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Enabling Brain-Computer Interface Co-adaptation with Performance-Related Signals
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Enabling Brain-Computer Interface Co-adaptation with Performance-Related Signals. |
| 개인저자 | Wilson, Nile R. |
| 단체저자명 | University of Washington. Bioengineering. |
| 발행사항 | [S.l.]: University of Washington., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 150 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781085694445 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Rao, Rajesh |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | Brain-Computer Interfaces (BCIs) have the potential to positively impact the lives of many people with sensorimotor disabilities due to neurological conditions, such as spinal cord injury and stroke. Through BCIs, individuals are able to interact with their surroundings and perform simple daily tasks independently, which once proved difficult or impossible without assistance. However, most BCIs are restricted to research settings due to cost, extensive setup, or need for researchers to monitor and modify the system. Many factors, such as difficulty of use and cost, prevent current BCIs from being integrated into regular daily use for health applications outside the lab or hospital. One direction of research hoping to push BCI out of the research setting focuses on enabling co-adaptation between the BCI and the user to increase BCI longevity and encourage better performance over time. This dissertation work focuses on investigating two performance related neural signals at the cortical level, namely error-related potentials and attention, and developing a co-adaptive BCI using these performance related signals to estimate subject performance during BCI use.We reveal the topography of cortical error-related potentials during a continuous control one-dimensional BCI task where subjects utilize motor imagery and rest to control the vertical velocity of a cursor to reach and dwell within a target. In this investigation, we found increased local activity in various cortical areas, including the somatosensory, motor, premotor, and parietal areas. We also investigate the effects of Default Mode Network (DMN) disruption on reaction time and on cortical activity and connectivity in a modified Stroop Task. In this work, we find that subject reaction time following auditory disruption of the DMN tends to be shorter than without disruption, and we find that connectivity between regions within the DMN tends to decrease with both auditory disruption and direct cortical stimulation disruption of the DMN. Lastly, we build and test a co-adaptive BCI using both error-related potentials and DMN activity to create "confidence scores" which estimate BCI performance. Our novel "confidence score" based co-adaptation was able to improve overall BCI performance across subjects and led to better performance than with no decoder adaptation. Using the same data, we use simulation to study the feasibility of applying transfer learning to allow first time users to attempt BCI control without training.Our findings not only contribute to a greater understanding of the presentation of two performance related signals at the cortical level and their roles in task contexts, but also contribute to the growing field of co-adaptive BCIs. Future BCIs may benefit from this co-adaptive design which relies on non-control neural signals already being recorded during BCI operation. Understanding various signals related to our internal performance monitoring system and how to utilize them to estimate BCI performance in a co-adaptive BCI system will enable future systems to adapt automatically over time without manual intervention from researchers. This should improve BCI longevity and use and bring these systems one step closer to being useful in at-home settings for potential users. |
| 일반주제명 | Bioengineering. Biomedical engineering. Neurosciences. |
| 언어 | 영어 |
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