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.
