A Virtual Reality-Based Protocol to Determine the Preferred Control Strategy for Hand Neuroprostheses in People With Paralysis.

Hand neuroprostheses restore voluntary movement in people with paralysis through neuromodulation protocols. There are a variety of strategies to control hand neuroprostheses, which can be based on residual body movements or brain activity. There is no universally superior solution, rather the best approach may vary from patient to patient.

An Investigation of Manifold-Based Direct Control for a Brain-to-Body Neural Bypass.

Brain-body interfaces (BBIs) have emerged as a very promising solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands from brain signals should provide the user with intuitive, accurate, and stable control. Here, we present a preliminary investigation in a monkey of a brain decoding strategy based on the direct coupling between the activity of intrinsic neural ensembles and output variables, aiming at achieving ease of learning and long-term robustness.

Immersive VR for upper-extremity rehabilitation in patients with neurological disorders: a scoping review.

Background: Neurological disorders, such as stroke and chronic pain syndromes, profoundly impact independence and quality of life, especially when affecting upper extremity (UE) function. While conventional physical therapy has shown effectiveness in providing some neural recovery in affected individuals, there remains a need for improved interventions. Virtual reality (VR) has emerged as a promising technology-based approach for neurorehabilitation to make the patient's experience more enjoyable.

Bayesian optimization of peripheral intraneural stimulation protocols to evoke distal limb movements.

Motor neuroprostheses require the identification of stimulation protocols that effectively produce desired movements. Manual search for these protocols can be very time-consuming and often leads to suboptimal solutions, as several stimulation parameters must be personalized for each subject for a variety of target motor functions. Here, we present an algorithm that efficiently tunes peripheral intraneural stimulation protocols to elicit functionally relevant distal limb movements.