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

脑机接口（BCI）研究的长期目标是为由于损伤或退行性疾病（如肌萎缩侧索硬化症、多发性硬化症和严重脑瘫）而失去部分或全部随意肌控制的个体建立一种新的交流模式。如果失去所有的随意肌控制，就会导致闭锁综合征，患者无法与外界交流。脑机接口可能为这些人提供一种与护理人员沟通的方式，并控制电视、轮椅和机器人助手等设备。虽然脑机接口技术具有很大的前景，但由于目前脑电图（EEG）头皮电极提供的信号质量有限，非侵入性脑机接口系统尚未实际应用。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，从而在BCI应用的可靠性和准确性方面取得突破。在脑机接口的准确性和可靠性方面多年来进展有限，项目成果将加速新的脑机接口应用程序的设计，从而显著改善许多急需帮助的人的生活质量。该项目也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。在这个探索性项目中，将获得设备，以记录由罗德岛大学的Walter Besio博士开发的新型无创三极性同心环电极脑电图传感器产生的高质量脑电图信号，该传感器能够以比传统脑电图电极更高的空间和时间精度记录大脑活动。由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，将大脑活动转化为预期的手臂和手部运动，并将进行实验来证明检测真实和想象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电极，以避免大脑信号在通过脑脊液、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的运动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.