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Neural Coding of Finger Movements in Human Posterior Parietal Cortex and Motor Cortex


Guan, Charles (2023) Neural Coding of Finger Movements in Human Posterior Parietal Cortex and Motor Cortex. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/31rt-cy14.


We use our hands constantly in our everyday lives. This seemingly simple ability is disrupted in individuals with cervical spinal cord injuries. By circumventing injured signal pathways, brain-computer interfaces (BCIs) promise to enable such individuals to control artificial limbs for everyday use. However, existing BCI limb control remains coarse and inflexible, because we do not understand how the recorded neural activity relates to dexterous movement. As a result, BCI control in physical settings remains frustratingly difficult for paralyzed users. To improve dexterous BCI control, I studied the neural coding of individual finger movements in the posterior parietal cortex and motor cortex of tetraplegic participants. These regions are directly involved in dexterous hand movements and are candidates for BCI recording implants. Finger coding matched the correlation structure and dynamics of able-bodied usage, reflecting preserved motor circuits even after paralysis. Individual finger movements of each hand were coded in a factorized, correlated manner that still allowed decoding. Participants controlled artificial fingers with state-of-the-art accuracy. Finally, we studied the temporal dynamics of neural control to understand how existing models of neural activity extend to BCI control. These findings contribute to the understanding of human hand movements and advance the development of dexterous BCIs.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:brain-computer interface (BCI), brain-machine interface (BMI), neural prosthesis, hand, finger movements, posterior parietal cortex (PPC), motor cortex (MC), paralysis
Degree Grantor:California Institute of Technology
Division:Biology and Biological Engineering
Major Option:Bioengineering
Minor Option:Computer Science
Awards:Graduate Deans’ Award, 2023.
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Andersen, Richard A.
Thesis Committee:
  • Meister, Markus (chair)
  • Rutishauser, Ueli
  • Yue, Yisong
  • Andersen, Richard A.
Defense Date:29 March 2023
Funding AgencyGrant Number
National Institutes of Health (NIH)UG1EY032039
National Institutes of Health (NIH)5R01EY015545-12
Tianqiao and Chrissy Chen Brain-Machine Interface Center at CaltechUNSPECIFIED
Amazon AI4Science FellowshipUNSPECIFIED
Record Number:CaltechTHESIS:04092023-200347393
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Chapter 2 adapted for Chapter 3
Guan, Charles0000-0002-8040-8844
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15130
Deposited By: Charles Guan
Deposited On:16 May 2023 16:11
Last Modified:08 Nov 2023 00:08

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