CAREER: Leveraging Electroencephalography (EEG) Artifacts for Multimodal Neuromechanics

脑电图（EEG）被广泛用于研究大脑，并越来越多地用于研究行走和动态运动。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，这些伪信号（非大脑）需要被移除，然后经常被丢弃，即使伪信号包含了身体中发生的其他正在进行的过程的信息。这个CAREER项目的目标是开发新的电极和方法，用于分离和利用EEG伪信号（肌肉、眼睛、心脏和运动）来预测步态对称、眼睛注视和行走过程中的代谢成本等指标。该项目将建立单独的脑电图可以提供多种模式的指标来研究人类运动的神经力学（即神经过程和生物力学之间的相互作用）。这些努力将有助于加深对大脑过程和行走生物力学之间相互作用的理解，这可能会对随着衰老和疾病而发生的潜在的行动能力和认知缺陷提供更深入的了解。在整个项目中，教育和推广活动将使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，哥哥姐姐，老师，大学生等）提供STEM经验，以了解大脑，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领域取得成功。研究者的首要研究目标是发展对人类运动的脑动力学和神经力学的全面理解。要全面了解行走和运动适应过程中的大脑动力学，唯一的方法是在单个实验中使用多种方法来测量实际行走过程中的大脑活动和生物力学，这在目前是不切实际或不可行的。该项目将开发新的脑电图（EEG）传感器和方法，利用脑电图记录的大量源信号，然后将这些技术应用于裂带步行实验，从多模态神经力学的角度研究运动适应。中心假设是，通过盲源分离（独立分量分析，ICA）获得的EEG伪迹（肌肉、眼睛、心脏和运动）源信号比原始EEG更能预测生物力学指标（步态对称、眼睛注视和代谢成本）。研究计划有三个目标。第一个目标是通过台式实验开发和评估多个双面电极，以确定最能提高脑电源信号质量的双面电极类型。双面电极将传统的脑电信号记录在头皮上，同时测量可从头皮脑电信号中去除的孤立运动伪信号，提高了脑电信号与伪源信号的分离。第二个目标是评估多种机器学习技术，以从EEG伪影源信号中预测生物力学指标（步态对称、眼睛注视和代谢成本）。将人类参与者在不同条件下行走时的双层脑电图记录下来，并在三个单独的实验中产生固定的步态对称性、注视注视和代谢成本值，以获得机器学习分类器的训练和测试数据。第三个目的是利用先进的技术来确定脑皮层电动力学、步态对称、眼球注视和代谢成本与运动适应之间的关系。我们将进行一项扩展的分带行走实验，在分带跑步机上行走45分钟，记录两层脑电图。在这个项目中开发的技术将建立单独的EEG可以从一个综合的角度提供关于神经力学的新见解，在一个单一的实验中集成了大脑，运动学，视觉和代谢的测量。该项目还将强调脑电图有潜力用于理解不仅仅是大脑动力学，并将为该领域开发多模态神经力学的其他技术奠定基础。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。

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.