서브메뉴
검색
Classification of Neuromechanical Control Strategy in a Wrist Rotation Task
Classification of Neuromechanical Control Strategy in a Wrist Rotation Task
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105658
- ISBN
- 9798263307431
- DDC
- 310
- 서명/저자
- Classification of Neuromechanical Control Strategy in a Wrist Rotation Task
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 75 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Hernandez, Manuel.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
- 초록/해제
- 요약Speed-accuracy trade-offs exist in a variety of neural tasks. It is the goal of this work to establish the presence of and predictability of a motor control strategy during a wrist rotation task. Participants were asked to perform a series of continuous and discrete wrist rotations. This motion data was clustered into segments of either speed or range of motion oriented control strategy, controlling for age cohort, continuity of task, and motion type. Age-related changes in motion and cortical data were explored, as were control strategy related changes. Finally, competing neural ordinary differential equation (NODE) and random forest models were fit to explore the ability to classify control strategy using cortical data alone. The clustering method was found to be successful in establishing control strategy. While age-related changes were not prevalent in direct exploration of motion data, older adults are found to have a lower speed with prioritizing speed as compared to young adults doing the same. Control strategy differences were present in the primary motor cortex at the N1, N2, and P3 components, at the supplementary motor area in the P1 component, and in the prefrontal cortex using prefrontal negativity. When using both motor and prefrontal cortical inputs to competing models, models perform with a similar accuracy but the NODE model was able to train and test data at a much faster pace compared to the random forest.
- 일반주제명
- Statistics
- 일반주제명
- Neurosciences
- 일반주제명
- Physical therapy
- 일반주제명
- Aging
- 키워드
- Control strategy
- 키워드
- Machine learning
- 키워드
- Neuromechanics
- 기타저자
- University of Illinois at Urbana-Champaign Neuroscience Program
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2024 us c eng d■001000017361052
■00520260202105658
■006m o d
■007cr#unu||||||||
■020 ▼a9798263307431
■035 ▼a(MiAaPQ)AAI32409812
■035 ▼a(MiAaPQ)124314
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aZiegelman, Liran.
■24510▼aClassification of Neuromechanical Control Strategy in a Wrist Rotation Task
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a75 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Hernandez, Manuel.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
■520 ▼aSpeed-accuracy trade-offs exist in a variety of neural tasks. It is the goal of this work to establish the presence of and predictability of a motor control strategy during a wrist rotation task. Participants were asked to perform a series of continuous and discrete wrist rotations. This motion data was clustered into segments of either speed or range of motion oriented control strategy, controlling for age cohort, continuity of task, and motion type. Age-related changes in motion and cortical data were explored, as were control strategy related changes. Finally, competing neural ordinary differential equation (NODE) and random forest models were fit to explore the ability to classify control strategy using cortical data alone. The clustering method was found to be successful in establishing control strategy. While age-related changes were not prevalent in direct exploration of motion data, older adults are found to have a lower speed with prioritizing speed as compared to young adults doing the same. Control strategy differences were present in the primary motor cortex at the N1, N2, and P3 components, at the supplementary motor area in the P1 component, and in the prefrontal cortex using prefrontal negativity. When using both motor and prefrontal cortical inputs to competing models, models perform with a similar accuracy but the NODE model was able to train and test data at a much faster pace compared to the random forest.
■590 ▼aSchool code: 0090.
■650 4▼aStatistics
■650 4▼aNeurosciences
■650 4▼aPhysical therapy
■650 4▼aAging
■653 ▼aNeural ordinary differential equation
■653 ▼aControl strategy
■653 ▼aElectroencephalography
■653 ▼aMachine learning
■653 ▼aNeuromechanics
■690 ▼a0317
■690 ▼a0463
■690 ▼a0382
■690 ▼a0493
■71020▼aUniversity of Illinois at Urbana-Champaign▼bNeuroscience Program.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361052▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
