Enhancement and optimization of a mobile iBCI for Veterans with paralysis

为瘫痪退伍军人增强和优化移动 iBCI

• 批准号: 10538008
• 负责人: John David Simeral
• 金额: --
• 依托单位: PROVIDENCE VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10538008
• 关键词: 3-Dimensional; Address; Adoption; Algorithms; Amyotrophic Lateral Sclerosis; Artificial Arm; Calibration; Classification; Clinical; Communication; Computer software; Computers; Data; Data Science; Development; Devices; Dimensions; Disabled Persons; Electric Stimulation; Evaluation; Exhibits; Fingers; Gestures; Goals; Hand; Home; Human; Imagery; Implanted Electrodes; Individual; Internet; Intuition; Limb structure; Link; Machine Learning; Methods; Motor; Motor Cortex; Movement; Mus; Muscle; Neurofibrillary Tangles; Non-linear Models; Outcome; Output; Paralysed; Participant; Pattern; Performance; Persons; Precentral gyrus; Process; Quadriplegia; Reporting; Research; Running; Sampling; Self-Help Devices; Signal Transduction; Speech; Speed; Spinal cord injury; Stream; Stroke; Supervision; Tablets; Techniques; Technology; Text; Time; Touch sensation; Translating; Upper Extremity; User-Computer Interface; Veterans; Wheelchairs; arm; assistive robot; autoencoder; brain computer interface; deep learning; deep neural network; disability; feature extraction; finger movement; handheld mobile device; high dimensionality; improved; innovation; interest; kinematics; long short term memory; markov model; mobile application; neurotransmission; phrases; preclinical study; prototype; recurrent neural network; recursive neural network; relating to nervous system; simulation; theories; two-dimensional; virtual; wireless

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.

皮层内脑机接口（ibci）记录和处理来自大脑的神经信号