Mainly composed of electrical motors and sophisticated mechanical components, existing neuroprosthetic hands 1,2 are typically heavy (>400 g) and expensive (>USD 10,000), and they lack the compliance and tactile feedback of human hands. These limitations hamper neuroprosthetic hands' innovation and broad utility for amputees 3-5 . Here we report the design, fabrication and applications of a lightweight (292 g) and potentially low-cost (component cost below USD 500) soft neuroprosthetic hand with simultaneous myoelectric control and tactile feedback. The soft neuroprosthetic hand consists of five soft fingers and a palm to give six active degrees of freedom under pneumatic actuation, four electromyography sensors that measure the surface electromyogram signals to control the hand to deliver four common grasp types, and five hydrogel-elastomer capacitive sensors on the fingertips that measure the touch pressure and elicit electrical stimulation on the skin of the residual limb. The soft finger is made of a fiber-reinforced elastomeric structure embedded with rigid segments to mimic the soft-joint/rigid-bone anatomy of the human finger. We use a set of standardized tests 6 to compare the speed and dexterity of the soft neuroprosthetic hand and a conventional rigid neuroprosthetic hand 7 on two transradial amputees. The soft neuroprosthetic hand gives overall superior performances to the rigid hand. We further demonstrate that one transradial amputee wearing the soft neuroprosthetic hand can regain the versatile hand functions with primitive touch sensation and real-time closed-loop control in daily activities such as handling tools, eating, shaking hands, petting animals, and recognizing touch pressure. This work not only represents a new paradigm for designing soft 2 neuroprosthetic devices but also opens an avenue to widespread applications of lightweight, low-cost, and compliant hand replacements for amputees.
The stable PFM and sensory thresholds of ETS are desirable for a non-invasive neural interface that can feed back finger-specific tactile information from the prosthetic hand to forearm amputees.
Cutaneous electrical stimulation can provide tactile feedback for upper-limb amputees through somatotopic feedback (SF) or non-somatotopic feedback (NF). The SF delivers electrotactile stimulus to projection finger maps (PFMs) on the stumps of amputees, which outperforms NF that transfers stimulus to other human intact skin areas in general. However, the SF areas on stumps are very limited and often occupied by electromyography (EMG) sensors in application of myoelectric prosthesis. This work aims at improving NF performance on human upper arms through user training with electrotactile stimulation. The experiments were conducted over seven consecutive days on nine able-bodied subjects and two forearm amputees. The performance measures of NF/SF included the correct identification rates (CIR), the response time and the NASA-TLX questionnaire. The between-day CIR s on NF sites increased logarithmically with a mean course of 3-day rapid-improving phase and plateaued in the relative-steady phase. The response time and NASA-TLX scores could also rapidly reduce to the comparable levels of the SF areas during the same mean period of 3-day rapid-improving phase, respectively. These results indicated that the performance of NF could be highly improved to the equivalent level as that of SF through 3-day electrotactile training, which we named as "3-day effect". It provides important insights that intact skin areas without phantom sensations can effectively replace SF sites to transfer tactile feedback after continuous user training, which validates effectiveness of non-invasive interfaces of tactile feedback for upper-limb amputees in practice.
Objective. The aim of the study was to characterize the accuracy in the identification of motor unit discharges during natural movements using high-density electromyography (EMG) signals and to investigate their correlation with finger kinematics. Approach. High-density EMG signals of forearm muscles and finger joint angles were recorded concurrently during hand movements of ten able-bodied subjects. EMG signals were decomposed into motor unit spike trains (MUSTs) with a blind-source separation method. The first principle component (FPC) of the low-pass filtered MUST was correlated with finger joint angles. Main results. On average, motor units were identified during each individual finger task with an estimated decomposition accuracy 85%. The FPC extracted from discharge rates was strongly associated to the joint angles (), and preceded the joint angles on average by ms. Moreover, the FPC outperformed two time-domain features (the EMG envelop and the root mean square of EMG) in estimating joint angles. Significance. These results indicated the possibility of identifying individual motor unit behavior in dynamic natural contractions. Moreover, the strong association between motor unit discharge behaviors and kinematics proves the potential of the approach for the simultaneous and proportional control of prostheses.
Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings. Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding. Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only. Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.
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