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Overview

Recent news:

1. Currently recruiting a Lab Manager to help run day-to-day operations in Prof. Shenoy's and Prof. Henderson's NPTL research group and in Prof. Shenoy's NPSL research group (appointed via HHMI at Stanford). url

2. Recent presentation to a general audience, covering some of our recent research: Shenoy KV (11/5/2021) Brain-to-text communication via imagined handwriting. 2021 Tencent WE Summit, Beijing, China. 32 minutes. YouTube (GreenScreen)

3. Recent overview article on BCIs / BMIs for a general audience, spanning basic academic science and engineering to emerging industry and commercialization efforts: Regaldo A (2021) A computer mouse inside your head. MIT Technology Review. Nov/Dec 2021 issue: 28-35. pdf url



The Stanford Neural Prosthetics Translational Laboratory (NPTL) conducts research aimed at providing clinically-useful brain-computer interfaces (BCIs) to people with paralysis. NPTL is co-directed by Professor Jaimie Henderson MD (Departments of Neurosurgery and, by courtesy, Neurology) and by Professor Krishna Shenoy PhD (Departments of Electrical Engineering and, by courtesy, of Bioengineering, Neurobiology and Neurosurgery), and is part of the Wu Tsai Neurosciences Institute, Bio-X Institute, School of Medicine and School of Engineering. BCIs are also referred to as brain-machine interfaces (BMIs) and cortical neural prostheses.  Our particular focus is on creating new types of communication interfaces, and advancing their performance such that they are useful to people, by working directly with participants attempting to make rapid, dexterous motor sequences as part of the BrainGate2 multi-site clinical trial (BrainGate2: Feasibility study of an intracortical neural interface system for persons with tetraplegia (NCT00912041); BrainGate.org; Timeline of selected BrainGate publications, url).  We investigate the underlying, fundamental human neuroscience (e.g., neural-population information representation and computations, often through the lens of dynamical systems) and fundamental engineering designs (e.g., machine-learning based decoder algorithms).  Our goal is to (1) measure voltage signals from large numbers of neurons simultaneously (i.e., action potentials from hundreds of individual neurons), (2) do so with neurosurgically-implanted electrode arrays where each electrode is mere microns from each neuron and thus maximum-available neural information is measured, in order to then (3) deeply understand this neural information (basic systems and computational neuroscience) and (4) to then create and demonstrate principled, high-speed and highly-accurate neural decoding algorithms that drive "high-bandwidth" communication between a person with paralysis (e.g., tetraplegia [1], anarthria [2]) and multiple computer and smart-home devies (e.g., mobile phones, tablets, computers, environmental control systems, doors). Projects are supported by the National Institutes of Health (NIH) NIDCD, NINDS and BRAIN Initiative, the Simons Foundation and the Howard Hughes Medical Institue (HHMI; Investigator Krishna Shenoy).

[1] Tetraplegia (sometimes referred to as quadriplegia) is a term used to describe the inability to voluntarily move the upper and lower parts of the body. The areas of impaired mobility usually include the fingers, hands, arms, chest, legs, feet and toes and may or may not include the head, neck, and shoulders. This is caused by upper spinal cord injury (SCI), brainstem stroke and other neurological diseases and injuries. [2] Anarthria is a severe form of dysarthria. Dysarthria is a motor speech disorder that occurs when someone can't coordinate or control the muscles used for speaking. People with dysarthria usually have slurred or slowed speech. People with anarthria, however, can't articulate speech at all. This is caused by neurological diseases and injuries including Amyotrophic Lateral Sclerosis (ALS) which is also known as Lou Gehrig's disease, Charcot's disease and Motor Neuron disease. ALS is a progressive neurodegenerative disorder.

 

Current projects include the fundamental neuroscience of highly-dexterous, human-only movement sequences and the engineering design and system validation of high-performance and highly-robust communication BCIs:

  1. Generate text to help restore communication by decoding attempted handwriting: "Brain-to-Text BCIs" (Willett et al. Nature 2021 pdf).
  2. Generate speech to help restore communication by decoding attempted speech: "Brain-to-Speech BCIs" (Stavisky et al. eLife 2019 pdf, Stavisky et al. J Neural Eng 2020 pdf, Wilson*, Stavisky* et al. J Neural Eng 2020 pdf).
  3. Generate full body (both arms, both legs) control signals to help restore arm and leg movements: "Full-body BCIs" (Willett*, Deo* et al. Cell 2020 pdf)
  4. Control of 2D point-and-click cursors to help restore computer, tablet and phone operation: "2D point-and-click BCIs" (Pandarinath*, Nuyujukian* et al. eLife 2017 pdf; Nuyujukian et al. PLoS One 2018 pdf).
  5. Fundamental neuroscience investigations of these uniquely human, high-speed and highly-dexterous movement sequences employing single-neuron resolution ensemble recordings.
  6. Advancing analytical methods: Computation Through Dynamics (CTD; Vyas et al. Ann Rev Neurosci 2020 pdf) and Dynamical Systems Framework (DSF; Shenoy et al. Ann Rev Neurosci 2013 pdf) experiments and analyses.

By investigating the natural motor control system and brain-computer interface (BCI) designs concurrently it is possible to gain new and different insights on both (top row). Electrode arrays are neurosurgically placed in motor-related cortical areas and various behavioral tasks are used to measure and interpret neural activity (2nd row, left to right). Many different BCIs, also termed brain-machine interfaces (BMIs) are possible, including full-body functional electrical stimulation (FES) or (soft) exoskeletons, attempted-handwriting BCIs, speech BCIs and 2D cursor point-and-click BCIs (3rd row, left to right). As an example of the close relationship between computational and systems neuroscience and BCI design, it is possible to understand how motor cortical neural populations encode upcoming movement segments (staight and curved) and relate them to letter writing, and then use this knowledge to decode attempted handwritting letters in order to provide high-performance BCIs (bottom row, left to right).  Illustration credit: Erika Woodrum. High-resolution version of the cover: pdf

 

Last modified: 
Sunday, November 14, 2021 - 10:30