Fluid Synchrony (United States)

Background:Implanted brain-computer interfaces (iBCIs) establish direct communication with the brain and hold the potential to enable people with severe disability to achieve control of digital devices, enabling communication and digital activities of daily living. The ability to access brain signals reliably and continuously over many years post-implantation is crucial for iBCIs to be effective and feasible. This study investigates the signal characteristics and long-term stability of neural activity recorded with a stent-electrode array over 1 year post-implant.Methods:We report on five participants with paralysis who were enrolled in an early feasibility clinical trial of an endovascular iBCI (Stentrode; ClinicalTrials.gov, NCT05035823). Each participant was implanted with a 16-channel stent-electrode array, deployed in the superior sagittal sinus to record bilaterally from the primary motor cortices. Neural activity was recorded during home-based sessions while the participants performed a set of standardized tasks. Metrics including motor signal strength during attempted movement, resting state signal features, and electrode impedances were quantified over time.Results:Motor-related modulation in neural activity was exhibited in the high-frequency bands (30–200 Hz) during attempted movements, with rest and attempted movement states showing sustained differentiation over time. Impedance and resting state band power for most channels did not change significantly over time.Conclusions:These findings provide strong evidence that the endovascular BCIs may be suitable for long-term neural signal acquisition in the home environment, demonstrating the ability to record movement-related modulation over one year.

Abstract A computationally efficient strategy is presented for adjusting analytic rotordynamic models to make them consistent with experimental data. The approach permits use of conventional rotordynamic models derived using finite element methods in conjunction with conventional plant identification models derived from impact or sine sweep testing in a transfer function or influence coefficient format. The underlying assumption is that the predominant uncertainties in engineered models occur at discrete points as effects like shrink fits, seal coefficients or foundation interactions. Further, it is assumed that these unmodeled or poorly modeled effects are essentially linear (at least within the testing and expected operating domains). Matching is accomplished by deriving a dynamic model for these uncertain effects such that the resulting composite model has a transfer function which matches that obtained experimentally. The derived augmentations are computationally compatible with the original rotor model and valid for stability or forced response predictions. Further, computation of this augmentation is accomplished using well developed and widely disseminated tools for modern control. Background theory and a complete recipe for the solution are supported by a number of examples.