Brain-machine interfaces (BMIs) have progressed rapidly through technological advances that allow recording chronically from large numbers of neurons, more computational power, better training, and better regression algorithms for estimating neural tuning. However, current BMIs rely almost exclusively on fixed, linear models of neural encoding that are the same for all types of movements. These fixed models are the same whether the movements are fast or slow, and the same no matter which degrees of freedom require the most precision for a particular task. In part, this limitation of current BMIs reflects inadequate basic knowledge concerning how the brain controls movement at different levels of precision. To advance the field, the present project will test the hypotheses that 1) specific changes occur in the neural activity of the motor cortex depending on the precision required, and 2) more flexible, non-linear algorithms that adjust precision and actively select from multiple linear decoders will enable BMI performance closer to that of normal humans. This general hypothesis will be tested with two Specific Aims.
Aim 1 will examine selective neural encoding of movement precision when the instructed precision is varied systematically in a reach and grasp task.
Aim 2 will examine improving BMI performance using novel neural signal input to BMI output transforms. This novel BMI experiment will focus on using multiple dimensions of neural activity to improve precision along a given degree of freedom. In pursuing these Specific Aims, I will receive additional training to advance my career and transition from my current post-doctoral associate position to that of an independent, tenure-track faculty member by developing a research program that bridges the fields of neural control of movement, BMI design, and computational neuroscience. During my K99 years, I will advance my training in computational neuroscience, mentored by Drs. Sridevi Sarma and Robert Jacobs. With Dr. Sarma, I will focus on statistical modeling of neural activity using point process modeling. With Dr. Jacobs, I will focus on applying mixture models and gain-scheduling for BMIs. By the end of my K99 years, I thus will have expertise in three complementary disciplines?motor neurophysiology, BMI, and computational neuroscience?all contributing to the success of my transition to independence.

Public Health Relevance

The proposed studies will test ways to help patients with paralysis use a brain-machine interface to perform a more diverse set of tasks. The proposed methods and results of these studies will help to develop neuroprosthetic devices that allow movements that are more precise and intuitive to the user.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Transition Award (R00)
Project #
Application #
Study Section
Neurological Sciences Training Initial Review Group (NST)
Program Officer
Kukke, Sahana Nalini
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Kansas
Schools of Medicine
Kansas City
United States
Zip Code