Intracortical 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.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/1x3x-qz20 |
| Date | January 2023 |
| Creators | Perkins, Sean McClintock |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
Page generated in 0.0018 seconds