Neuroprosthetics Lab
The goal of the UC Davis Neuroprosthetics Lab is to develop technology to restore abilities affected from neurological injury and disease. We build brain-computer interfaces (BCIs) focusing on helping people living with neurological impairments regain lost function. The group is part of the Department of Neurological Surgery and is led by Assistant Professors David Brandman and Sergey Stavisky.
Our current research focuses on restoring speech and reach and grasp. These motor BCIs can potentially help people with severe speech and motor impairment in the near-term, while providing direct access to human neural circuits for gaining the deeper neuroscientific understanding required to build BCIs that are more effective and capable of treating a wider range of conditions. We currently record using chronic Utah multielectrode arrays and with short-term sEEG recordings, while also working with partners to bring next-generation neurotechnologies to human use safely and quickly. Our expertise spans neuroengineering, systems neuroscience, neurosurgery, machine learning, and computational neuroscience.
Our group launched in October 2021 and is growing. If you’re interested in joining our team, you can find a list of opportunities here.
Our Team
David Brandman, MD PhD FRCSC (Co-Director)
I’m a board-certified neurosurgeon interested in brain computer interfaces (BCIs) to help people living with motor impairments. I completed my neurosurgical training at Dalhousie University. During residency I joined the Clinical Investigator Program and started doctoral research at Brown University. I joined the BrainGate research group, with Dr. Leigh Hochberg as my supervisor. My research focused on decoding algorithms for BCIs. After graduate school I studied with Dr. John Simeral, developing software platforms for real-time neural decoding in embedded systems.
I did my stereotactic functional / epilepsy neurosurgical fellowship at Emory University. I specialize in using neuromodulation and minimally invasive procedures to help people living with movement disorders, chronic pain, spasticity, and epilepsy.
Sergey Stavisky, PhD (Co-Director)
I’m a neuroscientist and neuroengineer and an Assistant Professor in the UC Davis Department of Neurological Surgery. I work at the intersection of systems and computational neuroscience, neuroengineering, and machine learning. I’m trying to understand how the brain controls movements, and to use this knowledge to build brain-computer interfaces that treat brain injury and disease.
I did my undergrad at Brown University, my PhD in the Stanford Neurosciences Program in the group of Dr. Krishna Shenoy, and my postdoc in the Stanford Neural Prosthetics Translational Laboratory led by Drs. Jaimie Henderson and Shenoy.
Trainees
Nick Card, BS (Postdoctoral Fellow, starting summer 2022)
I'm an incoming postdoc for the UC Davis NPL. I am currently a PhD candidate in the University of Pittsburgh's neural engineering program. My PhD research is centered around the development of imaging techniques for studying primate cortical connectivity in vivo at high resolution. My undergraduate research at Pitt Bioengineering was centered around brain-computer interfaces in behaving primates. I look forward to joining the NPL in Summer 2022!
Maitreyee Wairagkar, PhD (Postdoctoral Fellow)
I am a postdoc researcher in AI, brain-computer interface, affective robotics and rehabilitation and assistive technology. I am currently at Imperial College London and will be joining the UC Davis Neuroprosthetics Lab in spring 2022. I have completed my PhD in Cybernetics from the University of Reading, UK focusing on decoding motor intention from brain signals for BCI. My research focuses on developing intuitive modes of interaction with technology using brain signals, movements, natural language, and facial expressions with healthcare applications to support people with neurological disorders.
Lab Staff
Chaodan "April" Luo, BS (Junior Specialist, jointly with Prof. Randy O'Reilly)
I graduated from UC Davis with a degree in cognitive science with an emphasis in neuroscience in 2020. I joined Dr. Randall O’Reilly’s lab as a junior specialist in summer 2020 and have been helping design, running EEG experiments and analyzing EEG data. Driven by my interests in neuroengineering, I will participate in projects related to neurally-controlled speech devices in the UC Davis Neuroprosthetic Lab.
Tyler Singer-Clark, BS (Part-time Research Engineer)
I graduated from MIT in 2014 with a degree in Computer Science and Engineering. After 5 years in industry developing business software with the startup MaestroQA, I joined the BrainGate research team at Brown University in 2019 as a Research Engineer, with a focus on delivering a BCI system that can be used continuously in the home. I joined the UC Davis Neuroprosthetics Lab in 2021 to contribute to the research effort toward building a neurally-controlled speech device for use by people with paralysis.
Xianda Hou, BS (Masters student in Computer Science)
I’m a second-year graduate student at UC Davis with an interest in Natural Language Processing. After joining UC Davis Neuroprosthetics Lab, I’ve been working on building a real-time phoneme substitution tool to help analyze people's responses to audio latency and synthesized speech errors.
Venina Kalistratova, MA (Medical student)
I am a first-year medical student at UC Davis School of Medicine. I am interested in neurosurgery, language acquisition, prosody and phonetics. I have a background in Classics, linguistics and prosody of Greek and Latin poetry. I am interested in exploring the clinical significance of prosody and how it can be applied to improve speech in patients with neurological disorders.
Kushant Patel, MS (Data Scientist / Software Engineer)
I’m a recent grad from University of Waterloo (2021) with a Masters of Applied Science degree in Mechatronics Engineering. I specialize in machine learning and autonomous vehicles. I’ve spent 4 years working professionally at various firms including Tibco softwares, TopHat Robotics, Aquasensing and Watonomous as Data Analyst, Data Engineer and Robotics Engineer. I’ve keen interests in domains including computer vision and reinforcement learning. I’ll be joining the UC Davis Neuroprosthetics Lab as a full time Data Scientist/Software Engineer in Spring '22 to assist team in building a BCI system for restoring lost abilities.
Suvi Varshney, BS (Masters student in Computer Science, jointly with Prof. Lee Miller)
I am an MS in Computer Science student at UC Davis. My research interests lie in the amalgamation of Computer Vision and Natural Language Processing, specifically in smart personal assistants. At the Neuroprosthetics Lab, I am working on automated metrics for assessing neurally-synthesized speech quality.
Restoring speech
Much like how brain computer interfaces (BCIs) use brain signals to send commands to a robotic arm, a speech BCI could synthesize speech (or text) from the brain activity corresponding to attempted movements of the lips, jaw, tongue, and voice box in people who cannot speak. Building on recent progress in decoding speech using electrocorticography and stereoelectroencephalography signals, we’ve identified phonemes and synthesized speech from intracortical signals in the “hand knob” area of the motor cortex of people with tetraplegia.
The next steps are to decode continuous speaking using modern natural language processing frameworks; to incorporate brain signals from other areas related to speech production; and synthesize speech for a person who has lost the ability to do so.
Restoring reach and grasp
Restoring the ability of people with paralysis to reach and grasp with an arm and hand BCI, either through a robotic arm or by stimulating their own muscles, would go a long way towards restoring independence.
We’re developing methods to decode brain signals as a person tries to move, and use them to provide precise control of many arm and hand “degrees-of-freedom” (independent movements). Two of the challenges we’re going after are how to control additional degrees-of-freedom despite only recording from a modest number of electrodes in the brain, and how we can use artificial intelligence to compensate for it being difficult to handle objects without a sense of touch.
Next-generation human neural interfaces
The therapeutic capabilities of BCIs would be dramatically improved with new devices that can safely record and stimulate from more neurons, across multiple brain areas, in humans. We’re working with hardware and regulatory experts to design and test advanced neural interfaces for human use. We’ve recently posted apre-printthat describes what is, to our knowledge, the first recording from people using specially modified Neuropixels probes (intraoperatively). We are currently expanding in this direction to record from hundreds of neurons while participants are awake and can perform tasks during DBS placement.
Understanding how the brain produces movement
While the purpose of our clinical trial research is to advance therapies, these human brain recordings provide a chance to answer fundamental scientific questions about how the brain produces movements. By better understanding how the brain functions, we’re able to design better medical devices to treat brain injuries and diseases.
For example, we recently showed that brain cells in the “arm area” of cortex are also active during speaking and show similar neural ensemble dynamics motifs. Scientifically, this challenges the textbook view of a “motor homunculus” where parts of the motor cortex only care about a specific body part. Pragmatically, this finding has allowed us to prototype methods for restoring lost speech.
We will post about opportunities to participate in our clinical trial research here. At this time we are not actively recruiting clinical trial participants.
Our first major clinical trial will be aimed at developing a system in order to restore the ability to communicate using a brain-computer interface. This device would record brain activity related to when a person is trying to speak, and then translate the brain’s activity into computer synthesized words. This will involve neurosurgically placing a so-called “multielectrode array” into speech-related areas of the brain . We anticipate participants will need to live within a 3 hour driving radius of Sacramento, California. If you or someone you know has lost/is losing their ability to speak and would like to learn more about our research, please contact Dr. David Brandman.
Preprint manuscripts
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2019
SIMNETS: a computationally efficient and scalable framework for identifying networks of functionally similar neurons
Hynes JB, Brandman DM, Zimmerman JB, Donoghue JP, Vargas-Irwin CE bioRxiv. January. 463364 |
Peer reviewed manuscripts
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2022
Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex
Paulk AC, Kfir Y, Khanna A, Mustroph M, Trautmann EM, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Richardson MR, Williams* ZM, Cash* SS Nature Neuroscience. |
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2021
The neural representation of force across grasp types in motor cortex of humans with tetraplegia
Rastogi A, Willett FR, Abreu J, Crowder DC, Murphy B, Memberg WD, Vargas-Irwin CE, Miller JP, Sweet J, Walter NL, Rezaii PG, Stavisky SD, Hochberg LR, Shenoy KV, Henderson JM, Kirsch RF, Ajiboye AB eNeuro. |
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2020
Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus
Wilson* GH, Stavisky* SD, Willett FR, Avansino DT, Kelemen JN, Hochberg LR, Henderson** JM, Druckman** S, Shenoy** KV Journal of Neural Engineering. |
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2020
Power-saving design opportunities for wireless intracortical brain–computer interfaces
Even-Chen N, Muratore D, Stavisky SD, Hochberg L, Henderson J, Murmann B, Shenoy KV Nature Biomedical Engineering. |
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2020
Speech-related dorsal motor cortex activity does not interfere with iBCI cursor control
Stavisky SD, Willett FR, Avansino D, Hochberg LR, Shenoy* KV, Henderson* Journal of Neural Engineering. |
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2020
Neural Representation of Observed, Imagined, and Attempted Grasping Force in Motor Cortex of Individuals with Chronic Tetraplegia
Rastogi A, Vargas-Irwin C, Willett F, Abreu J, Crowder DC, Murphy B, Memberg W, Miller J, Sweet J, Walter B, Cash S, Rezaii P, Franco B, Saab J, Stavisky SD, Shenoy KV, Henderson J, Hochberg LR, Kirsch R, Ajiboye AB Scientific Reports. |
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2020
The Discriminative Kalman Filter for Nonlinear and Non-Gaussian Online Bayesian Filtering
Burkhart MC, Brandman DM, Franco B, Hochberg LR, Harrison MT Neural Computation. Mar 18:1-49 |
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2019
Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis
Stavisky SD, Willett FR, Wilson GH, Murphy BA, Rezaii P, Memberg WD, Miller JP, Kirsch RF, Hochberg LR, Ajiboye AB, Druckmann S, Shenoy KV*, Henderson JM* eLife. |
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2019
Accurate estimation of neural population dynamics without spike sorting
Trautmann E, Stavisky SD, Lahiri S, Ames KC, Kaufman M, O’Shea DJ, Vyas S, Sun X, Ryu S, Ganguli S, Shenoy KV Neuron. |
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2019
Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model
Willett FR, Young D, Murphy B, Memberg W, Blabe C, Pandarinath C, Stavisky SD, Rezaii P, Saab J, Walter B, Sweet J, Miller J, Henderson JM, Shenoy KV, Simeral JD, Jarosiewicz B, Hochberg LR, Kirsch R, Ajiboye AB Scientific Reports. |
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2019
Decoding Speech from Spike-Based Neural Population Recordings in Secondary Auditory Cortex of Non-Human Primates
Heelan C, Lee J, O'Shea R, Brandman DM, Truccolo W, Nurmikko AV Nature Communications Biology. 2:466 |
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2019
BCI performance comparison of recurrent neural network and Kalman filter decoders in retrospective simulation
Hosman T, Vilela M, Milstein D, Kelemen JN, Brandman DM, Hochberg LR, Simeral JD 9th International IEEE/EMBS Neural Engineering Conference on Neural Engineering. |
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2018
Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements
Stavisky SD, Kao JC, Nuyujukian P, Pandarinath C, Blabe C, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV Scientific Reports. |
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2018
Inferring single-trial neural population dynamics using sequential auto-encoders
Pandarinath C, O’Shea DJ, Collins J, Jozefowicz R, Stavisky SD, Kao JC, Trautmann EM, Kaufman MT, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV, Abbott LF, Sussillo D Nature Methods. |
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2018
Neural Population Dynamics Underlying Motor Learning Transfer
Vyas S, Even-Chen N, Stavisky SD, Ryu SI, Nuyujukian P, Shenoy KV Neuron. |
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2018
Feasibility of automatic error detect-and-undo system in human intracortical brain-computer interfaces
Even-Chen N, Stavisky SD, Pandarinath C, Nuyujukian P, Blabe CH, Hochberg LR, Henderson JM, Shenoy KV IEEE Transactions on Biomedical Engineering. |
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2018
Robust closed-loop control of a cursor in a person with tetraplegia using Gaussian process regression
Brandman DM, Burkhart MC, Kelemen J, Franco B, Harrison MT, Hochberg LR Neural Computation. November 30(11) |
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2018
Rapid calibration of an intracortical brain computer interface for people with tetraplegia
Brandman DM, Hosman T, Saab J, Burkhart MC, Shanahan BE, Ciancibello JG, Sarma AA, Milstein DJ, Vargas-Irwin CE, Franco B, Kelemen J, Blabe C, Murphy B, Young DR, Willett F, Pandarinath C, Stavisky SD, Kirsch RF, Walter BL, Ajiboye B, Cash SS, Eskandar EN, Miller J, Sweet J, Shenoy KV, Henderson JM, Jarosiewicz B, Harrison MT, Simeral JD, Hochberg LR Journal of Neural Engineering. January 24; 15(2) |
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2017
Augmenting intracortical brain-machine interface with neurally driven error detectors
Even-Chen N, Stavisky SD, Kao JC, Ryu SI, Shenoy KV Journal of Neural Engineering. |
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2017
Motor cortical visuomotor feedback activity is initially isolated from downstream targets in output-null neural state space dimensions
Stavisky SD, Kao JC, Ryu SI, Shenoy KV Neuron. |
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2017
Trial-by-trial motor cortical correlates of a rapidly adapting visuomotor internal model
Stavisky SD, Kao JC, Ryu SI, Shenoy KV Journal of Neuroscience. |
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2017
Review: Human Intracortical recording and neural decoding for brain-computer interfaces
Brandman DM, Cash SS, Hochberg LR Transactions on Neural Systems & Rehabilitation Engineering. 2017. March 2 |
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2016
The need for calcium imaging in nonhuman primates: new motor neuroscience and brain-machine interfaces
O’Shea D, Trautmann EM, Chandrasekaran C, Stavisky SD, Kao JC, Sahani M, Ryu SI, Deisseroth K, Shenoy KV Experimental Neurology. |
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2016
Making brain-machine interfaces robust to future neural variability
Sussillo* D, Stavisky* SD, Kao* JC, Ryu SI, Shenoy KV Nature Communications. |
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2015
A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes
Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV Journal of Neural Engineering. |
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2015
Neural point-and-click communication by a person with incomplete locked-in syndrome
Bacher D, Jarosiewicz B, Masse NY, Stavisky SD, Simeral JD, Cash SS, Friehs G, Hochberg, LR Neurorehabilitation & Neural Repair. |
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2015
Spike Train SIMilarity Space (SSIMS): a framework for single neuron and ensemble data analysis
Vargas-Irwin C, Brandman DM, Zimmerman J, Donoghue JP Journal of Neural Computation. Jan 27(1):1-31 |
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2014
Performance sustaining intracortical neural prostheses
Nuyujukian P, Kao JC, Stavisky SD, Fan JM, Ryu SI, Shenoy KV Journal of Neural Engineering. |
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2014
Information systems opportunities in brain-machine interface decoders
Kao JC, Stavisky SD, Sussillo D, Nuyujukian P, Shenoy KV Proceedings of the IEEE. |
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2014
Non-causal spike filtering improves the information content of threshold crossings of intracortical neural signals
Masse NY, Jarosiewicz B, Bacher D, Stavisky SD, Simeral JD, Hochberg LR, Donoghue JP Journal of Neuroscience Methods. |
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2012
A recurrent neural network for closed-loop intracortical brain-machine interface decoders
Sussillo D, Nuyujukian P, Fan JM, Kao JC, Stavisky SD, Ryu SI, Shenoy KV Journal of Neural Engineering. |
Peer reviewed conference proceedings
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2021
Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia
Simeral JD, Hosman T, Saab J, Vilela M, Franco B, Kelemen J, Brandman DM, Ciancibello JG, Flesher SN, Rezaii PG, Rosler DM, Shenoy KV, Henderson JM, Nurmikko AV, Hochberg LR IEEE Trans Biomed Eng. Mar 30 |
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2019
The Neuropixels probe: A CMOS based integrated microsystems platform for neuroscience and brain-computer interfaces
Dutta B, Andrei A, Harris TD, Lopez CM, O’Callahan J, Putzeys J, Raducanu BC, Severi S, Stavisky SD, Trautmann EM, Welkenhuysen M, Shenoy KV International Electron Devices Meeting (IEDM), San Francisco, USA. |
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2018
Decoding Speech from Intracortical Multielectrode Arrays in Dorsal "Arm/Hand Areas" of Human Motor Cortex
Stavisky SD, Rezaii P, Willett FR, Hochberg LR, Shenoy* KV, Henderson* JM Proceedings of the 40th Annual International Conference of the IEEE EMBS, Honolulu, USA. |
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2015
An auto-deleting brain machine interface: Error detection using spiking neuronal activity in the motor cortex
Even-Chen* N, Stavisky* SD, Kao JC, Ryu SI, Shenoy KV 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy. |
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2015
System Identification of Brain-Machine Interface Control Using a Cursor Jump Perturbation
Stavisky SD*, Kao* JC, Sorokin JM, Ryu SI, Shenoy KV Proceedings of the 7th International IEEE EMBS Conference on Neural Engineering, Montpellier, France. |
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2014
Hybrid Decoding of Both Spikes and Low-Frequency Local Field Potentials for Brain-Machine Interfaces
Stavisky SD, Kao JC, Ryu SI, Shenoy KV Proceedings of the 36th Annual International Conference of the IEEE EMBS, Chicago, USA. |
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2013
Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness
Stavisky SD, Kao JC, Ryu SI, Shenoy KV Proceedings of the 35th Annual International Conference of the IEEE EMBS, Osaka, Japan. |
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2010
Continuous Control of the DLR Light-weight Robot III by a Human with Tetraplegia Using the BrainGate2 Neural Interface System
Vogel J, Haddadin S, Simeral DJ, Stavisky SD, Bacher D, Hochberg LR, Donoghue JP, van der Smagt, P Proceedings of the International Symposium on Experimental Robotics, New Delhi, India. |
October 6, 2021
Our first grant! We’re delighted to announce that we were selected as one of the Simons Collaboration on the Global Brain’s Pilot Awards. This 2-year award (PI: Stavisky, Co-I: Brandman) is in collaboration with our colleagues at Stanford (PI: Henderson, Co-Is: Shenoy, Druckmann, Sussillo) and supports our work to understand single-neuron resolution ensemble dynamics across multiple brain areas underlying speech preparation and production.
October 1, 2021
The UC Davis Neuroprosthetics Lab has officially started. Hello world!
Code
Code for Stavisky et al. eLife 2020 “Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis” is here.
Datasets
We are committed to open dissemination of data while respecting the privacy of our research participants. Datasets will be posted here as they become available.
- Data for Stavisky et al. eLife 2020 “Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis” ishere.
- Data for Paulk et al. Nature Neuroscience 2022 "Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex" is available on Dryad: https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.d2547d840
Intellectual Property (IP)
Our mission is to generate and share knowledge in the service of human health, so why do we file patents? The way biomedical advances end up actually reaching and helping patients at scale (i.e., outside of early-stage academic clinical trials) is almost always through commercialization by startup and established medical companies. The economics of commercialization often require some IP protection in order to justify the high cost of bringing a therapy to market. Thus, part of our academic mandate is to file IP (through the university, e.g. UC Davis InnovationAccess). The university then works to license this IP to entities that are committed to best utilizing it to positively impact society. This means limiting exclusivity where possible, avoiding patent trolls, and imposing checkpoints to make sure that licensed technology is really making its way to market.
An exciting recent development is that as of the end of 2021, the IP below has been licensed by the neurotech industry. We look forward to seeing the knowledge we helped create make its way into clinical devices.
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Stavisky SD, Shenoy KV, Henderson JM, U.S. Patent Application No. US20190333505A1. Systems and Methods for Decoding Intended Speech from Neuronal Activity (filed April 2018, pending)
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Even-Chen N, Shenoy KV, Kao JC, Stavisky SD. U.S. Patent No. 10779746. Task-outcome error signals and their use in brain-machine interfaces (issued 09/22/2020)
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Sussillo D, Kao JC, Stavisky SD, Shenoy KV, U.S. Patent No. 10223634. Multiplicative recurrent neural network for fast and robust intracortical brain machine interface decoders (issued 3/5/2019)
Industry involvement
It is an exciting time for neurotechnology, with a rapidly growing ecosystem of startups and interest from established tech companies as well. This is a fantastic and mostly recent development which translates to better devices for research and medicine and a wide range of excellent career options for neurotechnology/neuroscience trainees. We know many of these firms and strive to support the wider ecosystem as best we can. For a good list of neurotech groups and companies, I suggest Krishna Shenoy’s list.
Furthermore, as UC faculty, we are encouraged to spend a small fraction of our time formally advising and consulting with industry. This serves our mission of sharing knowledge with the wider world outside of just academia, and it helps make us better educators and researchers, e.g. by understanding what skills our trainees will need to succeed in industry and what scientific/engineering challenges need to be solved to move technology from academic research to industry development.
We strongly believe in transparency, and thus will list our commercial relationships here.
Sergey Stavisky is Scientific Advisor to wispr.ai, a startup developing a non-invasive silent speech wearable. (2021 to present)
Sergey Stavisky Scientific Advisor to Nēsos Corp., a medical device company developing neurotechnology to affect the neuro-immune axis (e.g., rheumatoid arthritis) and mood disorders (e.g., postpartum depression). (2018 to present)
Join us
We are a new lab and are growing our team across a range of roles and career stages. For all roles, please send Drs. Brandman and Stavisky a brief cover letter of why you’re interested in our group (in the email body is totally fine), along with a CV and contact info for 1-3 references (we won’t contact them without your explicit permission). For more senior roles, please also attach one paper or work product (e.g., Github project) that you’re particularly proud of.
Graduate Students at UC Davis
We are taking graduate students! If you’re a current UC Davis Masters or PhD student and are interested in learning more and potentially rotating in/joining the lab, please contact us. Prof. Stavisky is a member of the Neurosciences Graduate Group, Computer Science Graduate Group, and the Biomedical Engineering Graduate Group, but we can affiliate with other graduate groups as needed if there’s a student who wants to work with us from another program.
Prospective Graduate Students
At UC Davis, candidates need to first apply and be accepted to the overall PhD or Masters program, before selecting a specific lab. First-year PhD students will rotate in several labs before committing to their longer-term PhD lab. The purpose of this is to help them broaden their experience and find a lab they like, and we think this a really great system. There isn't a way to directly apply to a specific lab, and individual faculty like us don't make the decision of who UC Davis will interview or accept (it's handled on behalf of the entire PhD program by an admissions committee).
Thus, we’d encourage prospective PhD students to apply to one or more of the UC Davis PhD programs, such as Electrical and Computer Engineering (potentially with Designated Emphasis in Biotechnology), Neuroscience, Biomedical Engineering, or Computer Science this autumn. Feel free to mention your interest in our group in your application materials and to send us an email when you’ve applied. If you are invited to interview, please let us know and we would make sure to be on your interview schedule and discuss potential opportunities in the UC Davis Neuroprosthetics Lab.
Postdoctoral Fellows
Please reach out if you want to gain advanced training in human neuroscience, BCI engineering, and have relevant background in some (not necessarily all) of the key requisite skill sets including signal processing, real-time software development, systems/computational neuroscience, speech or natural language processing, decoding/data science/machine learning.
Clinical Neurotechnology Research Assistant (CNRA)
In the next three months we plan to post this position, which involves being the key contact person for our clinical trial participants and actually goes to the participant’s home to run the research sessions that our research team has put together. For this crucial role we’re looking for highly organized individuals with excellent interpersonal skills and is tech savvy and comfortable learning how to operate our BCI technology with instruction from the PIs, postdocs, software engineer and grad students. A Bachelors Degree is required, and this role may be of high interest to someone considering going on to medical school, PhD program, or neurotechnology industry role after a couple of years (or staying on with us!).
Values
We are committed to making the UC Davis Neuroprosthetics Lab a welcoming and collaborative environment. We are striving to build a lab culture of open communication and mutual respect. There are no exceptions for “brilliant jerks” and no tolerance for harassment of any form. We believe that an environment where everyone feels safe and valued is the key to all of us being able to learn from one another. We hope to foster a scientific and educational environment that benefits from the unique perspectives, expertise, and hard work of every member of the team.
We also recognize that neuroengineering, and the wider society that it is inextricably a part of, is not safe for, fair to, or inclusive of all groups. It is part of our mission to improve diversity, equity, and inclusion in this field and to actively combat bias. In the coming months we will expand this section with specific initiatives we are involved in.
We are committed to the training and success of all of the members of our group. We support our trainees who want to pursue careers in academia, medicine, industry, government, etc. Our goal is to help you achieve your goals, and we want to work with you to tailor your experience in the lab. Even after you leave the lab, you can count on us to champion your cause. In return, we ask you to bring your best effort, creativity, and curiosity to your research; to conduct studies with the highest integrity and concern for the well-being of our research participants; to be kind to your colleagues; and, to be proactive about identifying obstacles and seeking advice. Lastly, research does not happen in a vacuum -- our doors are always open to listen and help (to the extent we can) with “outside of lab” challenges.