Patents by Inventor Sergey Stavisky

Sergey Stavisky has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10779746
    Abstract: A brain machine interface (BMI) for improving a performance of a subject is provided. The BMI has two decoders that act in real-time and in parallel to each other. The first decoder is for intention execution of a subject's intention. The second decoder is for error detection in a closed-loop error fashion with the first detector and to improve the performance of the first detector. Embodiments of this invention may enable an entirely new way to substantially increase the performance and robustness, user experience, and ultimately the clinical viability of BMI systems.
    Type: Grant
    Filed: August 11, 2016
    Date of Patent: September 22, 2020
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Nir Even-Chen, Krishna V. Shenoy, Jonathan C. Kao, Sergey Stavisky
  • Publication number: 20190333505
    Abstract: Systems and methods for decoding indented speech from neuronal activity in accordance with embodiments of the invention are illustrated.
    Type: Application
    Filed: April 30, 2019
    Publication date: October 31, 2019
    Applicant: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Sergey Stavisky, Krishna V. Shenoy, Jaimie M. Henderson
  • Publication number: 20190205731
    Abstract: A brain machine interface (BMI) to control a device is provided. The BMI has a neural decoder, which is a neural to kinematic mapping function with neural signals as input to the neural decoder and kinematics to control the device as output of the neural decoder. The neural decoder is based on a continuous-time multiplicative recurrent neural network, which has been trained as a neural to kinematic mapping function. An advantage of the invention is the robustness of the decoder to perturbations in the neural data; its performance degrades less—or not at all in some circumstances—in comparison to the current state decoders. These perturbations make the current use of BMI in a clinical setting extremely challenging. This invention helps to ameliorate this problem. The robustness of the neural decoder does not come at the cost of some performance, in fact an improvement in performance is observed.
    Type: Application
    Filed: March 4, 2019
    Publication date: July 4, 2019
    Inventors: David Sussillo, Jonathan C. Kao, Sergey Stavisky, Krishna V. Shenoy
  • Patent number: 10223634
    Abstract: A brain machine interface (BMI) to control a device is provided. The BMI has a neural decoder, which is a neural to kinematic mapping function with neural signals as input to the neural decoder and kinematics to control the device as output of the neural decoder. The neural decoder is based on a continuous-time multiplicative recurrent neural network, which has been trained as a neural to kinematic mapping function. An advantage of the invention is the robustness of the decoder to perturbations in the neural data; its performance degrades less—or not at all in some circumstances—in comparison to the current state decoders. These perturbations make the current use of BMI in a clinical setting extremely challenging. This invention helps to ameliorate this problem. The robustness of the neural decoder does not come at the cost of some performance, in fact an improvement in performance is observed.
    Type: Grant
    Filed: August 14, 2015
    Date of Patent: March 5, 2019
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: David Sussillo, Jonathan C. Kao, Sergey Stavisky, Krishna V. Shenoy
  • Publication number: 20170042440
    Abstract: A brain machine interface (BMI) for improving a performance of a subject is provided. The BMI has two decoders that act in real-time and in parallel to each other. The first decoder is for intention execution of a subject's intention. The second decoder is for error detection in a closed-loop error fashion with the first detector and to improve the performance of the first detector. Embodiments of this invention may enable an entirely new way to substantially increase the performance and robustness, user experience, and ultimately the clinical viability of BMI systems.
    Type: Application
    Filed: August 11, 2016
    Publication date: February 16, 2017
    Inventors: Nir Even-Chen, Krishna V. Shenoy, Jonathan C. Kao, Sergey Stavisky
  • Publication number: 20160048753
    Abstract: A brain machine interface (BMI) to control a device is provided. The BMI has a neural decoder, which is a neural to kinematic mapping function with neural signals as input to the neural decoder and kinematics to control the device as output of the neural decoder. The neural decoder is based on a continuous-time multiplicative recurrent neural network, which has been trained as a neural to kinematic mapping function. An advantage of the invention is the robustness of the decoder to perturbations in the neural data; its performance degrades less—or not at all in some circumstances—in comparison to the current state decoders. These perturbations make the current use of BMI in a clinical setting extremely challenging. This invention helps to ameliorate this problem. The robustness of the neural decoder does not come at the cost of some performance, in fact an improvement in performance is observed.
    Type: Application
    Filed: August 14, 2015
    Publication date: February 18, 2016
    Inventors: David Sussillo, Jonathan C. Kao, Sergey Stavisky, Krishna V. Shenoy