Enhancement and optimization of a mobile iBCI for Veterans with paralysis

皮质内脑机接口（IBCI）记录和处理来自大脑皮层的神经信号流。 植入大脑皮层的电极阵列能够快速、准确和直观地控制 为因脊髓损伤、中风 或肌萎缩侧索硬化症（ALS）。使用皮质内脑机接口，四肢瘫痪的人 能够使用他们想象的手部动作来命令在一个 电脑，使用虚拟键盘打字，使用聊天等通信应用程序，并浏览 web.想象的运动也被用来控制辅助设备，包括DEKA 假肢，辅助机械臂，甚至是一个人自己的瘫痪肢体， 电刺激麻痹的肌肉。一种微型无线信号的研究进展 发射器和无线，紧凑，电池供电的神经信号处理器已经提高了 有严重运动障碍的个人使用轮椅安装iBCI的可能性 在没有技术支持的情况下，在家独立工作。作为一种可行的辅助技术， iBCI不仅必须是移动的，而且必须是高性能、可靠和直观的。这 研究通过将算法创新转化为技术创新，增强了移动的iBCI的所有这些方面 在各种临床前研究中得到证实，并将其优化为稳定、高性能 在移动的iBCI中解码。这项研究首先将一个高度准确和反应灵敏的 运动神经解码器（一种深度学习递归神经网络），在移动的iBCI上运行 强大的嵌入式硬件。为了帮助稳定运动解码随着时间的推移， 提高性能，并简化校准要求，本研究然后期待的理论， 内在的神经流形，以适应降维（DR）技术，以高- 多维多尺度人类神经数据。接下来，最先进的数据科学方法是 与多类分析相结合，以促进对大量 iBCI用户想象的离散手势。接下来，对DR方法进行评估， 同时进行运动学和手势解码，实现更平滑、更准确和更稳定 iBCI控制。将这些累积的方法转化为嵌入式硬件形式来运行 在强大的移动的处理器上，提供对移动的和支持触摸的 使用类似鼠标的移动和手势（例如滑动滚动和捏动 zoom）。将独特的手势映射到其他功能将立即激活键快捷键或 手势到短语输出。使用这种安装在轮椅上的iBCI， 可以想象一个手势来生成文本到语音的问候或求助。总体而言，这 研究利用最先进的机器学习创新， 可靠、多功能的iBCI，以促进重度运动障碍患者的独立性。

Intracortical brain-computer interfaces (iBCIs) record and process neural signals streaming from arrays of electrodes implanted in the cortex to enable fast, accurate and intuitive control of assistive technologies for individuals living with paralysis arising from spinal cord injury, stroke, or amyotrophic lateral sclerosis (ALS). Using an intracortical BCI, people with tetraplegia have been able to use their imagined hand movements to command point-and-click actions on a computer, type with a virtual keyboard, use communication apps such as chat, and browse the web. Imagined movements have also been used to control assistive devices including the DEKA prosthetic arm, assistive robotic arms and even one’s own paralyzed limb through patterned electrical stimulation of paralyzed muscles. Recent development of a miniature wireless signal transmitter and a wireless, compact, battery-operated neural signal processor has raised the potential for individuals with severe motor disability to use a wheelchair-mounted iBCI independently at home without technical assistance. To be a viable assistive technology, the iBCI must be not only mobile but also high-performance, reliable, and intuitive to use. This research enhances all of these aspects of a mobile iBCI by translating algorithmic innovations demonstrated in varied pre-clinical studies and optimizing them toward stable, high-performance decoding in a mobile iBCI. This research first transforms a highly accurate and responsive kinematic neural decoder (a deep learning recursive neural network) to run on the mobile iBCI’s computationally powerful embedded hardware. To help stabilize kinematic decoding over time, enhance performance, and ease calibration requirements, this research then looks to theories of intrinsic neural manifolds to adapt dimensionality reduction (DR) techniques to high- dimensional, multiscale human neural data. Next, state-of-the-art data science approaches are integrated with multiclass analyses to promote reliable, accurate classification of a large set of discrete hand gestures imagined by iBCI users. Next, DR methods are evaluated to disentangle simultaneous kinematic and gesture decoding for smoother, more accurate and unperturbed iBCI control. These cumulative approaches will be translated to embedded hardware form to run on the powerful mobile processor to provide on-demand control of mobile and touch-enabled devices using both mouse-like movements and gestures (such as swipe-to-scroll and pinch-to zoom). Mapping unique gestures to additional functions will instantly activate key shortcuts or gesture-to-phrase output. Using this wheelchair-mounted iBCI, a speech-disabled individual could imagine a hand gesture to generate a text-to-speech greeting or call for help. Overall, this research leverages state-of-the-art machine learning innovations toward a more capable, reliable, and versatile iBCI to promote independence for people with severe motor disability.