Movement representation in human motor cortex and applications to brain-computer interface control
- Darrel R. Deo.
- [Stanford, California] : [Stanford University], 2019.
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- Deo, Darrel Rohit, author.
- Okamura, Allison, degree supervisor.
- Follmer, Sean, degree committee member.
- Shenoy, Krishna V. (Krishna Vaughn), degree committee member.
- Stanford University. Department of Mechanical Engineering.
- Intracortical brain-computer interfaces (iBCIs) have largely built upon work investigating the neural representation of overt reaching movements in nonhuman primates (NHPs). However, in people with paralysis, iBCIs leverage neural features related to attempted movement of paralyzed limbs, which may differ substantially from that of overt movement of unparalyzed limbs. To understand how paralysis affects movement representation in the motor cortex, we first compared direction- and distance-related neural tuning of a human participant's attempted arm movements and overt head movements to that of NHP overt arm movements. The participant's neural activity during overt head movement was most similar to NHP overt arm movement with strongest tuning to distance. To further clarify how attempted movement-related neural activity translates into iBCI control, the participant controlled a cursor using a series of different attempted movement strategies. We found that neural activity changes during iBCI control, becoming more similar across different strategies. Applying these gained insights, we designed and demonstrated a discrete neural decoding system which leveraged the neural representation of both overt and attempted movements to enable classification of up to 32 discrete movements across the body. The attempt to move a paralyzed limb also differs from overt movement of an unparalyzed limb in that haptic feedback normally accompanying the movement is lost or diminished. To better understand how haptic stimulation affects motor cortical neurons and iBCI control, we integrated a haptic feedback device into our iBCI system, which provided skin-shear haptic stimulation at the back of the participant's neck. We found motor cortical units that exhibited sensory responses to the stimuli, some of which were significantly tuned to the stimuli and well modeled by cosine-shaped functions. We also demonstrated online iBCI cursor control with continuous skin-shear feedback driven by decoded command signals. Cursor control performance increased slightly but significantly when the participant was given haptic feedback as compared to the visual feedback condition. This work deepens our understanding of how paralysis affects movement representation, delivers a novel discrete neural decoding system leveraging the movement representation of both paralyzed and unparalyzed limbs, provides insight into how motor cortical units respond to haptic stimulation, and shows how this stimulation affects iBCI control performance. These results can help guide and inform the design of future neural prostheses.
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- Submitted to the Department of Mechanical Engineering.
- Thesis Ph.D. Stanford University 2019.