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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, 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Pages: 2307-2310, ISSN: 1557-170X
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- Citations: 2
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, 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Pages: 3079-3082, ISSN: 1557-170X
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- Citations: 1
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, 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Pages: 6267-6272, ISSN: 1557-170X
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- Citations: 6
Stachaczyk M, Atashzar SF, Farina D, 2019, An Online Spectral Information-Enhanced Approach for Artifact Detection and Fault Attentuation in Myoelectric Control, 2019 IEEE 16TH INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS (ICORR), Pages: 671-675, ISSN: 1945-7898
Ubeda A, Del Vecchio A, Vujaklija I, et al., 2019, Analysis of Intramuscular Motor Unit Coherence in the Tibialis Anterior Muscle as a Tool for the Assessment of Robot-Assisted Rehabilitation, Editors: Masia, Micera, Akay, Pons, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 231-235, ISBN: 978-3-030-01844-3
Stachaczyk M, Atashzar SF, Dupan S, et al., 2019, Multiclass Detection and Tracking of Transient Motor Activation based on Decomposed Myoelectric Signals, 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 1080-1083, ISSN: 1948-3546
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- Citations: 7
Barsakcioglu DY, Farina D, 2018, A real-time surface EMG decomposition system for non-invasive human-machine interfaces, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, ISSN: 2163-4025
Real-time surface EMG decomposition, to extract neural activity of spinal motor neurons, provides a non-invasive solution for establishing direct interfaces with the central nervous system. In this paper, we present a real-time EMG decomposition system, validate it through both synthetic and experimental high-density surface EMG (HD-sEMG) data, and demonstrate the system in an upper-limb prosthetic control scenario. The proposed system achieves (in real-time) median decomposition accuracy comparable to offline methods (within 0.5 %) with minimal utilisation of computational resources (x20 faster compared to the literature).
Alameh M, Saleh M, Ansovini F, et al., 2018, Live Demonstration: System based on Electronic Skin and Cutaneous Electrostimulation for Sensory Feedback in Prosthetics
To restore the sense of touch in upper limb prosthetics, a prosthetic device can be equipped with tactile sensors providing data to be transmitted to the user using either invasive or non-invasive interfaces. This demo will be based on our sensing - noninvasive stimulation feedback system [1]. It will show two important aspects of our technology: 1) High sensitivity: light touch detection will be enabled by the high sensitivity of electronic skin (e-skin) prototypes for fingertips, 2) Measuring complex interactions: different contact shapes and multiple contact points will be detected by the commercial e- skin prototype suitable for palm.
Schweisfurth MA, Frahm J, Farina D, et al., 2018, Comparison of fMRI digit representations of the dominant and non-dominant hand in the human primary somatosensory cortex, Frontiers in Human Neuroscience, Vol: 12, ISSN: 1662-5161
The tactile digit representations in the primary somatosensory cortex have so far been mapped for either the left or the right hand. This study localized all ten digit representations in right-handed subjects and compared them within and across the left and right hands to assess potential differences in the functional organization of the digit map between hands and in the structural organization between hemispheres. Functional magnetic resonance imaging of tactile stimulation of each fingertip in BA 3b confirmed the expected lateral-anterior-inferior to medial-posterior-superior succession from thumb to little-finger representation, located in the post-central gyrus opposite to the motor hand knob. While the more functionally related measures, such as the extent and strength of activation as well as the Euclidean distance between neighboring digit representations, showed significant differences between the digits, no side difference was detected: the layout of the functional digit-representation map did not consistently differ between the left, non-dominant, and the right, dominant hand. Comparing the absolute spatial coordinates also revealed a significant difference for the digits, but not between the left and right hand digits. Estimating the individual subject's digit coordinates of one hand by within-subject mirroring of the other-hand digit coordinates across hemispheres yielded a larger estimation error distance than using averaged across-subjects coordinates from within the same hemisphere. However, both methods should only be used with care for single-subject clinical evaluation, as an average estimation error of around 9 mm was observed, being slightly higher than the average distance between neighboring digits.
Xu L, Negro F, Xu Y, et al., 2018, Does vibration superimposed on l ow-level isometric contraction alter motor unit recruitment strategy?, JOURNAL OF NEURAL ENGINEERING, Vol: 15, ISSN: 1741-2560
Sartori M, Durandau G, Dosen SA, et al., 2018, Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling, JOURNAL OF NEURAL ENGINEERING, Vol: 15, ISSN: 1741-2560
Murphy SA, Negro F, Farina D, et al., 2018, Stroke increases ischemia-related decreases in motor unit discharge rates, Journal of Neurophysiology, Vol: 120, Pages: 3246-3256, ISSN: 0022-3077
Following stroke, hyperexcitable sensory pathways, such as the group III/IV afferents that are sensitive to ischemia, may inhibit paretic motor neurons during exercise. We quantified the effects of whole leg ischemia on paretic vastus lateralis motor unit firing rates during submaximal isometric contractions. Ten chronic stroke survivors (>1 yr poststroke) and 10 controls participated. During conditions of whole leg occlusion, the discharge timings of motor units were identified from decomposition of high-density surface electromyography signals during repeated submaximal knee extensor contractions. Quadriceps resting twitch responses and near-infrared spectroscopy measurements of oxygen saturation as an indirect measure of blood flow were made. There was a greater decrease in paretic motor unit discharge rates during the occlusion compared with the controls (average decrease for stroke and controls, 12.3 ± 10.0% and 0.1 ± 12.4%, respectively; P < 0.001). The motor unit recruitment thresholds did not change with the occlusion (stroke: without occlusion, 11.68 ± 5.83%MVC vs. with occlusion, 11.11 ± 5.26%MVC; control: 11.87 ± 5.63 vs. 11.28 ± 5.29%MVC). Resting twitch amplitudes declined similarly for both groups in response to whole leg occlusion (stroke: 29.16 ± 6.88 vs. 25.75 ± 6.78 Nm; control: 38.80 ± 13.23 vs 30.14 ± 9.64 Nm). Controls had a greater exponential decline (lower time constant) in oxygen saturation compared with the stroke group (stroke time constant, 22.90 ± 10.26 min vs. control time constant, 5.46 ± 4.09 min; P < 0.001). Ischemia of the muscle resulted in greater neural inhibition of paretic motor units compared with controls and may contribute to deficient muscle activation poststroke. NEW
Prosperini L, Annovazzi P, Boffa L, et al., 2018, No evidence of disease activity (NEDA-3) and disability improvement after alemtuzumab treatment for multiple sclerosis: a 36-month real-world study., J Neurol, Vol: 265, Pages: 2851-2860
In this retrospective, multicenter, real-world study we collected clinical and magnetic resonance imaging (MRI) data of all patients (n = 40) with relapsing-remitting multiple sclerosis (RRMS) treated with alemtuzumab according to a "free-of-charge" protocol available before the drug marketing approval in Italy. Almost all (39/40) started alemtuzumab after discontinuing multiple disease-modifying treatments (DMTs) because of either lack of response or safety concerns. We considered the proportion of alemtuzumab-treated patients who had no evidence of disease activity (NEDA-3) and disability improvement over a 36-month follow-up period. NEDA-3 was defined as absence of relapses, disability worsening, and MRI activity. Disability improvement was defined as a sustained reduction of ≥ 1-point in Expanded Disability Status Scale (EDSS) score. At follow-up, 18 (45%) patients achieved NEDA-3, 30 (75%) were relapse-free, 33 (82.5%) were EDSS worsening-free, and 25 (62.5%) were MRI activity-free. Eleven (27.5%) patients had a sustained disability improvement. We found no predictor for the NEDA-3 status, while the interaction of higher EDSS score by higher number of pre-alemtuzumab relapses was associated with a greater chance of disability improvement (odds ratio 1.10, p = 0.049). Our study provides real-world evidence that alemtuzumab can promote clinical and MRI disease remission, as well as disability improvement, in a significant proportion of patients with RRMS despite prior multiple DMT failures. The drug safety profile was consistent with data available from clinical trials.
Shu X, Chen S, Meng J, et al., 2018, Tactile Stimulation Improves Sensorimotor Rhythm-based BCI Performance in Stroke Patients, IEEE Transactions on Biomedical Engineering, ISSN: 0018-9294
IEEE Objective: BCI decoding accuracy plays a crucial role in practical applications. With accurate feedback, BCI-based therapy determines beneficial neural plasticity in stroke patients. In this study, we aimed at improving sensorimotor rhythm (SMR)-based BCI performance by integrating motor tasks with tactile stimulation. Methods: Eleven stroke patients were recruited for three experimental conditions, i.e., motor attempt (MA) condition, tactile stimulation (TS) condition, and tactile stimulation-assisted motor attempt (TS-MA) condition. Tactile stimulation was delivered to the paretic hand wrist during both task and idle states using a DC vibrator. Results: We observed that the TS-MA condition achieved greater motor-related cortical activation (MRCA) in alpha-beta band when compared with both TS and MA conditions. Consequently, online BCI decoding accuracies between task and idle states were significantly improved from 74.5% in the MA condition to 85.1% in the TS-MA condition (p < 0.001), whereas the accuracy in the TS condition was 54.6% (approaching to the chance level of 50%). Conclusion: This finding demonstrates that sensory afferent from peripheral nerves benefits the neural process of sensorimotor cortex in stroke patients. With appropriate sensory stimulation, MRCA is enhanced and corresponding brain patterns are more discriminative. Significance: This novel SMR-BCI paradigm shows great promise to facilitate the practical application of BCI-based stroke rehabilitation.
Del Vecchio A, Ubeda A, Sartori M, et al., 2018, The central nervous system modulates the neuromechanical delay in a broad range for the control of muscle force, Journal of Applied Physiology, Vol: 125, Pages: 1404-1410, ISSN: 8750-7587
Force is generated by muscle units according to the neural activation sent by motor neurons. The motor unit is therefore the interface between the neural coding of movement and the musculotendinous system. Here we propose a method to accurately measure the latency between an estimate of the neural drive to muscle and force. Further, we systematically investigate this latency, that we refer to as the neuromechanical delay (NMD), as a function of the rate of force generation. In two experimental sessions, eight men performed isometric finger abduction and ankle dorsiflexion sinusoidal contractions at three frequencies and peak-to-peak amplitudes [0.5,1,1.5 (Hz); 1,5,10 of maximal force (%MVC)], with a mean force of 10% MVC. The discharge timings of motor units of the first dorsal interosseous (FDI) and tibialis anterior (TA) muscle were identified by high-density surface EMG decomposition. The neural drive was estimated as the cumulative discharge timings of the identified motor units. The neural drive predicted 80 ± 0.4% of the force fluctuations and consistently anticipated force by 194.6 ± 55 ms (average across conditions and muscles). The NMD decreased non-linearly with the rate of force generation (R2 = 0.82 ± 0.07; exponential fitting) with a broad range of values (from 70 to 385 ms) and was 66 ± 0.01 ms shorter for the FDI than TA (P<0.001). In conclusion, we provided a method to estimate the delay between the neural control and force generation and we showed that this delay is muscle-dependent and is modulated within a wide range by the central nervous system.
Del Vecchio A, Úbeda A, Sartori M, et al., 2018, Central nervous system modulates the neuromechanical delay in a broad range for the control of muscle force, Journal of Applied Physiology, Vol: 125, Pages: 1404-1410, ISSN: 8750-7587
Force is generated by muscle units according to the neural activation sent by motor neurons. The motor unit is therefore the interface between the neural coding of movement and the musculotendinous system. Here we propose a method to accurately measure the latency between an estimate of the neural drive to muscle and force. Furthermore, we systematically investigate this latency, which we refer to as the neuromechanical delay (NMD), as a function of the rate of force generation. In two experimental sessions, eight men performed isometric finger abduction and ankle dorsiflexion sinusoidal contractions at three frequencies and peak-to-peak amplitudes {0.5, 1, and 1.5 Hz; 1, 5, and 10 of maximal force [%maximal voluntary contraction (MVC)]}, with a mean force of 10% MVC. The discharge timings of motor units of the first dorsal interosseous (FDI) and tibialis anterior (TA) muscle were identified by high-density surface EMG decomposition. The neural drive was estimated as the cumulative discharge timings of the identified motor units. The neural drive predicted 80 ± 0.4% of the force fluctuations and consistently anticipated force by 194.6 ± 55 ms (average across conditions and muscles). The NMD decreased nonlinearly with the rate of force generation ( R2 = 0.82 ± 0.07; exponential fitting) with a broad range of values (from 70 to 385 ms) and was 66 ± 0.01 ms shorter for the FDI than TA ( P < 0.001). In conclusion, we provided a method to estimate the delay between the neural control and force generation, and we showed that this delay is muscle-dependent and is modulated within a wide range by the central nervous system. NEW & NOTEWORTHY The motor unit is a neuromechanical interface that converts neural signals into mechanical force with a delay determined by neural and peripheral properties. Classically, this delay has been assessed from the muscle resting level or d
Martinez-Valdes E, Farina D, Negro F, et al., 2018, Early motor unit conduction velocity changes to high-intensity interval training versus continuous training, Medicine and Science in Sports and Exercise, Vol: 50, Pages: 2339-2350, ISSN: 0195-9131
Purpose Moderate-intensity continuous training (MICT) and high-intensity interval training (HIIT) are associated with different adjustments in motor output. Changes in motor unit (MU) peripheral properties may contribute to these adjustments, but this is yet to be elucidated. This study evaluated early changes in MU conduction velocity (MUCV) and MU action potential amplitude after 2 wk of either HIIT or MICT.Methods Sixteen men were assigned to either an MICT group or HIIT group (n = 8 each), and participated in six training sessions over 14 d. HIIT: 8 to 12 × 60-s intervals at 100% peak power output. Moderate-intensity continuous training: 90 to 120 min continuous cycling at ~65% V˙O2peak. Preintervention and postintervention, participants performed maximal voluntary contractions (MVC) and submaximal (10%, 30%, 50%, and 70% of MVC) isometric knee extensions while high-density EMG was recorded from the vastus medialis (VM) and vastus lateralis (VL) muscles. The high-density EMG was decomposed into individual MU by convolutive blind-source separation and tracked preintervention and postintervention.Results Both training interventions induced changes in MUCV, but these changes depended on the type of training (P < 0.001). The HIIT group showed higher values of MUCV after training at all torque levels (P < 0.05), MICT only displayed changes in MUCV at low torque levels (10%–30% MVC, P < 0.002). There were no changes in MU action potential amplitude for either group (P = 0.2).Conclusions Two weeks of HIIT or MICT elicit differential changes in MUCV, likely due to the contrasting load and volume used in such training regimes. This new knowledge on the neuromuscular adaptations to training has implications for exercise prescription.
De Nunzio AM, Schweisfurth MA, Ge N, et al., 2018, Relieving phantom limb pain with multimodal sensory-motor training, Journal of Neural Engineering, Vol: 15, ISSN: 1741-2552
Objective. The causes for the disabling condition of phantom limb pain (PLP), affecting 85% of amputees, are so far unknown, with few effective treatments available. Sensory feedback based strategies to normalize the motor commands to control the phantom limb offer important targets for new effective treatments as the correlation between phantom limb motor control and sensory feedback from the motor intention has been identified as a possible mechanism for PLP development. Approach. Ten upper-limb amputees, suffering from chronic PLP, underwent 16 days of intensive training on phantom-limb movement control. Visual and tactile feedback, driven by muscular activity at the stump, was provided with the aim of reducing PLP intensity. Main results. A 32.1% reduction of PLP intensity was obtained at the follow-up (6 weeks after the end of the training, with an initial 21.6% reduction immediately at the end of the training) reaching clinical effectiveness for chronic pain reduction. Multimodal sensory-motor training on phantom-limb movements with visual and tactile feedback is a new method for PLP reduction. Significance. The study results revealed a substantial reduction in phantom limb pain intensity, obtained with a new training protocol focused on improving phantom limb motor output using visual and tactile feedback from the stump muscular activity executed to move the phantom limb.
Sartori M, Farina D, 2018, Estimation of Phantom Limb Musculoskeletal Mechanics after Targeted Muscle Reinnervation: Towards Online Model-Based Control of Myoelectric Bionic Limbs, Pages: 126-130, ISSN: 2155-1774
Upper limb loss substantially impacts on the quality of life of thousands of individuals worldwide. Current advanced treatments rely on myoelectric prostheses controlled by electromyograms (EMG). Despite advances in surgical procedures (i.e. Targeted muscle reinnervation) as well as in electrode design and bio-electric signal sampling, current myocontrol schemes provide limited re-gain of functionality and lack of bio-mimesis. Current solutions create mappings between EMG and prosthesis joint angles, disregarding the underlying neuromusculoskeletal processes. The poor performance of these approaches determines high rejection rates (40-50%) of myoelectric bionic limbs. This paper presents a biomimetic paradigm for active prosthesis control. It encompasses a modelling formulation that simulates the amputee's phantom limb musculoskeletal dynamics as controlled by high-density EMG-extracted neural activations to muscles. We demonstrate how this technique can be applied to a transhumeral amputee offline to decode musculoskeletal function in the phantom elbow and wrist offline. Moreover, we provide preliminary data showing how this technique can be operated online on intact-limbed individuals. The proposed paradigm represents an important step towards next-generation bionic limbs that can mimic human biological limb functionality and robustness.
Schmalfuss L, Hahne J, Farina D, et al., 2018, A hybrid auricular control system: direct, simultaneous, and proportional myoelectric control of two degrees of freedom in prosthetic hands, Journal of Neural Engineering, Vol: 15, ISSN: 1741-2552
Objective. The conventional myoelectric control scheme of hand prostheses provides a high level of robustness during continuous use. Typically, the electrical activity of an agonist/antagonist muscle pair in the forearm is detected and used to control either opening/closing or rotation of the prosthetic hand. The translation of more sophisticated control approaches (e.g. regression-based classifiers) to clinical practice is limited mainly because of their lack of robustness in real-world conditions (e.g. due to different arm positions). We therefore explore a new hybrid approach, in which a second degree of freedom (DOF) controlled by the myoelectric activity of the posterior auricular muscles is added to the conventional forearm control. With this, an independent, simultaneous and proportional control of rotation and opening/closing of the hand is possible. Approach. In this study, we compared the hybrid auricular control system (hACS) to the two most commonly used control techniques for two DOF. Ten able-bodied subjects and one person with transradial amputation performed two standardizes tests in three different arm positions. Main results. Subjects controlled a hand prosthesis significantly more rapidly and more accurately using the hACS. Moreover, the robustness of the system was not influenced by different arm positions. Significance. The hACS therefore offers an alternative solution for simultaneous and proportional myoelectric control of two degrees of freedom that avoids several robustness issues related to machine learning based approaches.
Castronovo AM, Mrachacz-Kersting N, Stevenson AJT, et al., 2018, Decrease in force steadiness with aging is associated with increased power of the common but not independent input to motor neurons., J Neurophysiol, Vol: 120, Pages: 1616-1624
Declines in motor function with advancing age have been attributed to changes occurring at all levels of the neuromuscular system. However, the impact of aging on the control of muscle force by spinal motor neurons is not yet understood. In this study on 20 individuals aged between 24 and 75 yr (13 men, 7 women), we investigated the common synaptic input to motor neurons of the tibialis anterior muscle and its impact on force control. Motor unit discharge times were identified from high-density surface EMG recordings during isometric contractions at forces of 20% of maximal voluntary effort. Coherence analysis between motor unit spike trains was used to characterize the input to motor neurons. The decrease in force steadiness with age ( R2 = 0.6, P < 0.01) was associated with an increase in the amplitude of low-frequency oscillations of functional common synaptic input to motor neurons ( R2 = 0.59; P < 0.01). The relative proportion of common input to independent noise at low frequencies increased with variability (power) in common synaptic input. Moreover, variability in interspike interval did not change and strength of the common input in the gamma band decreased with age ( R2 = 0.22; P < 0.01). The findings indicate that age-related reduction in the accuracy of force control is associated with increased common fluctuations to motor neurons at low frequencies and not with an increase in independent synaptic input. NEW & NOTEWORTHY The influence of aging on the role of spinal motor neurons in accurate force control is not yet understood. We demonstrate that aging is associated with increased oscillations in common input to motor neurons at low frequencies and with a decrease in the relative strength of gamma oscillations. These results demonstrate that the synaptic inputs to motor neurons change across the life span and contribute to a decline in force control.
Del Vecchio A, Negro F, Falla D, et al., 2018, Higher muscle fiber conduction velocity and early rate of torque development in chronically strength-trained individuals, Journal of Applied Physiology, Vol: 125, Pages: 1218-1226, ISSN: 8750-7587
Strength-trained individuals (ST) develop greater levels of force compared with untrained subjects. These differences are partly of neural origin and can be explained by training-induced changes in the neural drive to the muscles. In the present study we hypothesize a greater rate of torque development (RTD) and faster recruitment of motor units with greater muscle fiber conduction velocity (MFCV) in ST compared with a control cohort. MFCV was assessed during maximal voluntary isometric explosive contractions of the elbow flexors in eight ST and eight control individuals. MFCV was estimated from high-density surface electromyogram recordings (128 electrodes) in intervals of 50 ms starting from the onset of the electromyogram. RTD and MFCV were computed and normalized to their maximal voluntary torque (MVT) values. The explosive torque of the ST was greater than in the control group in all time intervals analyzed (P < 0.001). The absolute MFCV values were also greater for the ST than for controls at all time intervals (P < 0.001). ST also achieved greater normalized RTD in the first 50 ms of contraction [887.6 (152) vs. 568.5 (148.66)%MVT/s, mean (SD), P < 0.001] and normalized MFCV before the rise in force compared with controls. We have shown for the first time that ST can recruit motor units with greater MFCV in a shorter amount of time compared with untrained subjects during maximal voluntary isometric explosive contractions.
Markovic M, Schweisfurth MA, Engels LF, et al., 2018, Myocontrol is closed-loop control: Incidental feedback is sufficient for scaling the prosthesis force in routine grasping, Journal of NeuroEngineering and Rehabilitation, Vol: 15, ISSN: 1743-0003
Background: Sensory feedback is critical for grasping in able-bodied subjects. Consequently, closing the loop in upper-limb prosthetics by providing artificial sensory feedback to the amputee is expected to improve the prosthesis utility. Nevertheless, even though amputees rate the prospect of sensory feedback high, its benefits in daily life are still very much debated. We argue that in order to measure the potential functional benefit of artificial sensory feedback, the baseline open-loop performance needs to be established. Methods: The myoelectric control of naïve able-bodied subjects was evaluated during modulation of electromyographic signals (EMG task), and grasping with a prosthesis (Prosthesis task). The subjects needed to activate the wrist flexor muscles and close the prosthesis to reach a randomly selected target level (routine grasping). To assess the baseline performance, the tasks were performed with a different extent of implicit feedback (proprioception, prosthesis motion and sound). Finally, the prosthesis task was repeated with explicit visual force feedback. The subjects' ability to scale the prosthesis command/force was assessed by testing for a statistically significant increase in the median of the generated commands/forces between neighboring levels. The quality of control was evaluated by computing the median absolute error (MAE) with respect to the target. Results: The subjects could successfully scale their motor commands and generated prosthesis forces across target levels in all tasks, even with the least amount of implicit feedback (only muscle proprioception, EMG task). In addition, the deviation of the generated commands/forces from the target levels decreased with additional feedback. However, the increase in implicit feedback, from proprioception to prosthesis motion and sound, seemed to have a more substantial effect than the final introduction of explicit feedback. Explicit feedback improved the performance mainly at the high
Patel GK, Castellini C, Hahne JM, et al., 2018, A classification method for myoelectric control of hand prostheses inspired by muscle coordination, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 26, Pages: 1745-1755, ISSN: 1534-4320
© 2018 IEEE. Dexterous upper limb myoelectric prostheses can, to some extent, restore the motor functions lost after an amputation. However, ensuring the reliability of myoelectric control is still an open challenge. In this paper, we propose a classification method that exploits the regularity in muscle activation patterns (uniform scaling) across different force levels within a given movement class. This assumption leads to a simple training procedure, using training data collected at single contraction intensity for each movement class. The proposed method was compared to the widely accepted benchmark [linear discriminant analysis (LDA) classifier] using off-line and online evaluation. The off-line classification errors obtained with the new method were either lower or higher than LDA depending upon the chosen feature set. In the online evaluation, the new classification method was operated using amplitude-EMG features and compared to the state-of-the-art LDA classifier combined with the time domain feature set. The online evaluation was performed in 11 able-bodied and one amputee subject using a set of four functional tasks mimicking daily-life activities. The tasks assessed the dexterity (e.g., switching between functions) and robustness of control (e.g., handling heavy objects). With the new classification scheme, the amputee performed better in all functional tasks, whereas the able-bodied subjects performed significantly better in three out of four functional tasks. Overall, the novel method outperformed the state-of-the-art approach (LDA) while utilizing less training data and a smaller feature set. The proposed method is, therefore, a simple but effective and robust classification scheme, convenient for online implementation and clinical use.
Farina D, Yao, Sheng, et al., 2018, Decoding covert somatosensory attention by a BCI system calibrated with tactile sensation, IEEE Transactions on Biomedical Engineering, Vol: 65, Pages: 1689-1695, ISSN: 0018-9294
Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation. Methods: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real sensation) and somatosensory attentional orientation (SAO) provides a way to decode covert somatic attention. Subjects were instructed to sense the tactile stimulation, which was applied to the left (SS-L) or the right (SS-R) wrist. The brain-computer interface (BCI) system was calibrated with the sensation data and then applied for online SAO decoding. Results: Both SS and SAO showed oscillatory activation concentrated on the contralateral somatosensory hemisphere. Offline analysis showed that the proposed calibration method led to a greater accuracy than the traditional calibration method based on SAO only. This is confirmed by online experiments, where the online accuracy on 15 subjects was 78.8 ± 13.1%, with 12 subjects >70% and 4 subject >90%. Conclusion: By integrating the stimulus-induced oscillatory dynamics from sensory cortex, covert somatosensory attention can be reliably decoded by a BCI system calibrated with tactile sensation. Significance: Indeed, real tactile sensation is more consistent during calibration than SAO. This brain-computer interfacing approach may find application for stroke and completely locked-in patients with preserved somatic sensation.
ur Rehman MZ, Waris A, Gilani SO, et al., 2018, Multiday EMG-Based classification of hand motions with deep learning techniques, Sensors (Switzerland), Vol: 18, ISSN: 1424-8220
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with respect to current clinical approaches based on direct control. However, the choice of features for classification is challenging and impacts long-term performance. Here, we propose the use of EMG raw signals as direct inputs to deep networks with intrinsic feature extraction capabilities recorded over multiple days. Seven able-bodied subjects performed six active motions (plus rest), and EMG signals were recorded for 15 consecutive days with two sessions per day using the MYO armband (MYB, a wearable EMG sensor). The classification was performed by a convolutional neural network (CNN) with raw bipolar EMG samples as the inputs, and the performance was compared with linear discriminant analysis (LDA) and stacked sparse autoencoders with features (SSAE-f) and raw samples (SSAE-r) as inputs. CNN outperformed (lower classification error) both LDA and SSAE-r in the within-session, between sessions on same day, between the pair of days, and leave-out one-day evaluation (p < 0.001) analyses. However, no significant difference was found between CNN and SSAE-f. These results demonstrated that CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features. This data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.
Yao L, Mrachacz-Kersting N, Sheng X, et al., 2018, A Multi-Class BCI Based on Somatosensory Imagery, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 26, Pages: 1508-1515, ISSN: 1534-4320
© 2018 IEEE. In this paper, we investigated the performance of a multi-class brain-computer interface (BCI). The BCI system is based on the concept of somatosensory attentional orientation (SAO), in which the user shifts and maintains somatosensory attention by imagining the sensation of tactile stimulation of a body part. At the beginning of every trial, a vibration stimulus (200 ms) informed the subjects to prepare for the task. Four SAO tasks were performed following randomly presented cues: SAO of the left hand (SAO-LF), SAO of the right hand (SAO-RT), bilateral SAO (SAO-BI), and SAO suppressed or idle state (SAO-ID). Analysis of the event-related desynchronization and synchronization (ERD/ERS) in the EEG indicated that the four SAO tasks had different somatosensory cortical activation patterns. SAO-LF and SAO-RT exhibited stronger contralateral ERD, whereas bilateral ERD activation was indicative of SAO-BI, and bilateral ERS activation was associated with SAO-ID. By selecting the frequency bands and/or optimal classes, classification accuracy of the system reached 85.2%±11.2% for two classes, 69.5%±16.2% for three classes, and 55.9%±15.8% for four classes. The results validated a multi-class BCI system based on SAO, on a single trial basis. Somatosensory attention to different body parts induces diverse oscillatory dynamics within the somatosensory area of the brain, and the proposed SAO paradigm provided a new approach for a multiple-class BCI that is potentially stimulus independent.
ur Rehman MZ, Gilani SO, Waris A, et al., 2018, Stacked sparse autoencoders for EMG-based classification of hand motions: A comparative multi day analyses between surface and intramuscular EMG, Applied Sciences (Switzerland), Vol: 8
© 2018 by the authors. Advances in myoelectric interfaces have increased the use of wearable prosthetics including robotic arms. Although promising results have been achieved with pattern recognition-based control schemes, control robustness requires improvement to increase user acceptance of prosthetic hands. The aim of this study was to quantify the performance of stacked sparse autoencoders (SSAE), an emerging deep learning technique used to improve myoelectric control and to compare multiday surface electromyography (sEMG) and intramuscular (iEMG) recordings. Ten able-bodied and six amputee subjects with average ages of 24.5 and 34.5 years, respectively, were evaluated using offline classification error as the performance matric. Surface and intramuscular EMG were concurrently recorded while each subject performed 11 hand motions. Performance of SSAE was compared with that of linear discriminant analysis (LDA) classifier. Within-day analysis showed that SSAE (1.38 ± 1.38%) outperformed LDA (8.09 ± 4.53%) using both the sEMG and iEMG data from both able-bodied and amputee subjects (p < 0.001). In the between-day analysis, SSAE outperformed LDA (7.19 ± 9.55% vs. 22.25 ± 11.09%) using both sEMG and iEMG data from both able-bodied and amputee subjects. No significant difference in performance was observed for within-day and pairs of days with eight-fold validation when using iEMG and sEMG with SSAE, whereas sEMG outperformed iEMG (p < 0.001) in between-day analysis both with two-fold and seven-fold validation schemes. The results obtained in this study imply that SSAE can significantly improve the performance of pattern recognition-based myoelectric control scheme and has the strength to extract deep information hidden in the EMG data.
Thompson CK, Negro F, Johnson MD, et al., 2018, Robust and accurate decoding of motoneuron behaviour and prediction of the resulting force output., J Physiol, Vol: 596, Pages: 2643-2659
KEY POINTS: The spinal alpha motoneuron is the only cell in the human CNS whose discharge can be routinely recorded in humans. We have reengineered motor unit collection and decomposition approaches, originally developed in humans, to measure the neural drive to muscle and estimate muscle force generation in the in vivo cat model. Experimental, computational, and predictive approaches are used to demonstrate the validity of this approach across a wide range of modes to activate the motor pool. The utility of this approach is shown through the ability to track individual motor units across trials, allowing for better predictions of muscle force than the electromyography signal, and providing insights in to the stereotypical discharge characteristics in response to synaptic activation of the motor pool. This approach now allows for a direct link between the intracellular data of single motoneurons, the discharge properties of motoneuron populations, and muscle force generation in the same preparation. ABSTRACT: The discharge of a spinal alpha motoneuron and the resulting contraction of its muscle fibres represents the functional quantum of the motor system. Recent advances in the recording and decomposition of the electromyographic signal allow for the identification of several tens of concurrently active motor units. These detailed population data provide the potential to achieve deep insights into the synaptic organization of motor commands. Yet most of our understanding of the synaptic input to motoneurons is derived from intracellular recordings in animal preparations. Thus, it is necessary to extend the new electrode and decomposition methods to recording of motor unit populations in these same preparations. To achieve this goal, we use high-density electrode arrays and decomposition techniques, analogous to those developed for humans, to record and decompose the activity of tens of concurrently active motor units in a hindlimb muscle in the in vivo cat. Our resu
Vujaklija I, Farina D, 2018, 3D printed upper limb prosthetics., Expert Rev Med Devices, Vol: 15, Pages: 505-512
INTRODUCTION: In the last 15 years, the market for prosthetic arms and hands has shifted toward systems with greater degrees of actuation. There has also been a progressive use of emerging technologies to overcome hardware design challenges. Moreover, the proliferation of rapid prototyping has resulted in applications in the prosthetic market. Even though there are concerns on robustness and wide-user acceptance, the affordable and customizable solution offered by rapid prototyping, combined with the possibility for easy maintenance and repair, is very attractive for prosthesis design. Areas covered: Functional layouts for multi-articulated, dexterous 3D printed hands and sockets are freely available, with many patients using them at home. We provide an overview of the current solutions, compare their features, and discuss their potential impact on the field of prosthetics. Expert commentary: The high level of low-cost customization is an appealing concept, but this comes with challenges not yet systematically addressed; such challenges include durability, sufficient grip strength, reproducibility, and general appeal to the wide range of users. The introduction of new printable materials could assist in overcoming some of these issues, but present an added risk of compromising the low cost and wide availability.
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