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Background:Brain Computer Interfaces (BCI) pair brain activity with visual and/or motor feedback. They may provide sorely needed training alternatives for patients with severely impaired upper limb after stroke, where treatment choices are limited and unsatisfactory.Objectives:To examine if BCI-based training is more effective in improving upper limb (UL) motor function than conventional therapy in the subacute phase after stroke and if patients with preserved cortical-spinal tract (CST) integrity benefit more from BCI training.Methods:Forty patients with stroke and severe UL paresis (< 13 on Action Research Arm Test (ARAT)) were included and randomized to either BCI training as part of their rehabilitation or conventional UL rehabilitation with a target of 12 BCI sessions. The sessions were conducted 3-4 times weekly for 3-4 weeks. Main endpoint was the ARAT at three months post stroke. Secondary outcome measures comprised Fugl-Meyer Motor Assessment UL and Functional Independence Measure. At baseline, Transcranial direct stimulation (TMS) was performed to examine CST integrity. EEG data were analyzed to see if there were changes in Event-related desynchronization (ERD) and lateralization during the course of treatment.Results: Data from 35 patients were available at three months follow-up. In general, few patients (10/35) improved above the minimally clinically important difference of 6 points on ARAT, in the BCI group 5/15, in control 5/20. An independent samples Mann-Whitney U test revealed no differences between the groups, p=0.382. CST lesion (MEP-) was a significant predictor of poor outcome, p=ConclusionsThough more patients in the BCI group improved, the difference was not significant. Larger studies including only patients with some preserved CST integrity should be attempted.

When using EMG biofeedback to control the grasping force of a myoelectric prosthesis, subjects need to activate their muscles and maintain the myoelectric signal within an appropriate interval. However, their performance decreases for higher forces, because the myoelectric signal is more variable for stronger contractions. Therefore, the present study proposes to implement EMG biofeedback using nonlinear mapping, in which EMG intervals of increasing size are mapped to equal-sized intervals of the prosthesis velocity. To validate this approach, 20 able-bodied subjects performed force-matching tasks using Michelangelo prosthesis with and without EMG biofeedback with linear and nonlinear mapping. Additionally, four transradial amputees performed a functional task in the same feedback and mapping conditions. The success rate in producing desired force was significantly higher with feedback (65.4±15.9%) compared to no feedback (46.2±14.9%) as well as when using nonlinear (62.4±16.8%) versus linear mapping (49.2±17.2%). Overall, in able-bodied subjects, the highest success rate was obtained when EMG biofeedback was combined with nonlinear mapping (72%), and the opposite for linear mapping with no feedback (39.6%). The same trend was registered also in four amputee subjects. Therefore, EMG biofeedback improved prosthesis force control, especially when combined with nonlinear mapping, which showed to be an effective approach to counteract increasing variability of myoelectric signal for stronger contractions.

Enhancing the perception of the surrounding environment by conveying information through tactile stimulation can improve performance, especially when other senses are already overloaded (e.g., first responders). Delivering electrotactile messages using spatiotemporal patterns has been suggested as an efficient and reliable approach to transmitting information through the skin. However, the effectiveness of this approach depends on the user’s capability to recognize tactile messages in potentially challenging scenarios (e.g., rescue operations). Therefore, the present study evaluated the participants’ performance in identifying electrotactile messages while they performed a parallel cognitive task. Thirty-six electrotactile messages were delivered through a 3 × 2 pad-matrix at the lateral torso in the baseline and multitasking conditions. The task-switching paradigm was employed as the parallel task, introducing cognitive interference to the electrotactile message recognition. The assumption was that the simultaneous task would impair the identification performance, but that this impairment could be minimized by decreasing the speed at which the message was delivered. The results indeed showed that the identification success rates decreased from ~83% in the baseline to ~61% during multitasking. Unexpectedly, the performance did not recover when the message duration was increased. Hence, modulating the message duration seems not to be an effective strategy to prevent lower performance while multitasking.

BACKGROUND: Biomechanical gait analyses are typically performed in laboratory settings, and are associated with limitations due to space, marker placement, and tasks that are not representative of the real-world usage of lower limb prostheses. Therefore, the purpose of this study was to investigate the possibility of accurately measuring gait parameters using embedded sensors in a microprocessor-controlled knee joint.METHODS: Ten participants were recruited for this study and equipped with a Genium X3 prosthetic knee joint. They performed level walking, stair/ramp descent, and ascent. During these tasks, kinematics and kinetics (sagittal knee and thigh segment angle, and knee moment) were recorded using an optical motion capture system and force plates (gold standard), as well as the prosthesis-embedded sensors. Root mean square errors, relative errors, correlation coefficients, and discrete outcome variables of clinical relevance were calculated and compared between the gold standard and the embedded sensors.FINDINGS: The average root mean square errors were found to be 0.6°, 5.3°, and 0.08 Nm/kg, for the knee angle, thigh angle, and knee moment, respectively. The average relative errors were 0.75% for the knee angle, 11.67% for the thigh angle, and 9.66%, for the knee moment. The discrete outcome variables showed small but significant differences between the two measurement systems for a number of tasks (higher differences only at the thigh).INTERPRETATION: The findings highlight the potential of prosthesis-embedded sensors to accurately measure gait parameters across a wide range of tasks. This paves the way for assessing prosthesis performance in realistic environments outside the lab.

The present work introduces preliminary re-sults of an EU H2020 project aiming to develop an un-obtrusive system to assist first responders by conveying additional information through electrotactile feedback. This study assessed three different encoding schemes for delivering tactile messages in form of spatial patterns. Patterns were delivered using simultaneous activation, sequential activation, and sequential activation with fre-quency modulation of pads. Biphasic pulses with a pulse duration of 400 μs and frequency of 25, 50 and 150 Hz were delivered through a surface electrode (2 × 3 matrix array) placed at the lateral torso. The success rate in iden-tification of electrotactile spatial patterns was quantified in healthy subjects. Simultaneous activation resulted in poor performance, improving with the sequential ap-proach. The best results were achieved using mixed en-coding (sequential activation plus frequency modulation) with success rates of approximately 80%. These prelimi-nary results suggest the feasibility of conveying and iden-tifying electrotactile spatial patterns at the lateral torso when the encoding scheme incorporates spatiotemporal stimulation profiles, rather than simultaneous electrode activation. These are encouraging results since reliable recognition of spatial patterns can enable high- bandwidth communication through tactile channels.

A semi-autonomous prosthesis control based on computer vision can be used to improve performance while decreasing the cognitive burden, especially when using advanced systems with multiple functions. However, a drawback of this approach is that it relies on the complex processing of a significant amount of data (e.g., a point cloud provided by a depth sensor), which can be a challenge when deploying such a system onto an embedded prosthesis controller. In the present study, therefore, we propose a novel method to reconstruct the shape of the target object using minimal data. Specifically, four concurrent laser scanner lines provide partial contours of the object cross-section. Simple geometry is then used to reconstruct the dimensions and orientation of spherical, cylindrical and cuboid objects. The prototype system was implemented using depth sensor to simulate the scan lines and vibrotactile feedback to aid the user during aiming of the laser towards the target object. The prototype was tested on ten able-bodied volunteers who used the semi-autonomous prosthesis to grasp a set of ten objects of different shape, size and orientation. The novel prototype was compared against the benchmark system, which used the full depth data. The results showed that novel system could be used to successfully handle all the objects, and that the performance improved with training, although it was still somewhat worse compared to the benchmark. The present study is therefore an important step towards building a compact system for embedded depth sensing specialized for prosthesis grasping.

Lower limb prosthetic technology has greatly advanced in the last decade, but there are still many challenges that need to be tackled to allow amputees to walk efficiently and safely on many different terrain conditions. Neuro-mechanical modelling and online simulations combined with somatosensory feedback, has the potential to address this challenge. By virtually reconstructing the missing limb together with the associated somatosensory feedback, this approach could enable amputees to potentially perceive the bionic legs as extensions of their bodies. A prosthesis equipped with such biologically inspired closed-loop control could duplicate the mechanics of walking far more accurately than conventional solutions. The project SimBionics aims to explore these opportunities and advance the state-of-the-art in lower limb prosthesis control.

Additional file 1. Links and descriptions to a video demonstrating the system and to the project website.

The invention relates to a hand orthosis (1) for assisting the movements of a user's hand. The hand orthosis comprises a soft glove (2) adapted to receive at least a part of the user's hand with finger sections of the glove receiving each of the user's fingers. It further comprises at least one actuator box (3), and a power transmission (4) for transferring forces from the at least one actuator box to different parts of the glove via exo-tendons (5). The exo-tendons are arranged on a dorsal side, on a palmar side, or on both a dorsal side and a palmar side of the glove. The exo-tendons are arranged so that the glove can assist flexion, extension, or both extension and flexion of the fingers, respectively and so that the exo-tendons act on the fingers to produce motion that mimics the movement of a human hand. The hand orthosis provides different grasp types, self- adaptation of the fingers' motion to the shape of the object, and physiologically appropriate distribution of finger forces.

It has been recognized that bipedal locomotion is controlled using feed-forward (e.g., patterned) and feedback (e.g., reflex) control schemes. However, most current controllers fail to integrate the two schemes to simplify speed control of bipedal locomotion. To solve this problem, we here propose a patterned muscle-reflex controller integrating feed-forward control with a muscle-reflex controller. In feed-forward control, the pattern generator is modeled as a Matsuoka neural oscillator that produces four basic activation patterns that mimic those extracted experimentally via electromyograms (EMGs). The associated weights of the patterns for 16 Hill-type musculotendon units (MTUs) are calculated based on a predictive model of muscle excitations under human locomotion. The weighted sums of the basic activation patterns serve as the pre-stimulations to muscle-reflex control of the Hill-type MTUs actuating a 2D-simulated biped. As a result, the proposed controller enables the biped to easily regulate its speed on an even ground by only adjusting the descending input. The speed regulation does not require re-optimizations of the controller for various walking speeds, compared to pure muscle-reflex controllers.

WEARPLEX is a multidisciplinary research and innovation action with the overall aim to integrate printed electronics with flexible and wearable textile-based biomedical multi-pad electrodes. It aims to answer the growing need for user-friendly electrodes for pervasive measurement of electrophysiological signals and application of electrical stimulation. It focuses on the development of the printable electronics and manufacturing processes for stretchable textile based multi-pad electrodes with integrated logic circuits that enable a significant increase in the number of electrode pads (channels) and facilitate the creation of new products in the sectors of medical electronics and life-style. The advanced printed electronics integrated in WEARPLEX electrodes will allow the individual pads to be connected in arbitrary configurations to the output leads of the electrode. Therefore, the pads will be flexibly organized into several virtual electrodes of arbitrary position, shape and size that can be connected to any standard multi-channel recording and stimulation system. In addition, software methods will be developed for automatic calibration of these virtual electrodes, to detect stimulation/recording hotspots and adjust the virtual electrodes accordingly. Therefore, the WEARPLEX project will lead to a new generation of smart electrodes that will be able to adapt simultaneously to the user (wearable and stretchable garment), recording/stimulation scenario (movement type and target muscles) and recording/stimulation system (number of channels). This is a paradigm shift in designing the recording and stimulation systems, as the switching electronics is shifted from the custom-made stimulator/recording device to the smart electrode, leading to a universal solution compatible with any system. WEARPLEX is funded under the EU Horizon2020 call ICT-02-2018, the work program is over 3 years starting on the 1st of January 2019; this abstract is therefore an opportunity to introduce the project to the community and discuss research directions and early results for the project. The individual components of the project are at TRL2-4 with the goal of improving and combining them to achieve TRL6-7 industrial demonstrators by the end of the project.

Mind controlled bionic limbs promise to replace mechanical function of lost biological extremities and restore amputees’ motor capacity. State of the art approaches use machine learning for establishing a mapping function between electromyography (EMG) and joint kinematics. However, current approaches require frequent recalibration with lack of robustness, thus providing control paradigms that are sensitive to external conditions. This paper presents an alternative method based on the authors’ recent findings. That is, a biomimetic decoder comprising a computational model that explicitly synthesizes the dynamics of the musculoskeletal system as controlled by EMG-derived neural activation signals.

Proceedings of the 8th Graz Brain Computer Interface Conference 2019, Bridging Science and Application

State-of-the-art prosthetic hands allow separate control of all digits. Restoring natural hand use with these systems requires simultaneous and proportional control of all fingers. Regression algorithms might be able to predict any combination of degrees of freedom after training them separately. However, to the best of our knowledge, this has yet to be shown online. Twelve able-bodied participants were instructed to reach predefined target forces representing either single or combined finger presses, following a system training session consisting of only individual finger presses. Myoelectric control was implemented using linear ridge regression. The results demonstrated that myoelectric control allowed participants to reach both single finger, and combination targets, with hit rates of 88% and 54% respectively. These findings suggest that simultaneous control of multiple fingers is possible, even when these movements are not included in the training set.

Brain-computer interfaces have increasingly found applications within the rehabilitation of lost motor function in stroke patients. Most studies have targeted upper limb muscles and used sensorimotor rhythms as the control signal. In a series of studies, we have introduced an associative BCI modeled on known theories of memory and learning that implements the movement related cortical potential (MRCP) as a way to control an external device that provides afferent generated feedback to the user’s brain at the time of the peak negative phase of the MRCP. In its application to lower limb muscles it demonstrates significant plasticity induction that requires no user training. In the current study, we tested if this associative BCI is effective when targeting upper limb muscles. Further, we explored if there is a difference when the MRCP is gen- erated as part of a simple (wrist extension) versus a complex (reach and grasp) movement.

Online myoelectric control involves two types of adaptation: computational adaptation, in which the controller learns to associate muscle patterns with performed forces; and behavioural adaptation, where the users learn the new interface, and adapt their motor control strategies based on the errors they observe. In order to study the behavioural motor learning during online myoelectric control, twelve able-bodied participants performed single and 2-finger presses through force and myoelectric control. Myoelectric control was obtained with linear ridge regression, and was based on a training set only containing single finger presses. The distance between muscle patterns of force and EMG control trials indicated that motor learning leads to changes in neural drive, even on the trained presses. This suggests that motor learning is an integral part of myoelectric control, where the ability of the user to learn the EMG-to-force mapping impacts the overall performance of the myoelectric controller.

Objectives: The loss of a hand is a traumatizing experience that can comprise an individual capability to interact intuitively with his environment. The state-of-the-art (SoA) functional replacement for the lost limbs are myoelectric prostheses. Their overall mechanical design and dexterity have improved[for full text, please go to the a.m. URL], Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2018)

Mind controlled bionic limbs promise to replace mechanical function of lost biological extremities and restore amputees’ motor capacity. State of the art approaches use machine learning for establishing a mapping function between electromyography (EMG) and joint kinematics. However, current approaches require frequent recalibration with lack of robustness, thus providing control paradigms that are sensitive to external conditions. This paper presents an alternative method based on the authors’ recent findings. That is, a biomimetic decoder comprising a computational model that explicitly synthesizes the dynamics of the musculoskeletal system as controlled by EMG-derived neural activation signals.

This study systematically evaluated the perceptual properties of subdermal electrical stimulation to test its efficacy in providing sensory feedback for limb prostheses. The detection threshold (DT), pain threshold (PT), just noticeable difference (JND), as well as the elicited sensation quality, comfort, intensity and location were assessed in 16 healthy volunteers during stimulation of the ventral and dorsal forearm with subdermal electrodes. Moreover, the results were compared with those obtained from transcutaneous electrical stimulation. Despite a lower DT and PT, subdermal stimulation attained a greater relative dynamic range (i.e., PT/DT) and significantly smaller JNDs for stimulation amplitude. Muscle twitches and movements were more commonly elicited by surface stimulation, especially at the higher stimulation frequencies, whereas the pinprick sensation was more often reported with subdermal stimulation. Less comfort was perceived in subdermal stimulation of the ventral forearm at the highest tested stimulation frequency of 100 Hz. In summary, subdermal electrical stimulation was demonstrated to be able to produce similar sensation quality as transcutaneous stimulation and outperformed the latter in terms of energy efficiency and sensitivity. These results suggest that stimulation through implantable subdermal electrodes may lead to an efficient and compact sensory feedback system for substituting the lost sense in amputees.

© 2015 Gonzalez-Vargas et al., Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system’s complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs., This work was supported by the Japanese Society for Promotion of Science (JSPS) Grant for Researcher Dispatch Program-International Network of Advanced Medical Engineering using Bio-robotics Technology (2009-2012), European Commission under the MYOSENS (FP7-PEOPLE-2011-IAPP-286208) project and by the German Ministry for Education and Research (BMBF) via the Bernstein Focus Neurotechnology (BFNT) Gottingen under Grant 01GQ0810.

Background Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback). Methods As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task. Results During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees. Conclusions The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system. peerReviewed

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS) Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

The difficulty in defining standard benchmarks for human likeness is a well-known problem in bipedal robotics. This paper proposes the conceptual design of a novel benchmarking scheme for bipedal robots based on existing criteria and benchmarks related to the sensorimotor mechanisms involved in human walking and posture. The proposed scheme aims to be sufficiently generic to permit its application to a wide range of bipedal platforms, and sufficiently specific to rigorously test the sensorimotor skills found in humans. The achievement of global consensus on the definition of human likeness has a crucial importance not only in the field of humanoid robotics, but also in neuroscience and clinical settings. The EU project H2R is specifically dealing with this problem. A preliminary solution is here given to encourage the international discussion on this topic within the scientific community. published

Consejo Superior de Investigaciones Científicas (CSIC), Georg-August-Universität Göttingen Stiftung Öffentlichen Rechts Universitätsmedizin, B1 Patente sin examen previo

Method and neuroprosthetic device for monitoring and sup pression of pathological tremors in a user through the neuro stimulation of the afferent pathways to the brain, which com prises wearable elements placed over the parts of the body affected by tremor, wherein said wearable elements (1) have sensors selected from bioelectric sensors (3) generating a bioelectrical signal characterization of tremor, biomechanical sensors (4) generating a biomechanical signal characteriza tion of tremor and a combination thereof, a programmable electronic device (9) comprised of a control, acquisition and processing module of the characterization signals, and a sig nal generator for the afferent stimulation based on the bio electrical, biomechanical or combination of both signal char acterization and stimulation electrodes (8) which transmit the neuromodulation signals to the afferent pathways projecting into the central nervous system to modulate the activity of the neural structures responsible for tremor generation., Consejo Superior de Investigaciones Científicas (España), Georg-August-Universität Göttingen, A1 Solicitud de patente con informe sobre el estado de la técnica

Método y dispositivo neuroprotésico para el monitoreo y la supresión de los temblores patológicos en un usuario a través de la neuroestimulación de las vías aferentes hacia el cerebro, que comprende elementos portátiles colocados sobre las partes del cuerpo afectados por el temblor, en el que dichos elementos portátiles (1) tienen sensores bioeléctricos. (3) Generación de una primera caracterización de señal de temblor, sensores biomecánicos (4) Generación de una segunda caracterización de señal de temblor, un dispositivo electrónico programable (9) compuesto por un módulo de control, adquisición y procesamiento de las señales de caracterización de primera y segunda, y un Generador de señal para la estimulación aferente basada en la primera y segunda caracterización de la señal y electrodos de estimulación (8) que transmiten las señales de neuromodulación a las vías aferentes que se proyectan en el sistema nervioso central para modular la actividad de las estructuras neuronales responsables de la generación de temblores., Consejo Superior de Investigaciones Científicas (CSIC), Georg-August-Universität Göttingen Stiftung Öffentlichen Rechts Universitätsmedizin, T3 Traducción de patente europea

International audience; One possible method for restoring motion of paralyzed limbs is Functional Electrical Stimulation. FES can be applied in early phase of hemiplegia in order to control the affected leg while walking. FES was shown to be a valuable method for training that likely allows the user to walk later on without assistance. Rehabilitation centres mostly use preprogrammed stimulation patterns empirically obtained and manually triggered. The stimulation pattern and timing need to be set and online adapt to optimize the use of preserved capabilities and lead to symmetrical gait. We present an approach based on three phases. First an optimal control based on individually customized model is used to create the basic reference stimulation pattern for the deficient leg. Secondly, the plant is modeled as a nonlinear oscillator. Finally, the oscillator is triggered from the detection of the gait cycle phase assessed via a sensor placed on the healthy leg.

Background Electrocutaneous stimulation can restore the missing sensory information to prosthetic users. In electrotactile feedback, the information about the prosthesis state is transmitted in the form of pulse trains. The stimulation frequency is an important parameter since it influences the data transmission rate over the feedback channel as well as the form of the elicited tactile sensations. Methods We evaluated the influence of the stimulation frequency on the subject’s ability to utilize the feedback information during electrotactile closed-loop control. Ten healthy subjects performed a real-time compensatory tracking (standard test bench) of sinusoids and pseudorandom signals using either visual feedback (benchmark) or electrocutaneous feedback in seven conditions characterized by different combinations of the stimulation frequency (FSTIM) and tracking error sampling rate (FTE). The tracking error was transmitted using two concentric electrodes placed on the forearm. The quality of tracking was assessed using the Squared Pearson Correlation Coefficient (SPCC), the Normalized Root Mean Square Tracking Error (NRMSTE) and the time delay between the reference and generated trajectories (TDIO). Results The results demonstrated that FSTIM was more important for the control performance than FTE. The quality of tracking deteriorated with a decrease in the stimulation frequency, SPCC and NRMSTE (mean) were 87.5% and 9.4% in the condition 100/100 (FTE/FSTIM), respectively, and deteriorated to 61.1% and 15.3% in 5/5, respectively, while the TDIO increased from 359.8 ms in 100/100 to 1009 ms in 5/5. However, the performance recovered when the tracking error sampled at a low rate was delivered using a high stimulation frequency (SPCC = 83.6%, NRMSTE = 10.3%, TDIO = 415.6 ms, in 5/100). Conclusions The likely reason for the performance decrease and recovery was that the stimulation frequency critically influenced the tactile perception quality and thereby the effective rate of information transfer through the feedback channel. The outcome of this study can facilitate the selection of optimal system parameters for somatosensory feedback in upper limb prostheses. The results imply that the feedback variables (e.g., grasping force) should be transmitted at relatively high frequencies of stimulation (>25 Hz), but that they can be sampled at much lower rates (e.g., 5 Hz). peerReviewed

Background Restoring sensory feedback in myoelectric prostheses is still an open challenge. Closing the loop might lead to a more effective utilization and better integration of these systems into the body scheme of the user. Electrotactile stimulation can be employed to transmit the feedback information to the user, but it represents a strong interference to the recording of the myoelectric signals that are used for control. Time-division multiplexing (TDM) can be applied to avoid this interference by performing the stimulation and recording in dedicated, non-overlapping time windows. Methods A closed-loop compensatory tracking task with myocontrol and electrotactile stimulation was used to investigate how the duration of the feedback window (FW) influences the ability to perceive the feedback information and react with an appropriate control action. Nine subjects performed eight trials with continuous recording and contralateral feedback (CONT-CLT) and TDM with ispilateral stimulation and recording using the FW of 40 ms (TDM40), 100 ms (TDM100) and 300 ms (TDM300). The tracking quality was evaluated by comparing the reference and generated trajectories using cross-correlation coefficient (CCCOEF), time delay, root mean square tracking error, and the amount of overshoot. Results The control performance in CONT-CLT was the best in all the outcome measures. The overall worst performance was obtained using TDM with the shortest FW (TDM40). There was no significant difference between TDM100 and TDM300, and the quality of tracking in these two conditions was high (CCCOEF ~ 0.95). The results demonstrated that FW duration is indeed an important parameter in TDM, which appears to have an optimal value. Among the tested cases, the FW duration of 100 ms seems to be the best trade-off between the quality of perception and a limited command update rate. Conclusions This study represents the first systematic evaluation of a TDM-based approach for closing the loop using electrotactile feedback in myoelectric systems. The overall conclusion is that TDM is a feasible and attractive method for closed-loop myocontrol, since it is easy to implement (software-only solution), has limited impact on the performance when using proper FW duration, and might decrease habituation due to burst-like stimulation delivery. peerReviewed