real-time neurofeedback on the users' attention state can be implemented to focus the attention of the user back onto the main task.
Kapelner T, Sartori M, Negro F, et al., 2020, Neuro-Musculoskeletal Mapping for Man-Machine Interfacing., Sci Rep, Vol: 10
We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function.
Casolo A, Farina D, Falla D, et al., 2020, Strength training Increases conduction velocity of high-threshold motor units., Medicine and Science in Sports and Exercise, Vol: 52, Pages: 955-967, ISSN: 0195-9131
PURPOSE: Motor unit conduction velocity (MUCV) represents the propagation velocity of action potentials along the muscle fibres innervated by individual motor neurons and indirectly reflects the electrophysiological properties of the sarcolemma. In this study, we investigated the effect of a 4-week strength training intervention on the peripheral properties (MUCV and motor unit action potential amplitude, RMSMU) of populations of longitudinally tracked motor units (MUs). METHODS: The adjustments exhibited by 12 individuals who participated in the training (INT) were compared with 12 controls (CON). Strength training involved ballistic (4x10) and sustained (3x10) isometric ankle dorsi flexions. Measurement sessions involved the recordings of maximal voluntary isometric force (MViF) and submaximal isometric ramp contractions, while high-density surface EMG (HDsEMG) was recorded from the tibialis anterior. HDsEMG signals were decomposed into individual MU discharge timings and MUs were tracked across the intervention. RESULTS: MViF (+14.1%, P=0.003) and average MUCV (+3.00%, P=0.028) increased in the INT group, while normalized MUs recruitment threshold (RT) decreased (-14.9%, P=0.001). The slope (rate of change) of the regression between MUCV and MUs RT increased only in the INT group (+32.6%, P=0.028), indicating a progressive greater increase in MUCV for higher-threshold MUs. The intercept (initial value) of MUCV did not change following the intervention (P=0.568). The association between RMSMU and MUs RT was not altered by the training. CONCLUSION: The increase in the rate of change in MUCV as a function of MU recruitment threshold, but not the initial value of MUCV, suggests that short-term strength training elicits specific adaptations in the electrophysiological properties of the muscle fibre membrane in high-threshold motor units.
Martinez-Valdes E, Negro F, Farina D, et al., 2020, Divergent response of low- versus high-threshold motor units to experimental muscle pain., J Physiol
KEY POINTS: The neural strategies behind the control of force during muscle pain are not well understood as previous research has been limited in assessing pain responses only during low-force contractions. Here we compared, for the first time, the behaviour of motor units recruited at low and high forces in response to pain. The results showed that motor units activated at low forces were inhibited while those recruited at higher forces increased their activity in response to pain. When analysing lower- and higher-threshold motor unit behaviour at high forces we observed differential changes in discharge rate and recruitment threshold across the motor unit pool. These adjustments allow the exertion of high forces in acutely painful conditions but could eventually lead to greater fatigue and stress of the muscle tissue. ABSTRACT: During low-force contractions, motor unit discharge rates decrease when muscle pain is induced by injecting nociceptive substances into the muscle. Despite this consistent observation, it is currently unknown how the central nervous system regulates motor unit behaviour in the presence of muscle pain at high forces. For this reason, we analysed the tibialis anterior motor unit behaviour at low and high forces. Surface EMG signals were recorded from 15 healthy participants (mean age (SD) 26 (3) years, six females) using a 64-electrode grid while performing isometric ankle dorsiflexion contractions at 20% and 70% of the maximum voluntary force (MVC). Signals were decomposed and the same motor units were tracked across painful (intramuscular hypertonic saline injection) and non-painful (baseline, isotonic saline, post-pain) contractions. At 20% MVC, discharge rates decreased significantly in the painful condition (baseline vs. pain: 12.7 (1.1) Hz to 11.5 (0.9) Hz, P < 0.001). Conversely, at 70% MVC, discharge rates increased significantly during pain (baseline vs. pain: 19.7 (2.8) Hz to 21.3 (3.5) Hz, p = 0.029) and recr
Ibáñez J, Fu L, Rocchi L, et al., 2020, Plasticity induced by pairing brain stimulation with motor-related states only targets a subset of cortical neurones., Brain Stimul, Vol: 13, Pages: 464-466
Del Vecchio A, Negro F, Holobar A, et al., 2020, Direct translation of findings in isolated animal preparations to in vivo human motoneuron behaviour is challenging, JOURNAL OF PHYSIOLOGY-LONDON, Vol: 598, Pages: 1111-1112, ISSN: 0022-3751
Yu T, Akhmadeev K, Le Carpentier E, et al., 2020, Recursive Decomposition of Electromyographic Signals With a Varying Number of Active Sources: Bayesian Modeling and Filtering, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 67, Pages: 428-440, ISSN: 0018-9294
Germer CM, Del Vecchio A, Negro F, et al., 2020, Neurophysiological correlates of force control improvement induced by sinusoidal vibrotactile stimulation, JOURNAL OF NEURAL ENGINEERING, Vol: 17, ISSN: 1741-2560
Dideriksen JL, Del Vecchio A, Farina D, 2020, Neural and muscular determinants of maximal rate of force development, JOURNAL OF NEUROPHYSIOLOGY, Vol: 123, Pages: 149-157, ISSN: 0022-3077
Chen C, Yu Y, Ma S, et al., 2020, Hand gesture recognition based on motor unit spike trains decoded from high-density electromyography, BIOMEDICAL SIGNAL PROCESSING AND CONTROL, Vol: 55, ISSN: 1746-8094
Gathmann T, Atashzar SF, Alva PGS, et al., 2020, Wearable Dual-Frequency Vibrotactile System for Restoring Force and Stiffness Perception, IEEE TRANSACTIONS ON HAPTICS, Vol: 13, Pages: 191-196, ISSN: 1939-1412
Wilke MA, Niethammer C, Meyer B, et al., 2019, Psychometric characterization of incidental feedback sources during grasping with a hand prosthesis., J Neuroeng Rehabil, Vol: 16
BACKGROUND: A prosthetic system should ideally reinstate the bidirectional communication between the user's brain and its end effector by restoring both motor and sensory functions lost after an amputation. However, current commercial prostheses generally do not incorporate somatosensory feedback. Even without explicit feedback, grasping using a prosthesis partly relies on sensory information. Indeed, the prosthesis operation is characterized by visual and sound cues that could be exploited by the user to estimate the prosthesis state. However, the quality of this incidental feedback has not been objectively evaluated. METHODS: In this study, the psychometric properties of the auditory and visual feedback of prosthesis motion were assessed and compared to that of a vibro-tactile interface. Twelve able-bodied subjects passively observed prosthesis closing and grasping an object, and they were asked to discriminate (experiment I) or estimate (experiment II) the closing velocity of the prosthesis using visual (VIS), acoustic (SND), or combined (VIS + SND) feedback. In experiment II, the subjects performed the task also with a vibrotactile stimulus (VIB) delivered using a single tactor. The outcome measures for the discrimination and estimation experiments were just noticeable difference (JND) and median absolute estimation error (MAE), respectively. RESULTS: The results demonstrated that the incidental sources provided a remarkably good discrimination and estimation of the closing velocity, significantly outperforming the vibrotactile feedback. Using incidental sources, the subjects could discriminate almost the minimum possible increment/decrement in velocity that could be commanded to the prosthesis (median JND < 2% for SND and VIS + SND). Similarly, the median MAE in estimating the prosthesis velocity randomly commanded from the full working range was also low, i.e., approximately 5% in SND and VIS + SND. CO
Vecchio AD, Farina D, 2019, Interfacing the neural output of the spinal cord: robust and reliable longitudinal identification of motor neurons in humans., J Neural Eng, Vol: 17
OBJECTIVE: Non-invasive electromyographic techniques can detect action potentials from muscle units with high spatial dimensionality. These technologies allow the decoding of large samples of motor units by using high-density grids of electrodes that are placed on the skin overlying contracting muscles and therefore provide a non-invasive representation of the human spinal cord output. APPROACH: From a sample of >1200 decoded motor neurons, we show that motor neuron activity can be identified in humans in the full muscle recruitment range with high accuracy. MAIN RESULTS: After showing the validity of decomposition with novel test parameters, we demonstrate that the same motor neurons can be tracked over a period of one-month, which allows for the longitudinal analysis of individual human neural cells. SIGNIFICANCE: These results show the potential of an accurate and reliable assessment of large populations of motor neurons in physiological investigations. We discuss the potential of this non-invasive neural interfacing technology for the study of the neural determinants of movement and man-machine interfacing.
Del Vecchio A, Germer CM, Elias LA, et al., 2019, The human central nervous system transmits common synaptic inputs to distinct motor neuron pools during non-synergistic digit actions., J Physiol, Vol: 597, Pages: 5935-5948
KEY POINTS: Neural connectivity between distinct motor neuronal modules in the spinal cord is classically studied through electrical stimulation or multi-muscle EMG recordings. We quantified the strength of correlation in the activity of two distinct populations of motor neurons innervating the thenar and first dorsal interosseous muscles during tasks that required the two hand muscles to exert matched or un-matched forces in different directions. We show that when the two hand muscles are concurrently activated, synaptic input to the two motor neuron pools is shared across all frequency bandwidths (representing cortical and spinal input) associated with force control. The observed connectivity indicates that motor neuron pools receive common input even when digit actions do not belong to a common behavioural repertoire. ABSTRACT: Neural connectivity between distinct motor neuronal modules in the spinal cord is classically studied through electrical stimulation or multi-muscle EMG recordings. Here we quantify the strength of correlation in the activity of two distinct populations of motor neurons innervating the thenar and first dorsal interosseous muscles in humans during voluntary contractions. To remove confounds associated with previous studies, we used a task that required the two hand muscles to exert matched or un-matched forces in different directions. Despite the force production task consisting of uncommon digit force coordination patterns, we found that synaptic input to motor neurons is shared across all frequency bands, reflecting cortical and spinal inputs associated with force control. The coherence between discharge timings of the two pools of motor neurons was significant at the delta (0-5 Hz), alpha (5-15 Hz) and beta (15-35 Hz) bands (P < 0.05). These results suggest that correlated input to motor neurons of two hand muscles can occur even during tasks not belonging to a common behavioural repertoire and despite lack of
Puttaraksa G, Muceli S, Alvaro Gallego J, et al., 2019, Voluntary and tremorogenic inputs to motor neuron pools of agonist/antagonist muscles in essential tremor patients, JOURNAL OF NEUROPHYSIOLOGY, Vol: 122, Pages: 2043-2053, ISSN: 0022-3077
Aliakbaryhosseinabadi S, Kamavuako EN, Jiang N, et al., 2019, Classification of Movement Preparation Between Attended and Distracted Self-Paced Motor Tasks., IEEE Trans Biomed Eng, Vol: 66, Pages: 3060-3071
OBJECTIVE: Brain-computer interface (BCI) systems aim to control external devices by using brain signals. The performance of these systems is influenced by the user's mental state, such as attention. In this study, we classified two attention states to a target task (attended and distracted task level) while attention to the task is altered by one of three types of distractors. METHODS: A total of 27 participants were allocated into three experimental groups and exposed to one type of distractor. An attended condition that was the same across the three groups comprised only the main task execution (self-paced dorsiflexion) while the distracted condition was concurrent execution of the main task and an oddball task (dual-task condition). Electroencephalography signals were recorded from 28 electrodes to classify the two attention states of attended or distracted task conditions by extracting temporal and spectral features. RESULTS: The results showed that the ensemble classification accuracy using the combination of temporal and spectral features (spectro-temporal features, 82.3 ± 2.7%) was greater than using temporal (69 ± 2.2%) and spectral (80.3 ± 2.6%) features separately. The classification accuracy was computed using a combination of different channel locations, and it was demonstrated that a combination of parietal and centrally located channels was superior for classification of two attention states during movement preparation (parietal channels: 84.6 ± 1.3%, central and parietal channels: 87.2 ± 1.5%). CONCLUSION: It is possible to monitor the users' attention to the task for different types of distractors. SIGNIFICANCE: It has implications for online BCI systems where the requirement is for high accuracy of intention detection.
Thompson CK, Johnson MD, Negro F, et al., 2019, Exogenous neuromodulation of spinal neurons induces beta-band coherence during self-sustained discharge of hind limb motor unit populations., J Appl Physiol (1985), Vol: 127, Pages: 1034-1041
The spontaneous or self-sustained discharge of spinal motoneurons can be observed in both animals and humans. Although the origins of this self-sustained discharge are not fully known, it can be generated by activation of persistent inward currents intrinsic to the motoneuron. If self-sustained discharge is generated exclusively through this intrinsic mechanism, the discharge of individual motor units will be relatively independent of one another. Alternatively, if increased activation of premotor circuits underlies this prolonged discharge of spinal motoneurons, we would expect correlated activity among motoneurons. Our aim is to assess potential synaptic drive by quantifying coherence during self-sustained discharge of spinal motoneurons. Electromyographic activity was collected from 20 decerebrate animals using a 64-channel electrode grid placed on the isolated soleus muscle before and following intrathecal administration of methoxamine, a selective α1-noradrenergic agonist. Sustained muscle activity was recorded and decomposed into the discharge times of ~10-30 concurrently active individual motor units. Consistent with previous reports, the self-sustained discharge of motor units occurred at low mean discharge rates with low-interspike variability. Before methoxamine administration, significant low-frequency coherence (<2 Hz) was observed, while minimal coherence was observed within higher frequency bands. Following intrathecal administration of methoxamine, increases in motor unit discharge rates and strong coherence in both the low-frequency and 15- to 30-Hz beta bands were observed. These data demonstrate beta-band coherence among motor units can be observed through noncortical mechanisms and that neuromodulation of spinal/brainstem neurons greatly influences coherent discharge within spinal motor pools.NEW & NOTEWORTHY The correlated discharge of spinal motoneurons is often used to describe the input to the motor pool. We demonstrate spinal/bra
Rashid U, Niazi IK, Signal N, et al., 2019, Optimal automatic detection of muscle activation intervals., J Electromyogr Kinesiol, Vol: 48, Pages: 103-111
A significant challenge in surface electromyography (sEMG) is the accurate identification of onsets and offsets of muscle activations. Manual labelling and automatic detection are currently used with varying degrees of reliability, accuracy and time efficiency. Automatic methods still require significant manual input to set the optimal parameters for the detection algorithm. These parameters usually need to be adjusted for each individual, muscle and movement task. We propose a method to automatically identify optimal detection parameters in a minimally supervised way. The proposed method solves an optimisation problem that only requires as input the number of activation bursts in the sEMG in a given time interval. This approach was tested on an extended version of the widely adopted double thresholding algorithm, although the optimisation could be applied to any detection algorithm. sEMG data from 22 healthy participants performing a single (ankle dorsiflexion) and a multi-joint (step on/off) task were used for evaluation. Detection rate, concordance, F1 score as an average of sensitivity and precision, degree of over detection, and degree of under detection were used as performance metrices. The proposed method improved the performance of the double thresholding algorithm in multi-joint movement and had the same performance in single joint movement with respect to the performance of the double thresholding algorithm with task specific global parameters. Moreover, the proposed method was robust when an error of up to ±10% was introduced in the number of activation bursts in the optimisation phase regardless of the movement. In conclusion, our optimised method has improved the automation of a sEMG detection algorithm which may reduce the time burden associated with current sEMG processing.
Xu R, Dosen S, Jiang N, et al., 2019, Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch, JOURNAL OF NEURAL ENGINEERING, Vol: 16, ISSN: 1741-2560
Besomi M, Hodges PW, Van Dieën J, et al., 2019, Consensus for experimental design in electromyography (CEDE) project: Electrode selection matrix., J Electromyogr Kinesiol, Vol: 48, Pages: 128-144
The Consensus for Experimental Design in Electromyography (CEDE) project is an international initiative which aims to guide decision-making in recording, analysis, and interpretation of electromyographic (EMG) data. The quality of the EMG recording, and validity of its interpretation depend on many characteristics of the recording set-up and analysis procedures. Different electrode types (i.e., surface and intramuscular) will influence the recorded signal and its interpretation. This report presents a matrix to consider the best electrode type selection for recording EMG, and the process undertaken to achieve consensus. Four electrode types were considered: (1) conventional surface electrode, (2) surface matrix or array electrode, (3) fine-wire electrode, and (4) needle electrode. General features, pros, and cons of each electrode type are presented first. This information is followed by recommendations for specific types of muscles, the information that can be estimated, the typical representativeness of the recording and the types of contractions for which the electrode is best suited. This matrix is intended to help researchers when selecting and reporting the electrode type in EMG studies.
Felici F, Bazzucchi I, Casolo A, et al., 2019, The relative strength of common synaptic input to motor neurons is not a determinant of the maximal rate of force development in humans, Publisher: WILEY, Pages: 25-26, ISSN: 1748-1708
Sturma A, Hruby LA, Farina D, et al., 2019, Structured motor rehabilitation after selective nerve transfers, Jove-Journal of Visualized Experiments, Vol: 150, Pages: 1-11, ISSN: 1940-087X
Here, we present a protocol for the motor rehabilitation of patients with severe nerve injuries and selective nerve transfer surgery. It aims at restoring the motor function proposing several stages in patient education, early-stage therapy after surgery and interventions for rehabilitation after successful re-innervation of the nerve’s target.
Salminger S, Sturma A, Hofer C, et al., 2019, Long-term implant of intramuscular sensors and nerve transfers for wireless control of robotic arms in above-elbow amputees, SCIENCE ROBOTICS, Vol: 4, ISSN: 2470-9476
Durandau G, Farina D, Asín-Prieto G, et al., 2019, Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling., J Neuroeng Rehabil, Vol: 16
BACKGROUND: Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. METHODS: We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. RESULTS: We demonstrated patients' control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. CONCLUSIONS: Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessin
Del Vecchio A, Falla D, Felici F, et al., 2019, The relative strength of common synaptic input to motor neurons is not a determinant of the maximal rate of force development in humans, JOURNAL OF APPLIED PHYSIOLOGY, Vol: 127, Pages: 205-214, ISSN: 8750-7587
Ting J, Farina D, Weber DJ, et al., 2019, A wearable neural interface for detecting and decoding attempted hand movements in a person with tetraplegia., Pages: 1930-1933, ISSN: 1557-170X
We are developing a wearable neural interface based on high-density surface electromyography (HDEMG) for detecting and decoding signals from spared motor units in the forearms of people with tetraplegia after spinal cord injury (SCI). A lightweight, form-fitting garment containing 150 disc electrodes and covering the entire forearm was used to map the myoelectric activity of forearm muscles during a wide range of voluntary tasks of a person with chronic tetraplegia after SCI (C5 motor and C6 sensory American Spinal Injury Association Impairment Scale B spinal cord injury). Despite exhibiting no overt finger motion, myoelectric signals were detectable for attempted movements of individual digits and were highly discriminable. Motor unit decomposition was used to identify the activity of >30 motor neurons, active specifically during rotation, pronation of the wrist (4 units), and flexion of the elbow joint (7 units), and during attempted movements of individual hand digits (1-5 units). In addition, we performed a neural connectivity analysis based on the power of the common oscillations of the identified motor neurons in the delta (~5Hz), alpha (~6-12 Hz), and beta bands (~15-30 Hz). This analysis showed clear common synaptic inputs to the identified motor neurons in all the analyzed frequency bands. This neural interface offers a new potential for the control of assistive technologies, whereby the motor neurons spared after SCI may serve as a direct readout of motor intent that allows proportional control over several distinct degrees of freedom. Moreover, this framework can be used to study the reorganization and recovery of spinal networks after injury and rehabilitation.
Pascual-Valdunciel A, Barroso FO, Muceli S, et al., 2019, Modulation of reciprocal inhibition at the wrist as a neurophysiological correlate of tremor suppression: a pilot healthy subject study., Conf Proc IEEE Eng Med Biol Soc, Vol: 2019, Pages: 6267-6272, ISSN: 1557-170X
It has been shown that Ia afferents inhibit muscle activity of the ipsilateral antagonist, a mechanism known as reciprocal inhibition. Stimulation of these afferents may be explored for the therapeutic reduction of pathological tremor (Essential Tremor or due Parkinson's Disease, for example). However, only a few studies have investigated reciprocal inhibition of wrist flexor / extensor motor control. The main goal of this study was to characterize reciprocal inhibition of wrist flexors / extensors by applying surface electrical stimulation to the radial and median nerves, respectively. Firstly, the direct (M) and monosynaptic (H) reflex responses to increasing median and radial nerve stimulation were recorded to characterize the recruitment curve of the flexor carpi radialis (FCR) and extensor carpi radialis (ECR) muscles, respectively. Based on the recruitment curve data, we then stimulated the median and radial nerves below (<; MT) and above (> MT) motor threshold (MT) during a submaximal isometric task to assess the amount of inhibition on ECR and FCR antagonist muscles, respectively. The stimulation of both nerves produced a long-duration inhibition of the antagonist motoneuron pool activity. On average, maximum peak of inhibition was 27 ± 6% for ECR and 32 ± 9% for FCR with stimulation <; MT; maximum peak of inhibition was 45 ± 7% for ECR and 44 ± 13% for FCR when using stimulation > MT. These results validate this neurophysiological technique that demonstrates a mechanism similar to classical reciprocal Ia inhibition reported for other limb joints and that can be used to benchmark strategies to suppress pathological tremor.
Colamarino E, Muceli S, Ibanez J, et al., 2019, Adaptive learning in the detection of Movement Related Cortical Potentials improves usability of associative Brain-Computer Interfaces., Conf Proc IEEE Eng Med Biol Soc, Vol: 2019, Pages: 3079-3082, ISSN: 1557-170X
Brain-computer interfaces have increasingly found applications in motor function recovery in stroke patients. In this context, it has been demonstrated that associative-BCI protocols, implemented by means the movement related cortical potentials (MRCPs), induce significant cortical plasticity. To date, no methods have been proposed to deal with brain signal (i.e. MRCP feature) non-stationarity. This study introduces adaptive learning methods in MRCP detection and aims at comparing a no-adaptive approach based on the Locality Sensitive Discriminant Analysis (LSDA) with three LSDA-based adaptive approaches. As a proof of concept, EEG and force data were collected from six healthy subjects while performing isometric ankle dorsiflexion. Results revealed that adaptive algorithms increase the number of true detections and decrease the number of false positives per minute. Moreover, the markedly reduction of BCI system calibration time suggests that these methods have the potential to improve the usability of associative-BCI in post-stroke motor recovery.
Tanzarella S, Muceli S, Del Vecchio A, et al., 2019, A high-density surface EMG framework for the study of motor neurons controlling the intrinsic and extrinsic muscles of the hand., Conf Proc IEEE Eng Med Biol Soc, Vol: 2019, Pages: 2307-2310, ISSN: 1557-170X
We propose a framework based on high-density surface electromyography (HD-sEMG) to identify the neural drive to muscles controlling the human hand. High-density (320 channels) sEMG signals were recorded concurrently from intrinsic (the four dorsal interossei and thenar) and extrinsic (forearm) hand muscles and then decomposed into the constituent trains of motor unit (MU) action potentials. The participants performed pinch tasks with simultaneous activation of the thumb and one of the other fingers with sinusoidal force variations. The common drive among MUs across different muscles was extracted via principal component analysis (PCA) of the smoothed MU discharge rates. The first principal component of the smoothed discharge rates of all identified motor neurons explained 48.7 ± 15.4% of the total variance across all pinching tasks, indicating a common neural input shared by different muscles of the forearm and the hand.. When considering only the MUs extracted from extrinsic and intrinsic muscles, the percent of variance explained was 48.3 ± 15.3% and 57.1 ± 15.5%, respectively. This framework is conceived to use motor neuron activity for a proportional myoelectric control and rehabilitation technologies. A wearable adaptation of the framework is proposed for future perspectives.
Stachaczyk M, Atashzar SF, Farina D, 2019, An Online Spectral Information-Enhanced Approach for Artifact Detection and Fault Attentuation in Myoelectric Control., IEEE Int Conf Rehabil Robot, Vol: 2019, Pages: 671-675
In myocontrol of neuroprosthetic devices, multichannel electromyography (EMG) can be used to decode the intended motor command, based on distributed activation patterns of stump muscles. In this regard, the high density EMG (HD-EMG) approach allows for enhancement of the spatiotemporal resolution for motor intention detection. Despite the advantages of relying on several EMG channels, the challenge of high-density electrode systems is the dynamically changing electrode-skin contact impedance, which can affect a considerable number of electrodes over the time of data acquisition. This can result in obtaining unreliable, low-quality EMG recording with a distributed artifact pattern over the grid of EMG sensors. To address this issue, we propose a novel online approach for adaptive information extraction and enhancement for automatic artifact detection and attenuation in HD-EMG-based myocontrol of prosthetic devices. The method is based on an adaptive weighting scheme that modifies the contribution of each HD-EMG channel considering the spectral information content relative to artifacts. The technique (named IE-HD-EMG) was tested as an online pre-conditioning step for a challenging multiclass classification problem of 4-finger activation, using linear discriminant analysis. It is shown that for this application, the proposed IE-HD-EMG technique led to a superior performance in finger activation recognition (79.25% accuracy, 89% sensitivity, 89.15% specificity) in comparison to the conventional HD-EMG recording under the same condition without the proposed approach (56.25% accuracy, 61.3% sensitivity, 67% specificity). Therefore, the proposed technique can have a significant potential to expand the clinical viability of HD-EMG systems.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.