Quantifying neural variability and learning during real world brain-computer interface use

量化现实世界脑机接口使用过程中的神经变异和学习

• 批准号: 10548865
• 负责人: Jennifer L. Collinger
• 金额: $ 53.27万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10548865
• 关键词: Acceleration; Automobile Driving; Biomimetics; Calibration; Clinical; Cognitive; Complement; Computer software; Computers; Custom; Data; Development; Devices; Dimensions; Disabled Persons; Elements; Environment; Environmental Risk Factor; Fatigue; Goals; Home; Home environment; Intervention; Knowledge; Laboratories; Laboratory Study; Learning; Lighting; Limb structure; Longitudinal Studies; Measures; Monitor; Motion; Motor Cortex; Movement; Mus; Neuronal Plasticity; Noise; Output; Pain; Participant; Pattern; Performance; Physiological; Population; Positioning Attribute; Posture; Property; Quality of life; Self-Help Devices; Signal Transduction; Source; Speed; Stable Populations; Stress; System; Technology; Testing; Time; Training; Translating; Translations; brain computer interface; clinical translation; design; distraction; environmental change; experience; experimental study; grasp; home test; improved; motor learning; neural; neural patterning; neuromechanism; neurotransmission; population based; portability; response; skills; visual tracking

The performance of intracortical brain-computer interfaces (BCIs) has advanced substantially over the last decade, but these devices are not yet robust enough for the home environment, where they can truly improve quality of life for individuals with disabilities. To date, BCIs have depended on experienced technicians to operate large and complicated systems comprised of multiple computers, signal processors, neural recording headstages, and custom software. Our laboratory has developed a portable, battery-powered intracortical BCI system that enables independent in-home computer access. However, to achieve the long-term goal of true clinical viability, BCIs must also offer reliable and robust functional performance in the less well-controlled home environment. We have achieved robust and generalizable control of a computer cursor using a biomimetic approach that combines reach-based velocity control of cursor position with grasp-based control of mouse click onset and offset. This transient-based neural decoder allows for generalized click function, adding the ability to ‘click-and-drag’ when accessing a computer (similar to carrying an object) to the ‘point-and-click’ functionality that is typically implemented in BCIs. Independent home use of the BCI system will provide an opportunity to collect neural data over long periods of time during unstructured and varied tasks, enabling quantification of context-dependent neural variability due to subject-state (e.g., fatigue, pain, or stress) as well as plasticity due to learning. Understanding how neural signals vary over time will be critical for clinical BCI systems that must be robust, generalizable, and autonomous (i.e., operate for extended periods without technician intervention). This project will first quantify the impact of subject-state on movement-related neural activity and performance during in-home BCI use. This understanding is critical to developing robust BCIs that eliminate the need for recalibration even in uncontrolled environments. The extent to which subject-state information is represented in motor cortex and overlaps with BCI control dimensions will inform development efforts to mitigate the impact of these nuisance variables. Participants will use the BCI for a variety of self-selected computer access tasks over periods of many months that will challenge decoder performance. This project will investigate motor learning mechanisms that may be engaged to facilitate improvements in performance that generalize to many different tasks. Experiments will test the hypothesis that stable population-level neural activity emerges to strengthen movement-related activity while minimizing non-task-related neural variability. Finally, participants will undergo targeted neural training to determine if motor learning can be accelerated and whether different mechanisms of neural reorganization are engaged in response to interventions that challenge the speed and accuracy properties of the decoder in different ways. This project will improve our understanding of neural plasticity mechanisms during extended BCI use in a real-world environment. Ultimately this knowledge will enable stable, high- functioning BCI performance during independent home-use, which is critical for clinical translation.

皮质内脑机接口（bci）的性能在过去有了很大的进步