Cortical activations associated with spatial remapping of finger touch using EEG.

The spatial coding of tactile information is functionally essential for touch-based shape perception and motor control. However, the spatiotemporal dynamics of how tactile information is remapped from the somatotopic reference frame in the primary somatosensory cortex to the spatiotopic reference frame remains unclear. This study investigated how hand position in space or posture influences cortical somatosensory processing. Twenty-two healthy subjects received electrical stimulation to the right thumb (D1) or little finger (D5) in three position conditions: palm down on right side of the body (baseline), hand crossing the body midline (effect of position), and palm up (effect of posture). Somatosensory-evoked potentials (SEPs) were recorded using electroencephalography. One early-, two mid-, and two late-latency neurophysiological components were identified for both fingers: P50, P1, N125, P200, and N250. D1 and D5 showed different cortical activation patterns: compared with baseline, the crossing condition showed significant clustering at P1 for D1, and at P50 and N125 for D5; the change in posture showed a significant cluster at N125 for D5. Clusters predominated at centro-parietal electrodes. These results suggest that tactile remapping of fingers after electrical stimulation occurs around 100-125 ms in the parietal cortex.

Towards humanlike grasp in robotic hands: mechanical implementation of force synergies.

In the field of robotic hands, finger force coordination is usually achieved by complex mechanical structures and control systems. This study presents the design of a novel transmission system inspired from the physiological concept of force synergies, aiming to simplify the control of multifingered robotic hands. To this end, we collected human finger force data during six isometric grasping tasks, and force synergies (i.e. the synergy weightings and the corresponding activation coefficients) were extracted from the concatenated force data to explore their potential for force modulation. We then implemented two force synergies with a cable-driven transmission mechanism consisting of two spring-loaded sliders and five V-shaped bars. Specifically, we used fixed synergy weightings to determine the stiffness of the compression springs, and the displacements of sliders were determined by time-varying activation coefficients. The derived transmission system was then used to drive a five-finger robotic hand named SYN hand. We also designed a motion encoder to selectively activate desired fingers, making it possible for two motors to empower a variety of hand postures. Experiments on the prototype demonstrate successful grasp of a wide range of objects in everyday life, and the finger force distribution of SYN hand can approximate that of human hand during six typical tasks. To our best knowledge, this study shows the first attempt to mechanically implement force synergies for finger force modulation in a robotic hand. In comparison to state-of-the-art robotic hands with similar functionality, the proposed hand can distribute humanlike force ratios on the fingers by simple position control, rather than resorting to additional force sensors or complex control strategies. The outcome of this study may provide alternatives for the design of novel anthropomorphic robotic hands, and thus show application prospects in the field of hand prostheses and exoskeletons.

Tactile shape discrimination for moving stimuli.

In this study, we explored spatial-temporal dependencies and their impact on the tactile perception of moving objects. Building on previous research linking visual perception and human movement, we examined if an imputed motion mechanism operates within the tactile modality. We focused on how biological coherence between space and time, characteristic of human movement, influences tactile perception. An experiment was designed wherein participants were stimulated on their right palm with tactile patterns, either ambiguous (incongruent conditions) or non-ambiguous (congruent conditions) relative to a biological motion law (two-thirds power law) and asked to report perceived shape and associated confidence. Our findings reveal that introducing ambiguous tactile patterns (1) significantly diminishes tactile discrimination performance, implying motor features of shape recognition in vision are also observed in the tactile modality, and (2) undermines participants' response confidence, uncovering the accessibility degree of information determining the tactile percept's conscious representation. Analysis based on the Hierarchical Drift Diffusion Model unveiled the sensitivity of the evidence accumulation process to the stimulus's informational ambiguity and provides insight into tactile perception as predictive dynamics for reducing uncertainty. These discoveries deepen our understanding of tactile perception mechanisms and underscore the criticality of predictions in sensory information processing.

Grasping objects with the aid of haptics.

A smart suction cup uses haptics to supplement vision for exploration of objects in a grasping task.

Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals.

Objective.Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on the skin. sEMG is the state-of-the-art method used to control active upper limb prostheses because of the association between its amplitude and the neural drive sent from the spinal cord to muscles. However, accurately estimating the kinematics of a freely moving human hand using sEMG from extrinsic hand muscles remains a challenge. Deep learning has been recently successfully applied to this problem by mapping raw sEMG signals into kinematics. Nonetheless, the optimal number of EMG signals and the type of pre-processing that would maximize performance have not been investigated yet.Approach.Here, we analyze the impact of these factors on the accuracy in kinematics estimates. For this purpose, we processed monopolar sEMG signals that were originally recorded from 320 electrodes over the forearm muscles of 13 subjects. We used a previously published deep learning method that can map the kinematics of the human hand with real-time resolution.Main results.While myocontrol algorithms essentially use the temporal envelope of the EMG signal as the only EMG feature, we show that our approach requires the full bandwidth of the signal in the temporal domain for accurate estimates. Spatial filtering however, had a smaller impact and low-order spatial filters may be suitable. Moreover, reducing the number of channels by ablation resulted in large performance losses. The highest accuracy was reached with the highest number of available sensors (n = 320). Importantly and unexpected, our results suggest that increasing the number of channels above those used in this study may further enhance the accuracy in predicting the kinematics of the human hand.Significance.We conclude that full bandwidth high-density EMG systems of hundreds of electrodes are needed for accurate kinematic estimates of the human hand.

i-MYO: A multi-grasp prosthetic hand control system based on gaze movements, augmented reality, and myoelectric signals.

BACKGROUND: Controlling a multi-grasp prosthetic hand still remains a challenge. This study explores the influence of merging gaze movements and augmented reality in bionics on improving prosthetic hand control. METHODS: A control system based on gaze movements, augmented reality, and myoelectric signals (i-MYO) was proposed. In the i-MYO, the GazeButton was introduced into the controller to detect the grasp-type intention from the eye-tracking signals, and the proportional velocity scheme based on the i-MYO was used to control hand movement. RESULTS: The able-bodied subjects with no prior training successfully transferred objects in 91.6% of the cases and switched the optimal grasp types in 97.5%. The patient could successfully trigger the EMG to control the hand holding the objects in 98.7% of trials in around 3.2 s and spend around 1.3 s switching the optimal grasp types in 99.2% of trials. CONCLUSIONS: Merging gaze movements and augmented reality in bionics can widen the control bandwidth of prosthetic hand. With the help of i-MYO, the subjects can control a prosthetic hand using six grasp types if they can manipulate two muscle signals and gaze movement.

Effects of finger pinch motor imagery on short-latency afferent inhibition and corticospinal excitability.

Motor imagery is a cognitive process involving the simulation of motor actions without actual movements. Despite the reported positive effects of motor imagery training on motor function, the underlying neurophysiological mechanisms have yet to be fully elucidated. Therefore, the purpose of the present study was to investigate how sustained tonic finger-pinching motor imagery modulates sensorimotor integration and corticospinal excitability using short-latency afferent inhibition (SAI) and single-pulse transcranial magnetic stimulation (TMS) assessments, respectively. Able-bodied individuals participated in the study and assessments were conducted under two experimental conditions in a randomized order between participants: (1) participants performed motor imagery of a pinch task while observing a visual image displayed on a monitor (Motor Imagery), and (2) participants remained at rest with their eyes fixed on the monitor displaying a cross mark (Control). For each condition, sensorimotor integration and corticospinal excitability were evaluated during sustained tonic motor imagery in separate sessions. Sensorimotor integration was assessed by SAI responses, representing inhibition of motor-evoked potentials (MEPs) in the first dorsal interosseous muscle elicited by TMS following median nerve stimulation. Corticospinal excitability was assessed by MEP responses elicited by single-pulse TMS. There was no significant difference in the magnitude of SAI responses between motor imagery and Control conditions, while MEP responses were significantly facilitated during the Motor Imagery condition compared to the Control condition. These findings suggest that motor imagery facilitates corticospinal excitability, without altering sensorimotor integration, possibly due to insufficient activation of the somatosensory circuits or lack of afferent feedback during sustained tonic motor imagery.

Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types.

Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone's intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the same grasp, and these combinations could differ among subjects, and even among the trials done by the same subject. In this work, 22 healthy subjects performed seven representative grasp types (the most commonly used). sEMG signals were recorded from seven representative forearm spots identified in a previous work. Intra- and intersubject variability are presented by using four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. The results confirmed the presence of both intra- and intersubject variability, which evidences the existence of distinct, yet limited, muscle patterns while executing the same grasp. This work underscores the importance of utilizing diverse combinations of sEMG features or characteristics of various natures, such as time-domain or frequency-domain, and it is the first work to observe the effect of considering different muscular patterns during grasps execution. This approach is applicable for fine-tuning the control settings of current sEMG devices.

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies.

Objective.We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Approach.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects.Main results.We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p< 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals.Significance.We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.

Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications.

This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.

Multisensory perception depends on the reliability of the type of judgment.

The brain engages the processes of multisensory integration and recalibration to deal with discrepant multisensory signals. These processes consider the reliability of each sensory input, with the more reliable modality receiving the stronger weight. Sensory reliability is typically assessed via the variability of participants' judgments, yet these can be shaped by factors both external and internal to the nervous system. For example, motor noise and participant's dexterity with the specific response method contribute to judgment variability, and different response methods applied to the same stimuli can result in different estimates of sensory reliabilities. Here we ask how such variations in reliability induced by variations in the response method affect multisensory integration and sensory recalibration, as well as motor adaptation, in a visuomotor paradigm. Participants performed center-out hand movements and were asked to judge the position of the hand or rotated visual feedback at the movement end points. We manipulated the variability, and thus the reliability, of repeated judgments by asking participants to respond using either a visual or a proprioceptive matching procedure. We find that the relative weights of visual and proprioceptive signals, and thus the asymmetry of multisensory integration and recalibration, depend on the reliability modulated by the judgment method. Motor adaptation, in contrast, was insensitive to this manipulation. Hence, the outcome of multisensory binding is shaped by the noise introduced by sensorimotor processing, in line with perception and action being intertwined.NEW & NOTEWORTHY Our brain tends to combine multisensory signals based on their respective reliability. This reliability depends on sensory noise in the environment, noise in the nervous system, and, as we show here, variability induced by the specific judgment procedure.

Understanding the capacity of children with congenital unilateral below-elbow deficiency to actuate their affected muscles.

In recent years, commercially available dexterous upper limb prostheses for children have begun to emerge. These devices derive control signals from surface electromyography (measure of affected muscle electrical activity, sEMG) to drive a variety of grasping motions. However, the ability for children with congenital upper limb deficiency to actuate their affected muscles to achieve naturalistic prosthetic control is not well understood, as compared to adults or children with acquired hand loss. To address this gap, we collected sEMG data from 9 congenital one-handed participants ages 8-20 years as they envisioned and attempted to perform 10 different movements with their missing hands. Seven sEMG electrodes were adhered circumferentially around the participant's affected and unaffected limbs and participants mirrored the attempted missing hand motions with their intact side. To analyze the collected sEMG data, we used time and frequency domain analyses. We found that for the majority of participants, attempted hand movements produced detectable and consistent muscle activity, and the capacity to achieve this was not dissimilar across the affected and unaffected sides. These data suggest that children with congenital hand absence retain a degree of control over their affected muscles, which has important implications for translating and refining advanced prosthetic control technologies for children.

Effects of noninvasive neuromodulation targeting the spinal cord on early learning of force control by the digits.

AIMS: Control of finger forces underlies our capacity for skilled hand movements acquired during development and reacquired after neurological injury. Learning force control by the digits, therefore, predicates our functional independence. Noninvasive neuromodulation targeting synapses that link corticospinal neurons onto the final common pathway via spike-timing-dependent mechanisms can alter distal limb motor output on a transient basis, yet these effects appear subject to individual differences. Here, we investigated how this form of noninvasive neuromodulation interacts with task repetition to influence early learning of force control during precision grip. METHODS: The unique effects of neuromodulation, task repetition, and neuromodulation coinciding with task repetition were tested in three separate conditions using a within-subject, cross-over design (n = 23). RESULTS: We found that synchronizing depolarization events within milliseconds of stabilizing precision grip accelerated learning but only after accounting for individual differences through inclusion of subjects who showed upregulated corticospinal excitability at 2 of 3 time points following conditioning stimulation (n = 19). CONCLUSIONS: Our findings provide insights into how the state of the corticospinal system can be leveraged to drive early motor skill learning, further emphasizing individual differences in the response to noninvasive neuromodulation. We interpret these findings in the context of biological mechanisms underlying the observed effects and implications for emerging therapeutic applications.

Bi-Manual Sensory Discrimination: A Kinesthetic Study.

The ability of humans to perceive and differentiate kinesthetic sensory information significantly influences our daily activities and motor control. This study examines the impact of asynchronous bi-manual discrimination tasks compared to uni-manual discrimination tasks on kinesthetic perception. Our study aims to reveal the relationship between kinesthetic perception of haptic signals by examining perceptual thresholds in three different scenarios using (i) the dominant hand, (ii) the non-dominant hand, and (iii) both hands simultaneously to differentiate between two successive signals. Subjects exposed to force signals in these three situations conveyed their perceptions of alterations in signal magnitude after each trial. Subsequently, we applied psychometric functions to the collected responses to determine perceptual thresholds. Our results indicate a substantial difference in threshold values between bi-manual and uni-manual scenarios, with the bi-manual scenario exhibiting higher thresholds, indicating inferior perceptual ability when both hands are simultaneously utilized in two separate discrimination tasks. Furthermore, our investigation reveals distinct perception thresholds between the dominant and non-dominant hands, owing to differences in the perceptual capability of the two hands. These findings provide substantial insight into how the nature of tasks may alter kinesthetic perception, offering implications for the development of haptic interfaces in practical applications.

A sensory-motor hand prosthesis with integrated thermal feedback.

BACKGROUND: Recently, we reported the presence of phantom thermal sensations in amputees: thermal stimulation of specific spots on the residual arm elicited thermal sensations in their missing hands. Here, we exploit phantom thermal sensations via a standalone system integrated into a robotic prosthetic hand to provide real-time and natural temperature feedback. METHODS: The subject (a male adult with unilateral transradial amputation) used the sensorized prosthesis to manipulate objects and distinguish their thermal properties. We tested his ability to discriminate between (1) hot, cold, and ambient temperature objects, (2) different materials (copper, glass, and plastic), and (3) artificial versus human hands. We also introduced the thermal box and block test (thermal BBT), a test to evaluate real-time temperature discrimination during standardized pick-and-place tasks. FINDINGS: The subject performed all three discrimination tasks above chance level with similar accuracies as with his intact hand. Additionally, in all 15 sessions of the thermal BBT, he correctly placed more than half of the samples. Finally, the phantom thermal sensation was stable during the 13 recording sessions spread over 400 days. CONCLUSION: Our study paves the way for more natural hand prostheses that restore the full palette of sensations. FUNDING: This work was funded by the Bertarelli Foundation (including the Catalyst program); the Swiss National Science Foundation through the National Centre of Competence in Research (NCCR) Robotics; the European Union's Horizon 2020 research and innovation program; the Horizon Europe Research & Innovation Program; the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP); and the Tuscany Health Ecosystem.

Prefrontal activation when suppressing an automatic balance recovery step.

BACKGROUND: The present study investigated neural mechanisms for suppressing a highly automatic balance recovery step. Response inhibition has typically been researched using focal hand reaction tasks performed by seated participants, and this has revealed a neural stopping network including the Inferior Frontal Gyrus (IFG). It is unclear if the same neural networks contribute to suppressing an unwanted balance reaction. RESEARCH QUESTION: Is there greater IFG activation when suppressing an automatic balance recovery step? METHODS: Functional near-infrared spectroscopy (fNIRS) was used to measure brain activity in 21 young adults as they performed a balance recovery task that demanded rapid step suppression following postural perturbation. The hypothesis was that the IFG would show heightened activity when suppressing an automatic balance recovery step. A lean and-release system was used to impose temporally unpredictable forward perturbations by releasing participants from a supported forward lean. For most trials (80%), participants were told to recover balance by quickly stepping forward (STEP). However, on 20% of trials at random, a high-pitch tone was played immediately after postural perturbation signaling participants to suppress a step and fully relax into a catch harness (STOP). This allowed us to target the ability to cancel an already initiated step in a balance recovery context. Average oxygenated hemoglobin changes were contrasted between STEP and STOP trials, 1-6 s post perturbation. RESULTS: The results showed a greater bilateral prefrontal response during STOP trials, supporting the idea that executive brain networks are active when suppressing a balance recovery step. SIGNIFICANCE: Our study demonstrates one way in which higher brain processes may help us prevent falls in complex environments where behavioral flexibility is necessary. This study also presents a novel method for assessing response inhibition in an upright postural context where rapid stepping reactions are required.

Roles of Handedness and Hemispheric Lateralization: Implications for Rehabilitation of the Central and Peripheral Nervous Systems: A Rapid Review.

IMPORTANCE: Handedness and motor asymmetry are important features of occupational performance. With an increased understanding of the basic neural mechanisms surrounding handedness, clinicians will be better able to implement targeted, evidence-based neurorehabilitation interventions to promote functional independence. OBJECTIVE: To review the basic neural mechanisms behind handedness and their implications for central and peripheral nervous system injury. DATA SOURCES: Relevant published literature obtained via MEDLINE. FINDINGS: Handedness, along with performance asymmetries observed between the dominant and nondominant hands, may be due to hemispheric specializations for motor control. These specializations contribute to predictable motor control deficits that are dependent on which hemisphere or limb has been affected. Clinical practice recommendations for occupational therapists and other rehabilitation specialists are presented. CONCLUSIONS AND RELEVANCE: It is vital that occupational therapists and other rehabilitation specialists consider handedness and hemispheric lateralization during evaluation and treatment. With an increased understanding of the basic neural mechanisms surrounding handedness, clinicians will be better able to implement targeted, evidence-based neurorehabilitation interventions to promote functional independence. Plain-Language Summary: The goal of this narrative review is to increase clinicians' understanding of the basic neural mechanisms related to handedness (the tendency to select one hand over the other for specific tasks) and their implications for central and peripheral nervous system injury and rehabilitation. An enhanced understanding of these mechanisms may allow clinicians to better tailor neurorehabilitation interventions to address motor deficits and promote functional independence.

Optical Myography-Based Sensing Methodology of Application of Random Loads to Muscles during Hand-Gripping Training.

Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein's idea of "repetition without repetition" suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscles when performing physical tasks. We assumed that the time-variability and periodicity of the applied load appear in the time-series feature of muscle deformation data. In the experiment, 14 participants conducted the gripping tasks with a gripper, ball, balloon, Palm clenching, and paper. Crumpling pieces of paper (paper exercise) involves randomness because the resistance force of the paper changes depending on the shape and layers of the paper. Optical myography during gripping tasks was measured, and time-series features were analyzed. As a result, our system could detect the random movement of muscles during training.

A Circular, Wireless Surface-Electromyography Array.

Commercial, high-tech upper limb prostheses offer a lot of functionality and are equipped with high-grade control mechanisms. However, they are relatively expensive and are not accessible to the majority of amputees. Therefore, more affordable, accessible, open-source, and 3D-printable alternatives are being developed. A commonly proposed approach to control these prostheses is to use bio-potentials generated by skeletal muscles, which can be measured using surface electromyography (sEMG). However, this control mechanism either lacks accuracy when a single sEMG sensor is used or involves the use of wires to connect to an array of multiple nodes, which hinders patients' movements. In order to mitigate these issues, we have developed a circular, wireless s-EMG array that is able to collect sEMG potentials on an array of electrodes that can be spread (not) uniformly around the circumference of a patient's arm. The modular sEMG system is combined with a Bluetooth Low Energy System on Chip, motion sensors, and a battery. We have benchmarked this system with a commercial, wired, state-of-the-art alternative and found an r = 0.98 (p < 0.01) Spearman correlation between the root-mean-squared (RMS) amplitude of sEMG measurements measured by both devices for the same set of 20 reference gestures, demonstrating that the system is accurate in measuring sEMG. Additionally, we have demonstrated that the RMS amplitudes of sEMG measurements between the different nodes within the array are uncorrelated, indicating that they contain independent information that can be used for higher accuracy in gesture recognition. We show this by training a random forest classifier that can distinguish between 6 gestures with an accuracy of 97%. This work is important for a large and growing group of amputees whose quality of life could be improved using this technology.