Cortical basis of complex motor sequences in humans for neural interfaces

Investigator Names  Jaimie Henderson, Frank Willett, Leigh Hochberg, Daniel Rubin, David Sussillo, John Simeral, Melanie Fried-Oken, Carlos Vargas-Irwin, Henry Greely, Michael Young, John Donoghue, Allison Okamura, Sydney Cash
Institutions Stanford University, Brown University, Massachusetts General Hospital, Oregon Health Sciences University, 
Grant support: NIH BRAIN Initiative, U01NS123101
Performance period  9/22/2021 - 8/31/2026


Goal: Intracortical brain-computer interfaces (iBCIs) can restore lost function for people with severe speech and motor impairment (SSMI) due to neurological injury or disease. Despite tremendous recent progress, iBCI performance remains well below that of able-bodied people. In prior NIHsupported research, our collaborative team developed a high-performance intracortical braincomputer interface (iBCI) that decodes arm movement intentions directly from brain activity. This technology has allowed people with SSMI to control a computer cursor with sufficient speed and accuracy to type at up to 8 words/min and has enabled full control of unmodified consumer devices using only decoded motor cortical activity. In the proposed U01 clinical research, we will take an important next step for the field: investigating neural ensemble encoding during complex tasks that only people are capable of performing (i.e., moving arbitrary combinations of limbs and body parts, and handwriting). This work will build upon decades of experience in studying the motor system in humans and non-human primates, with the end goal of advancing iBCI technology, and will be performed as part of the multi-site BrainGate consortium. We propose to study in detail, at 'coarse' and 'fine' scales, how the Precentral Gyrus (PCG; “motor cortex”) generates complex movements. We will base our investigations on two new key discoveries from our lab: 1) that a small area of the PCG encodes movements of all 4 limbs in a ‘compositional’ way, allowing differentiation of separate limb and movement encoding dimensions, and 2) that complex, dexterous movements such as handwriting can be accurately decoded from the PCG of people with paralysis. The results of these detailed fundamental neuroscience studies will enable us to then design and demonstrate two entirely new iBCIs: a system for helping restore continuous motion of the entire body in virtual reality (‘Whole-Body iBCI') and a system to substantially increase on-screen text generation speed (‘Handwriting iBCI’). Finally, we will continue to evaluate the safety profile of Utah-array based iBCIs through the ongoing BrainGate2 pilot clinical trial, with particular emphasis on critical neuroethics considerations. Upon completion, this project will advance both the capabilities of iBCIs for restoration of lost function and our understanding of the detailed neural mechanisms of complex movements.

Study population and setting:

  • Research participants: People with tetraplegia (weakness of all 4 limbs) from spinal cord injury, brainstem stroke, or ALS
  • Recording device: Blackrock Neuroport

Data generated:

  • Neuronal ensemble recordings, task-related behavior. Data will be formatted into a “.mat” file which can be loaded using MATLAB or open-source tools in Python (scipy toolkit), which to our knowledge are the two most common data analysis tools used in the field. An accompanying readme file will describe the contents of each variable included in the .mat dataset  (e.g. as we have done previously Data will be archived on DABI 


  • Lab web site: 

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