EAGER: Detecting Real and Imagined Movement of Individual Fingers from Scalp EEG

EAGER：通过头皮脑电图检测单个手指的真实和想象的运动

• 批准号: 2038081
• 负责人: Charles Anderson
• 金额: $ 9.77万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038081&HistoricalAwards=false
• 关键词: EAGER; Detecting; Real; Imagined; Movement

The long-term goal of brain-computer interface (BCI) research is to establish a new mode of communication for individuals who have lost some or all voluntary muscle control due to injury or degenerative diseases such as amyotrophic lateral sclerosis, multiple sclerosis, and severe cerebral palsy. If all voluntary muscle control is lost, a locked-in syndrome results in which a person is unable to communicate with the outside world. BCIs could potentially provide a way for these individuals to communicate with their caregivers and to control devices such as televisions, wheelchairs, and robot assistants. While BCI technology holds great promise, non-invasive BCI systems are not yet practical, primarily due to limitations in signal quality provided by current electroencephalogram (EEG) scalp electrodes. This project will explore initial steps towards a research plan that will transform BCI technology in ways that will enable breakthroughs in the reliability and accuracy of BCI applications. After years of limited advances in BCI accuracy and reliability, project outcomes will accelerate the design of new BCI applications to significantly improve the quality of life for many persons who are in dire need of help. The project will also play a strong role in the interdisciplinary education of computer science and biomedical students.In this exploratory project, equipment will be acquired to enable the recording of high-quality EEG signals generated by a new non-invasive tripolar concentric ring electrode EEG sensor being developed by Dr. Walter Besio of the University of Rhode Island, which enables the recording of brain activity with much more spatial and temporal precision than what is possible with conventional EEG electrodes. The EEG data thus obtained will provide the information needed by novel deep learning algorithms to translate brain activity to intended arm and hand movements, and experiments will be performed to demonstrate the feasibility of detecting real and imagined individual finger movements. The belief for decades has been that detecting finger movements requires invasive, implanted electrodes to avoid degradation of brain signals as they pass through cerebral-spinal fluid, skull and skin. This research will be the first to try a new end-to-end deep learning approach to translating brain activity to arm and hand movements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

脑-机接口（BCI）研究的长期目标是为那些因损伤或退行性疾病（如肌萎缩侧索硬化症、多发性硬化症和严重脑瘫）而失去部分或全部自主肌肉控制的个体建立一种新的交流模式。 如果失去了所有的自主肌肉控制，就会导致闭锁综合征，即一个人无法与外界交流。 脑机接口可能为这些人提供一种与他们的护理人员进行沟通的方式，并控制电视、轮椅和机器人助手等设备。 虽然BCI技术具有很大的前景，但非侵入性BCI系统尚未实用，主要是由于当前脑电图（EEG）头皮电极提供的信号质量的限制。 该项目将探索一项研究计划的初步步骤，该计划将改变BCI技术的方式，使BCI应用的可靠性和准确性取得突破。 经过多年在BCI准确性和可靠性方面的有限进步，项目成果将加速新BCI应用程序的设计，以显着提高许多急需帮助的人的生活质量。 该项目还将在计算机科学和生物医学学生的跨学科教育中发挥重要作用。在这一探索性项目中，将获得设备，以便记录由罗得岛大学的Walter Besio博士开发的新型无创三极同心环电极EEG传感器产生的高质量EEG信号，这使得能够以比常规EEG电极可能的更高的空间和时间精度记录大脑活动。 由此获得的EEG数据将提供新型深度学习算法所需的信息，以将大脑活动转化为预期的手臂和手部运动，并将进行实验以证明检测真实的和想象的个人手指运动的可行性。几十年来，人们一直认为，检测手指运动需要侵入性的植入电极，以避免大脑信号在穿过脑脊液、头骨和皮肤时发生退化。这项研究将首次尝试一种新的端到端深度学习方法，将大脑活动转化为手臂和手部运动。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。