Neuro-Aligned Tactile Feedback for Upper Limb Prostheses: A Neuroanatomical Review and Evaluation Framework

Decoding Overlapping Lower-Limb Afferent Pathways from Human Epidural Spinal Recordings

This work demonstrates for the first time in humans that clinical-grade lumbosacral epidural paddle arrays capture sufficient fine-scale spatiotemporal structure to decode these overlapping inputs from highly overlapping, volume-conducted epidural fields.

Distinct neural modes carry information about attempted grasp timing and force in the sensorimotor cortex.

Investigating neural activity within the motor and somatosensory cortices of individuals with tetraplegia attempting to perform grasps to different target forces with varying temporal profiles identified distinct neural modes modulated during specific phases of grasp that encode attempted force information throughout the task.

Global research trends and thematic evolution of haptic feedback and force sensing in robot-assisted urologic surgery: a bibliometric and visualization study

Digital Neurorehabilitation: Virtual Reality, Robotics and Neuroplasticity After Stroke

Stroke is a leading cause of long-term neurological disability, frequently producing persistent motor impairment, loss of independence, and limitations in social participation. Virtual reality and robot-assisted rehabilitation have emerged as strategies for increasing treatment intensity, repetition, task specificity, feedback, and patient engagement. This study aimed to analyze their effectiveness in promoting neuroplasticity and improving motor and functional outcomes in adults after stroke. A structured integrative literature review based on the Scientific Method and PRISMA 2020 principles was conducted using PubMed/MEDLINE, the Cochrane Library, Scopus, Web of Science, IEEE Xplore, PEDro, and official journal databases. Twenty publications were selected, including systematic reviews, meta-analyses, randomized controlled trials, narrative reviews, and a clinical practice guideline. Ten publications were systematic reviews or meta-analyses; five presented predominantly favorable findings, four reported conditional or mixed results, and one found no clear additional benefit. Virtual reality was associated with selected improvements in upper-limb motor function, dexterity, functional independence, quality of life, cortical activation, and functional connectivity. Robot-assisted rehabilitation provided intensive and measurable movement practice and improved selected impairment-based outcomes, although its superiority over dose-matched conventional rehabilitation was inconsistent. Improvements in motor control and strength did not always translate into greater independence or spontaneous use of the affected limb in daily life. The findings indicate that digital neurorehabilitation is most effective when it provides active, repetitive, intensive, task-specific, progressively challenging, and feedback-rich training. Virtual reality and robotics should be integrated as complementary components of conventional rehabilitation rather than universal replacements. Further randomized trials with standardized protocols, equivalent treatment doses, clinically meaningful outcomes, direct neuroplasticity measurements, safety assessment, and long-term follow-up are required.

The Crossmodal Congruency Task as a measure of intuitiveness of sensory feedback in the lower limb

People with lower limb amputation lack somatosensory feedback from their prosthesis, and this loss contributes to functional deficits, including balance and gait impairments. Recent advances in neuroprostheses have demonstrated that electrical stimulation of sensory nerves in the residual limb and spinal cord can restore lost sensations in the lower limb. To maximize the efficacy of these somatosensory neuroprostheses, the restored sensations should be intuitive, seamlessly integrating into the sensorimotor network. However, it is challenging to quantify the intuitiveness of these evoked sensations. Recent studies have proposed using crossmodal congruency effect (CCE) tasks for this purpose in people with upper-limb amputation. The current study tests the feasibility of the CCE task for assessing the intuitiveness of sensory feedback in the lower limb. We hypothesized that CCE score would reliably differentiate between a more natural (pneumatic) sensation and a less natural (electric) sensation at two locations: the knee and the foot. Across fifteen able-bodied individuals, we observed that the CCE task differentiates sensory modalities at the knee, but not at the foot. Identification of external factors affecting the CCE is needed before it can be implemented to measure intuitiveness of sensory feedback in lower-limb amputees.

Targeting grasp-related cortical areas for intracortical brain-machine interfaces

This study aimed to improve intracortical microelectrode array implantation sites for grasp-related motor decoding by integrating anatomical, functional, and vascular imaging with preoperative 3D modeling. A participant with C5 tetraplegia underwent anatomical MRI, diffusion-weighted imaging, and task-based fMRI to identify grasp-related cortical regions while avoiding vasculature and speech-critical areas. Quicktome software was used to refine target selection by integrating structural connectivity and functional activation data. A 3D-printed skull and cortical model enabled preoperative planning, including craniotomy and electrode positioning simulations. Electrode placement was validated postoperatively using neural data collected from the implanted arrays during attempted movements of the arm and hand. Functional imaging identified distinct grasp-related activation in anterior intraparietal area (AIP), ventral premotor cortex (PMv), and inferior frontal gyrus (IFG). Putative AIP was selected based on its strong connectivity with motor cortex and distinct functional activation. Subregions 6v and 6r of PMv, which exhibited robust grasp-related activity and were surgically accessible, were chosen over the posterior IFG region, which extended into a sulcus making implantation difficult. Postoperatively, the arrays enabled high-fidelity decoding of arm/hand movements, achieving a combined classification accuracy of 96%. This study presents a multi-modal approach for improving intracortical electrode placement by combining MRI-based anatomical mapping, fMRI-guided functional localization, connectivity information, and 3D surgical modeling. These findings demonstrate an effective method for identifying surgically feasible grasp network implant locations in a paralyzed individual. This is an essential step toward brain–machine interfaces that enable individuals with spinal cord injury to control devices using grasp-related brain activity.