IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

Understanding the frequency dependent alterations in brain-muscle communication after stroke is crucial for advancing targeted neurorehabilitation strategies. In this study, we propose a novel multilayer corticomuscular network (MCMN) model based on functional corticomuscular coupling characteristics. Using multi-channel electrophysiological recordings acquired during a multi-joint motor task, we constructed a super-connectivity matrix by combining phase synchronization and phase-amplitude coupling across frequency bands. We then examined both local (single-layer) and global (multilayer) network properties by comparing nodal metrics between stroke patients and healthy controls in terms of functional connectivity and topological organization. The results revealed that stroke patients exhibited enhanced theta band within-frequency subnetwork relative to controls, but significantly reduced beta and gamma band subnetworks. Cross-frequency subnetworks in patients showed diminished integrative capacity compared to controls, with the exception of proximal muscle nodes in the beta-gamma subnetwork, which displayed pronounced hub properties. At the global level, patients demonstrated contralateral compensatory reorganization, whereas the contralateral hemisphere exhibited impaired cross-layer integration. The MCMN of stroke patients showed reduced algebraic connectivity, reflecting lower network robustness and information transfer efficiency. Finally, we found that node degree of gamma band and multiplex clustering coefficient of ipsilateral exhibited a linear correlation with FMA-UE scores in stroke patients. This multilayer network approach reveals frequency-specific and topological reorganization of corticomuscular interactions following stroke, providing a novel systems level framework for exploring motor network plasticity and informing precision neurorehabilitation.

The surface electromyogram (sEMG) based motion intent recognition has been considered as a promising approach for prosthetic hand control. However, the variations of EMG signals in daily applications and the insufficient residual muscles of amputees make the clinical application of EMG-based prosthesis remain a challenge. Tactile signals provide critical haptic information for human beings during object manipulation, which is expected to serve as a signal source for motion intent recognition. In this study, a novel tactile-triggered method of motion intent recognition and prosthetic hand control was proposed. This method is implemented by a finite state machine (FSM) using a multi-axis tactile sensor and is deployed in an integrated system. Validation consisted of three evaluation tests, the last involving both able-bodied and transradial-amputee participants. For comparison, the EMG pattern recognition (EMG-PR) control method was also implemented in all three tests. The experimental results demonstrate that the tactile-triggered control strategy provides an effective approach for prosthetic hand opening/closing control, enabling stable grasping of diverse objects of varying shapes and sizes. Compared to EMG-PR, this method substantially eliminates control performance degradation caused by arm posture changes and exhibits superior grasping stability. Additionally, it avoids muscle fatigue in users. This approach offers a promising prosthetic control strategy that can either function as a non-EMG control paradigm or serve as a complementary modality to myoelectric control.

Restoring sensory function post amputation remains a major challenge. Peripheral nerve stimulation and targeted reinnervation may partially restore somatotopic feedback, but their need for surgery hinders widespread adoption. Here, we investigate the feasibility of transcutaneous spinal cord stimulation (tSCS) as a non-invasive approach for sensory restoration in upper-limb amputees. In a study involving seventeen able-bodied participants and five individuals with upper-limb amputation, we show that tSCS can evoke a range of sensations, including touch, tapping, vibration, and movement, perceived as originating from the missing limb. Notably, these perceptions were primarily isolated to the missing limb and absent in the residual limb in 98% of trials. Participants with amputations found tSCS tolerable, with some reporting increased comfort during stimulation. tSCS evoked sensations in the fingertips of 93% of able-bodied participants, though these were mainly paraesthetic. We further characterised how stimulation parameters, including electrode placement, carrier frequency, and burst frequency, modulated the quality and type of perceived sensations. Additionally, we show that tSCS maintained force proprioception necessary for effective prosthesis control. These findings support the potential of tSCS as a non-invasive sensory feedback approach for upper-limb prosthesis users.

Previously, we proposed a power-free isokinetic training robot designed to provide resistive isokinetic training for knee injury patients during advanced-stage rehabilitation. However, patients in the early stage often lack sufficient muscle strength and necessitate assistive support. To address this limitation, this study introduces a hybrid assistive-resistive isokinetic training robot that integrates active assistance for early-stage knee rehabilitation and power-free resistive training for advanced stages. The system features a compact mechanical design and a reconfigurable control circuit capable of dynamically switching among three modes: active, passive (regeneration), and passive (consumption). Ten healthy subjects and ten knee-injury patients participated in the experimental validation. The results confirmed the adaptability of the system across multiple rehabilitation stages. These findings demonstrate the feasibility of the hybrid assistive-resistive isokinetic training robot and highlight the potential of the system for both clinical application and home-based rehabilitation. Future work will focus on extending the system to multi-joint training and enhancing control algorithms for broader patient populations.