CAREER: Leveraging Electroencephalography (EEG) Artifacts for Multimodal Neuromechanics

脑电图（EEG）被广泛用于研究大脑，并越来越多地用于步行和动态运动。虽然EEG的预期用途是非侵入性地测量脑电活动，但EEG实际上记录了来自大脑、肌肉、眼睛、心脏和运动的电信号的混合。为了研究大脑，这些伪影（非大脑）信号需要被去除，然后通常被丢弃，即使伪影信号包含有关身体中发生的其他正在进行的过程的信息。该CAREER项目的目标是开发新的电极和方法，用于分离并利用EEG伪影（肌肉，眼睛，心脏和运动）信号来预测步态对称性，眼睛注视和步行过程中的代谢成本等指标。该项目将确定EEG单独可以提供用于研究神经力学的多种模式的指标（即，神经过程和生物力学之间的相互作用）。这些努力将有助于增加对大脑过程和行走生物力学之间相互作用的理解，这可能会对衰老和疾病导致的移动性和认知方面的潜在缺陷提供更深入的了解。在整个项目中，教育和推广活动将利用脑电图和神经力学，为女孩及其支持网络成员（父母、祖父母、年长的兄弟姐妹、教师、大学生等）分享STEM经验，了解大脑，生物力学，工程和人体运动。该项目的一个重点是帮助建立和加强倡导者的基础设施，以鼓励，指导和支持女孩和妇女在STEM中取得成功。研究者的首要研究目标是全面了解人类运动的大脑动力学和神经力学。形成对行走和运动适应过程中大脑动力学的全面理解的唯一方法是使用多种模式，这些模式可以在单个实验中测量实际行走过程中的大脑活动和生物力学，这目前是不切实际或不可行的。该CAREER项目将开发新的脑电（EEG）传感器和方法，以利用EEG中记录的大量源信号，然后将这些技术应用于分带步行实验中，以多模式神经力学的角度研究运动适应。中心假设是从盲源分离（独立分量分析，ICA）获得的EEG伪影（肌肉、眼睛、心脏和运动）源信号将比原始EEG更好地预测生物力学指标（步态对称性、眼睛注视和代谢成本）。研究计划有三个目标。第一个目标是使用台式实验开发和评估多个双面电极，以确定最能提高EEG源信号质量的双面电极类型。双侧电极记录头皮上的传统EEG信号，同时测量可能从头皮EEG中去除的孤立运动伪影信号，改善EEG中大脑和伪影源信号的分离。第二个目标是评估多种机器学习技术，以根据EEG伪影源信号预测生物力学指标（步态对称性、眼睛注视和代谢成本）。当人类参与者在不同的条件下行走时，将记录双层EEG，这些条件在三个单独的实验中产生步态对称性，眼睛注视和代谢成本的固定值，以获得机器学习分类器的训练和测试数据。第三个目标是使用已开发的技术来确定皮层电动力学、步态对称性、眼睛注视固定和代谢成本与运动适应的关系。将进行扩展的分裂带行走实验，其中当人类参与者在分裂带跑步机上行走时，将记录双层EEG，其中一条带比另一条带移动得更快，持续45分钟。在这个项目中开发的技术将建立EEG单独可以提供关于神经力学从一个全面的角度，在一个单一的实验中集成大脑，运动，视觉和代谢措施的新见解。该项目还将突出EEG有潜力用于理解不仅仅是大脑动力学，并将为该领域奠定基础，以开发更多的技术，为多模态neuromechanics.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

Electroencephalography (EEG) is widely used for studying the brain and increasingly during walking and dynamic movements. While the intended use of EEG is to non-invasively measure brain electrical activity, EEG actually records a mixture of electrical signals from the brain, muscles, eyes, heart, and motion. To study the brain, these artifact (non-brain) signals need to be removed and are often then discarded, even though the artifact signals contain information about other on-going processes occurring in the body. The goal of this CAREER project is to develop new electrodes and methods for separating and then leveraging EEG artifact (muscle, eye, heart, and motion) signals to predict metrics such as gait symmetry, eye gaze, and metabolic cost during walking. This project will establish that EEG alone can provide metrics from multiple modalities for studying neuromechanics (i.e., interaction between neural processes and biomechanics) of human locomotion. These efforts will help increase understanding of the interplay between brain processes and the biomechanics of walking, which may provide greater insight regarding the underlying deficits in mobility and cognition that occur with aging and disease. Throughout this project, educational and outreach activities will use EEG and neuromechanics to provide girls and members of their support network (parents, grandparents, older siblings, teachers, university students, etc.) shared STEM experiences to learn about the brain, biomechanics, engineering, and human movement. A key focus of this project is to help build and strengthen an infrastructure of advocates to encourage, guide, and support girls and women to succeed in STEM.The Investigator’s overarching research goal is to develop a comprehensive understanding of the brain dynamics and neuromechanics of human locomotion. The only way to form a comprehensive understanding of brain dynamics during walking and locomotor adaptation is to use multiple modalities that can measure brain activity and biomechanics during actual walking within a single experiment, which is currently impractical or infeasible. This CAREER project will develop new electroencephalography (EEG) sensors and methods to leverage the multitude of source signals that are recorded in EEG and then apply these technologies in a split-belt walking experiment to study locomotor adaptation with a multimodal neuromechanics perspective. The central hypothesis is that EEG artifact (muscle, eye, heart, and motion) source signals obtained from blind source separation (independent component analysis, ICA) will predict biomechanical metrics (gait symmetry, eye gaze, and metabolic cost) better than raw EEG. The research plan has three objectives. The FIRST Objective is to develop and evaluate multiple dual-sided electrodes using benchtop experiments to identify the type of dual-sided electrode that improves the quality of EEG source signals the most. Dual-sided electrodes record traditional EEG signals on the scalp while simultaneously measuring isolated motion artifact signals that can likely be removed from scalp EEG, improving separation of brain and artifact source signals in EEG. The SECOND Objective is to evaluate multiple machine learning techniques to predict biomechanical metrics (gait symmetry, eye gaze fixation, and metabolic cost) from EEG artifact source signals. Dual-layer EEG will be recorded as human participants walk in different conditions that produce fixed values of gait symmetry, eye gaze fixation, and metabolic cost in three separate experiments to obtain training and testing data for the machine learning classifiers. The THIRD Objective is to use the developed technologies to determine how electrocortical dynamics, gait symmetry, eye gaze fixation, and metabolic cost correlate with locomotor adaptation. An extended split-belt walking experiment will be conducted where dual-layer EEG will be recorded as human participants walk on a split-belt treadmill with one belt moving faster than the other belt for 45 minutes. The technologies developed in this project will establish that EEG alone can provide new insights about neuromechanics from a comprehensive perspective that integrates brain, kinematic, visual, and metabolic measures in a single experiment. This project will also highlight that EEG has the potential to be used for understanding more than just brain dynamics and will lay the foundation for the field to develop additional technologies for multimodal neuromechanics.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.