자료유형  

 학위논문

Control Number  

0016933003

International Standard Book Number  

9798379759667

Dewey Decimal Classification Number  

610

Main Entry-Personal Name  

Perkins, Sean McClintock.

Publication, Distribution, etc. (Imprint  

[S.l.] : Columbia University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(183 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Wang, Qi;Churchland, Mark.

Dissertation Note  

Thesis (Ph.D.)--Columbia University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Intracortical brain-computer interfaces (BCIs) provide the means to do something extraordinary: restore movement to patients with paralysis or amputated limbs. Realizing this potential requires the development of decode algorithms capable of accurately translating measurements of neural activity, in real time, into appropriate time-varying commands for an external device (e.g. prosthetic limb). This problem is fundamentally interdisciplinary, drawing on tools and insights from engineering, neuroscience, statistics, and computer science, among others. Decode algorithms that have been favored historically tend to be computationally efficient, but perform suboptimally, likely because their assumptions fail to fully and accurately capture the complexity in neural population responses. Recent work harnessing the power of contemporary machine learning methods has raised the performance bar, yet these methods can be computationally demanding and it is unclear what properties of neural and/or behavioral data they exploit. In this dissertation, we characterize properties of motor-cortex population geometry and let these properties dictate decoder design, resulting in methods that perform very well, yet retain the benefits of simpler methods. We use this approach to develop a closed-loop navigation BCI, and to design a highly accurate, general, and interpretable decoder. The properties described in this dissertation have implications for any BCI. By designing decoders to explicitly respect (and leverage) these properties, we can construct powerful yet practical BCIs that better meet the needs of patients.

Subject Added Entry-Topical Term  

Biomedical engineering.

Subject Added Entry-Topical Term  

Neurosciences.

Subject Added Entry-Topical Term  

Biostatistics.

Index Term-Uncontrolled  

Brain-computer interfaces

Index Term-Uncontrolled  

Motor control

Index Term-Uncontrolled  

Motor cortex

Index Term-Uncontrolled  

Neural dynamics

Index Term-Uncontrolled  

Population geometry

Added Entry-Corporate Name  

Columbia University Biomedical Engineering

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:644085