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#prosthesis

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Eugene McParland 🇺🇦

Oleh Slyvar is 26 years old, and for 12 of them he danced in the Verkhovynka #dancing group.

He joined the #Ukraine National Guard in the summer of 2022 and fought in the #Donetsk region. Oleh sustained numerous shrapnel wounds to his leg during a mortar assault.

Oleh received a modern #prosthesis with an electronic knee, which allowed him to #dance again.

The incredible strength of spirit of Ukrainian defenders inspires to fight and move towards our victory.

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katch wreck

"Here we present an injectable tissue #prosthesis with instantaneous bidirectional electrical conduction in the #neuromuscular system. The soft and injectable prosthesis is composed of a biocompatible #hydrogel with unique phenylborate-mediated multiple crosslinking, such as irreversible yet freely rearrangeable biphenyl bonds and reversible coordinate bonds with conductive gold #nanoparticles formed in situ by cross-coupling"

nature.com/articles/s41586-023

NatureInjectable tissue prosthesis for instantaneous closed-loop rehabilitation - NatureAn injectable hydrogel for use as a scaffold to aid tissue repair is described, the material of which is conductive so that it can be used both for electrophysiological measurement and electrostimulation in closed-loop robot-assisted rehabilitation.
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The vOICe vision BCI 🧠🇪🇺

Intraretinal stimulation with high density carbon fiber microelectrodes ieeexplore.ieee.org/document/1 by James Weiland et al.; "modification of pulse amplitudes and electrode depths can create small and focal responses around the active electrode"; #retinal #implant, retinal #prosthesis

ieeexplore.ieee.orgIntraretinal stimulation with high density carbon fiber microelectrodesRetinal prostheses can improve vision for patients blinded by photoreceptor degenerative diseases. Despite the benefits of artificial vision, low spatial resolution of these prostheses limits the positive impact of clinically available devices. Visual percepts generated by single electrodes in epiretinal and subretinal implants can overlap and result in an unclear image, which limits shape and letter perception for retinal prosthesis users. However, research suggests higher resolution may be possible with smaller electrodes implanted intraretinally, in close proximity of target neurons. In this study we used penetrating subcellular-scale carbon fiber microelectrodes for retinal stimulation in an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$ex$</tex> vivo mouse retina, and performed calcium imaging to record spatial activation of retinal ganglion cells (RGC) in response to different stimulation amplitudes and RGC-electrode distances. We observed smaller RGC spatial activities and less off-target stimulation with higher RGC-electrode distances, which may be an indication of indirect RGC activation through bipolar cells. Impedance measurements of carbon fiber electrodes demonstrated their mechanical and electrical stability throughout the process of insertion and stimulation. Our results indicate that modification of pulse amplitudes and electrode depths can create small and focal responses around the active electrode. Intraretinal stimulation with carbon fibers can potentially increase stimulation precision and image resolution for retinal prostheses in clinical applications.
Public
The vOICe vision BCI 🧠🇪🇺

A fun example of the usefulness and necessity of temporal data augmentation in a #BCI setting (#BrainGate2 pilot clinical trial): Translating deep learning to neuroprosthetic control biorxiv.org/content/10.1101/20 applying dilation/compression and re-ordering of training data to prevent overfitting; #brain #prosthesis #NeuroTech #AI

bioRxivTranslating deep learning to neuroprosthetic controlAdvances in deep learning have given rise to neural network models of the relationship between movement and brain activity that appear to far outperform prior approaches. Brain-computer interfaces (BCIs) that enable people with paralysis to control external devices, such as robotic arms or computer cursors, might stand to benefit greatly from these advances. We tested recurrent neural networks (RNNs) on a challenging nonlinear BCI problem: decoding continuous bimanual movement of two computer cursors. Surprisingly, we found that although RNNs appeared to perform well in offline settings, they did so by overfitting to the temporal structure of the training data and failed to generalize to real-time neuroprosthetic control. In response, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously, far outperforming standard linear methods. Our results provide evidence that preventing models from overfitting to temporal structure in training data may, in principle, aid in translating deep learning advances to the BCI setting, unlocking improved performance for challenging applications. ### Competing Interest Statement The MGH Translational Research Center has a clinical research support agreement with Neuralink, Synchron, Axoft, Precision Neuro, and Reach Neuro, for which L.R.H. provides consultative input. J.M.H. is a consultant for Neuralink and serves on the Medical Advisory Board of Enspire DBS. K.V.S. consulted for Neuralink and CTRL-Labs (part of Meta Reality Labs) and was on the scientific advisory boards of MIND-X, Inscopix and Heal. All other authors have no competing interests.
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