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Background Chronic stroke patients often experience persistent motor impairments, and current rehabilitation therapies rarely achieve substantial functional recovery. Sensory feedback during movement plays a pivotal role in driving neuroplasticity. This study introduces a novel multi-modal sensory feedback brain-computer interface (Multi-FDBK-BCI) system that integrates proprioceptive, tactile, and visual stimuli into motor imagery-based training. We aimed to explore the potential therapeutic efficacy and elucidate its neural mechanisms underlying motor recovery. Methods Thirty-nine chronic stroke patients were randomized to either the Multi-FDBK-BCI group ( n = 20) or the conventional motor imagery therapy group ( n = 19). Motor recovery was assessed using the Fugl-Meyer Assessment (primary outcome), Motor Status Scale, Action Research Arm Test, and surface electromyography. Functional MRI was used to examine brain activation patterns during upper limb tasks, while Granger causality analysis and machine learning evaluated inter-regional connectivity changes and their predictive value for recovery. Results Multi-FDBK-BCI training led to significantly greater motor recovery compared to conventional therapy. Functional MRI revealed enhanced activation of high-order transmodal networks—including the default mode, dorsal/ventral attention, and frontoparietal networks—during paralyzed limb movement, with activation strength positively correlated with motor improvement. Granger causality analysis identified a distinct information flow pattern: signals from the lesioned motor cortex were relayed through transmodal networks to the intact motor cortex, promoting interhemispheric communication. These functional connectivity changes not only supported motor recovery but also served as robust predictors of therapeutic outcomes. Conclusions Our findings highlight the Multi-FDBK-BCI system as a promising strategy for chronic stroke rehabilitation, leveraging activity-dependent neuroplasticity within high-order transmodal networks. This multi-modal approach holds significant potential for patients with limited recovery options and sheds new light on the neural drivers of motor restoration, warranting further investigation in clinical neurorehabilitation. Trial registration All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (ChiCTR-ONC-17010739, registered 26 February 2017, starting from 10 January 2017). Graphical Abstract

Commercial prosthetic devices currently do not provide natural sensory information on the interaction with objects or movements. The subsequent disadvantages include unphysiological walking with a prosthetic leg and difficulty in controlling the force exerted with a prosthetic hand, thus creating health issues. Restoring natural sensory feedback from the prosthesis to amputees is an unmet clinical need. An optimal device should be able to elicit natural sensations of touch or proprioception, by delivering the complex signals to the nervous system that would be produced by skin, muscles and joints receptors. This Review covers the various neurotechnological approaches that have been proposed for the development of the optimal sensory feedback restoration device for arm and leg amputees. This Review highlights the approaches that have been utilized in the implementation of sensory feedback onto prosthetic devices to restore the sensation of touch and proprioception for amputees.

Neuroprosthetic hands are typically heavy (over 400 g) and expensive (more than US$10,000), and lack the compliance and tactile feedback of human hands. Here, we report the design, fabrication and performance of a soft, low-cost and lightweight (292 g) neuroprosthetic hand that provides simultaneous myoelectric control and tactile feedback. The neuroprosthesis has six active degrees of freedom under pneumatic actuation, can be controlled through the input from four electromyography sensors that measure surface signals from residual forearm muscles, and integrates five elastomeric capacitive sensors on the fingertips to measure touch pressure so as to enable tactile feedback by eliciting electrical stimulation on the skin of the residual limb. In a set of standardized tests performed by two individuals with transradial amputations, we show that the soft neuroprosthetic hand outperforms a conventional rigid neuroprosthetic hand in speed and dexterity. We also show that one individual with a transradial amputation wearing the soft neuroprosthetic hand can regain primitive touch sensation and real-time closed-loop control. A soft and lightweight neuroprosthetic hand that offers simultaneous myoelectric control and tactile feedback outperformed a conventional rigid neuroprosthetic hand in speed and dexterity.

Although balance training can improve balance across various populations, the underlying mechanisms, such as how balance training may alter sensory integration, remain unclear. This study examined the effects of balance training with visual input manipulations provided by virtual reality versus conventional balance training on measures of postural sway and sensory integration during balance control. Twenty-two healthy young adults were randomly allocated into a balance training group (BT) or a balance training with virtual reality group (BT + VR). The BT received traditional balance training, while the BT + VR additionally received visual manipulations during the 4-week balance training to elicit sensory conflicts. Static balance was measured in the form of center of pressure (COP) sway speed in trained (eyes open) and untrained (eyes closed) balance conditions. A model-based analysis quantified the sensory integration and feedback characteristics of the balance control mechanism. Herein, the visual weight quantifies the contribution of visual orientation information to balance while the proportional and derivative feedback loop-gains correct for deviations from the desired angular position and angular velocity, respectively. Significant main time effects were observed for the visual sensory contribution to balance ( p  = 0.002, = 0.41) and for the derivative feedback loop-gain ( p = 0.011, = 0.29). Significant group-by-time interactions were observed for COP sway speed in the untrained task ( p  = 0.023, = 0.23) in favor of BT + VR and in the proportional feedback loop-gain, with reductions only in the BT + VR group ( p  = 0.043, = 0.2). BT + VR resulted in larger performance improvements compared with traditional BT in untrained tasks, most likely due to reduced reliance on visual information. This suggests that the systematic modulation of sensory inputs leads to enhanced capacity for motor adaptation in balance training.

Soft machines are a promising design paradigm for human-centric devices 1 , 2 and systems required to interact gently with their environment 3 , 4 . To enable soft machines to respond intelligently to their surroundings, compliant sensory feedback mechanisms are needed. Specifically, soft alternatives to strain gauges—with high resolution at low strain (less than 5 per cent)—could unlock promising new capabilities in soft systems. However, currently available sensing mechanisms typically possess either high strain sensitivity or high mechanical resilience, but not both. The scarcity of resilient and compliant ultra-sensitive sensing mechanisms has confined their operation to laboratory settings, inhibiting their widespread deployment. Here we present a versatile and compliant transduction mechanism for high-sensitivity strain detection with high mechanical resilience, based on strain-mediated contact in anisotropically resistive structures (SCARS). The mechanism relies upon changes in Ohmic contact between stiff, micro-structured, anisotropically conductive meanders encapsulated by stretchable films. The mechanism achieves high sensitivity, with gauge factors greater than 85,000, while being adaptable for use with high-strength conductors, thus producing sensors resilient to adverse loading conditions. The sensing mechanism also exhibits high linearity, as well as insensitivity to bending and twisting deformations—features that are important for soft device applications. To demonstrate the potential impact of our technology, we construct a sensor-integrated, lightweight, textile-based arm sleeve that can recognize gestures without encumbering the hand. We demonstrate predictive tracking and classification of discrete gestures and continuous hand motions via detection of small muscle movements in the arm. The sleeve demonstration shows the potential of the SCARS technology for the development of unobtrusive, wearable biomechanical feedback systems and human–computer interfaces. Strain gauges with both high sensitivity and high mechanical resilience, based on strain-mediated contact in anisotropically resistive structures, are demonstrated within a sensor-integrated, textile-based sleeve that can recognize human hand motions via muscle deformations.

Current guidelines underline the importance of high-quality chest compression during cardiopulmonary resuscitation (CPR), to improve outcomes. Contrary to this many studies show that chest compression is often carried out poorly in clinical practice, and long interruptions in compression are observed. This prospective cohort study aimed to analyse whether chest compression quality changes when a real-time feedback system is used to provide simultaneous audiovisual feedback on chest compression quality. For this purpose, pauses in compression, compression frequency and compression depth were compared. The study included 292 out-of-hospital cardiac arrests in three consecutive study groups: first group, conventional resuscitation (no-sensor CPR); second group, using a feedback sensor to collect compression depth data without real-time feedback (sensor-only CPR); and third group, with real-time feedback on compression quality (sensor-feedback CPR). Pauses and frequency were analysed using compression artefacts on electrocardiography, and compression depth was measured using the feedback sensor. With this data, various parameters were determined in order to be able to compare the chest compression quality between the three consecutive groups. The compression fraction increased with sensor-only CPR (group 2) in comparison with no-sensor CPR (group 1) (80.1% vs. 87.49%; P < 0.001), but there were no further differences belonging compression fraction after activation of sensor-feedback CPR (group 3) (P = 1.00). Compression frequency declined over the three study groups, reaching the guideline recommendations (127.81 comp/min vs. 122.96 comp/min, P = 0.02 vs. 119.15 comp/min, P = 0.008) after activation of sensor-feedback CPR (group 3). Mean compression depth only changed minimally with sensor-feedback (52.49 mm vs. 54.66 mm; P = 0.16), but the fraction of compressions with sufficient depth (at least 5 cm) and compressions within the recommended 5-6 cm increased significantly with sensor-feedback CPR (56.90% vs. 71.03%; P = 0.003 and 28.74% vs. 43.97%; P < 0.001). The real-time feedback system improved chest compression quality regarding pauses in compression and compression frequency and facilitated compliance with the guideline recommendations. Compression depth did not change significantly after activation of the real-time feedback. Even the sole use of a CPR-feedback-sensor (\"sensor-only CPR\") improved performance regarding pauses in compression and compression frequency, a phenomenon known as the 'Hawthorne effect'. Based on this data real-time feedback systems can be expected to raise the quality level in some parts of chest compression quality. International Clinical Trials Registry Platform of the World Health Organisation and German Register of Clinical Trials (DRKS00009903), Registered 09 February 2016 (retrospectively registered).

Augmenting the body with artificial limbs controlled concurrently to one’s natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field. In this Review, the authors discuss recent technological and neuroscientific advances in human body augmentation. They construct a movement augmentation taxonomy, discuss how it is achieved, and propose a vision for the field.

The impact of vibrotactile feedback of the gravity vector, provided by a “balance” belt worn around the waist, was evaluated in 39 patients with a severe bilateral vestibular loss, confirmed by extensive laboratory testing and suffering from a low quality of life, mainly due to imbalance. The mobility and balance score (MBS) of all patients prior to the use of the belt was equal or less than 5 out of a scale of 10. Thirty-one out of the 39 patients experienced the effect of the belt on their balance and mobility as positive in a preselection trial of 2 h in the hospital. The 31 positive responders then used the belt for 1 month in daily life. The average MBS increased significantly from 4.2 to 7.9 (paired T test, T = 9.82, p < 0.00001). Twenty-three out of 31 patients reported a benefit ranging from an improvement of 60–200% in their MBS. Eight patients did not experience any benefit. In summary, 23 out of 39 patients with a severe imbalance due to a bilateral vestibular loss experienced a clear benefit of vibrotactile feed = back in daily life. We conclude that vibrotactile feedback via the waist can serve as an effective prothesis for patients with severe bilateral vestibular loss to improve the quality of life.

The emulation of tactile sensory nerves to achieve advanced sensory functions in robotics with artificial intelligence is of great interest. However, such devices remain bulky and lack reliable competence to functionalize further synaptic devices with proprioceptive feedback. Here, we report an artificial organic afferent nerve with low operating bias (−0.6 V) achieved by integrating a pressure-activated organic electrochemical synaptic transistor and artificial mechanoreceptors. The dendritic integration function for neurorobotics is achieved to perceive directional movement of object, further reducing the control complexity by exploiting the distributed and parallel networks. An intelligent robot assembled with artificial afferent nerve, coupled with a closed-loop feedback program is demonstrated to rapidly implement slip recognition and prevention actions upon occurrence of object slippage. The spatiotemporal features of tactile patterns are well differentiated with a high recognition accuracy after processing spike-encoded signals with deep learning model. This work represents a breakthrough in mimicking synaptic behaviors, which is essential for next-generation intelligent neurorobotics and low-power biomimetic electronics. Intelligent artificial tactile system for neurorobotics remains challenging. Here, Chen et al. developed an artificial organic afferent nerve to implement slip recognition and prevention actions by learning the real-time spatial information of directional touch.

Conventional leg prostheses do not convey sensory information about motion or interaction with the ground to above-knee amputees, thereby reducing confidence and walking speed in the users that is associated with high mental and physical fatigue 1 – 4 . The lack of physiological feedback from the remaining extremity to the brain also contributes to the generation of phantom limb pain from the missing leg 5 , 6 . To determine whether neural sensory feedback restoration addresses these issues, we conducted a study with two transfemoral amputees, implanted with four intraneural stimulation electrodes 7 in the remaining tibial nerve (ClinicalTrials.gov identifier NCT03350061). Participants were evaluated while using a neuroprosthetic device consisting of a prosthetic leg equipped with foot and knee sensors. These sensors drive neural stimulation, which elicits sensations of knee motion and the sole of the foot touching the ground. We found that walking speed and self-reported confidence increased while mental and physical fatigue decreased for both participants during neural sensory feedback compared to the no stimulation trials. Furthermore, participants exhibited reduced phantom limb pain with neural sensory feedback. The results from these proof-of-concept cases provide the rationale for larger population studies investigating the clinical utility of neuroprostheses that restore sensory feedback. A new prosthetic leg that can transmit sensory signals via implanted electrodes is shown to restore meaningful sensory feedback that improves walking performance and lowers phantom limb pain during use in two human lower-limb amputees.