News Column

Patent Issued for Brain Machine Interface

August 13, 2014

By a News Reporter-Staff News Editor at Journal of Engineering -- From Alexandria, Virginia, VerticalNews journalists report that a patent by the inventors Gilja, Vikash (San Francisco, CA); Nuyujukian, Paul (Stanford, CA); Chestek, Cynthia A (Menlo Park, CA); Cunningham, John P (Saratoga, CA); Yu, Byron M. (San Jose, CA); Ryu, Stephen I (Menlo Park, CA); Shenoy, Krishna V. (Palo Alto, CA), filed on February 17, 2011, was published online on July 29, 2014.

The patent's assignee for patent number 8792976 is The Board of Trustees of the Leland Stanford Junior University (Palo Alto, CA).

News editors obtained the following quote from the background information supplied by the inventors: "Brain-machine interfaces (BMIs) translate action potentials from cortical neurons into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, offering disabled patients greater interaction with the world. BMIs have recently demonstrated considerable promise in proof-of-concept laboratory animal experiments, as well as in human clinical trials. However, two critical barriers to successful translation remain. First, current BMIs move considerably slower and less accurately than the native arm. Second, they do not sustain performance across hours and days, or across behavioral tasks, without human intervention. The present invention addresses this need for increased performance and robustness and advances the art of neural prosthetics."

As a supplement to the background information on this patent, VerticalNews correspondents also obtained the inventors' summary information for this patent: "In one embodiment of the invention a method is provided for artificial control of a prosthetic device (e.g. a robotic limb, a computer cursor, or the like). A brain machine interface is stored on a computer-readable medium and executable by a computer. The brain machine interface contains a mapping from neural signals to corresponding intention estimating kinematics of a limb trajectory. The intention estimating kinematics includes positions and velocities. The neural signals are signals from cortical neurons related to volitional tasks.

"The prosthetic device is controlled by the stored brain machine interface using recorded neural signals as input to said brain machine interface and the stored mapping of the brain machine interface to determine the intention estimating kinematics. The determined intention estimating kinematics then controls the prosthetic device and results in an executed movement of the prosthetic device.

"During the control of the prosthetic device, a modified brain machine interface is developed by modifying (and storing by the computer) the vectors of the velocities defined in the brain machine interface. Each of the modifications of the velocity vectors includes changing the direction of the velocity vector towards an end target of the executed movement of the prosthetic device. Once on target, the velocity is set to zero. In one example, the velocity vectors are modified and stored at discrete intervals over the course of the executed movement of the prosthetic device.

"The modified brain machine interface includes a new mapping of the neural signals and the intention estimating kinematics that can now be used to control the prosthetic device using recorded neural brain signals from a user of the prosthetic device. In one example, the intention estimating kinematics of the original and modified brain machine interface includes a Kalman filter modeling velocities as intentions and positions as feedback.

"In another embodiment of the invention a neural prosthetic is provided having a prosthetic device and a controller. The controller is executable by a computer and interfaced with the prosthetic device. The controller has encoded (and stored on the computer) a mapping of neural signals and kinematics of the prosthetic device. Control kinematics is determined by the controller to control the prosthetic device based on neural signals received from a user of the prosthetic device. In one example of this device, the controller includes a Kalman filter which encodes the velocity kinematics as intentions and position kinematics as feedback."

For additional information on this patent, see: Gilja, Vikash; Nuyujukian, Paul; Chestek, Cynthia A; Cunningham, John P; Yu, Byron M.; Ryu, Stephen I; Shenoy, Krishna V.. Brain Machine Interface. U.S. Patent Number 8792976, filed February 17, 2011, and published online on July 29, 2014. Patent URL:

Keywords for this news article include: Robotics, Machine Learning, Emerging Technologies, Brain-machine Interface, The Board of Trustees of the Leland Stanford Junior University.

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Source: Journal of Engineering

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