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.

大脑计算机界面（BCI）研究的长期目标是为因受伤或退化性疾病（例如肌萎缩性侧面硬化症，多发性硬化症和严重的大脑瘫痪）等失去某些或所有自愿肌肉控制的个人建立新的交流方式。 如果所有自愿控制的肌肉控制丧失，则锁定综合症会导致一个人无法与外界交流。 BCIS有可能为这些人提供与照料者交流并控制电视，轮椅和机器人助手等设备的方法。 尽管BCI技术具有巨大的希望，但非侵入性BCI系统尚不实际，这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。 该项目将探索朝着研究计划的初步步骤，该研究计划将以能够在BCI应用程序的可靠性和准确性方面取得突破的方式来改变BCI技术。 经过多年在BCI准确性和可靠性方面的进步有限，项目成果将加速新的BCI应用程序的设计，以显着提高许多迫切需要帮助的人的生活质量。 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电极。 因此获得的脑电图数据将提供新的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动运动，并将进行实验以证明检测真实和想象中的单个手指运动的可行性。几十年来，人们一直认为，检测手指运动需要侵入性的，植入的电极，以避免脑信号通过大脑脊髓液，头骨和皮肤时降解。这项研究将是第一个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动作。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的审查标准，被认为值得通过评估来获得支持。