Leland Stanford Junior University

California Institute of Technology

Cambridge Enterprise Limited

Krishna V Shenoy

Jonathan C Kao

Sergey Stavisky

Jaimie M Henderson

Paul Nuyujukian

Richard A Andersen

David Sussillo

John Patrick Cunningham

Byron M Yu

Francis Robert Willett

Sohaib A Kureshi

Nir Even-Chen

James S Harris, Jr

Teresa H Meng

Joel W Burdick

Partha Pratim Mitra

Gopal Santhanam

Bijan Pesaran

Evan P Thrush

Ofer Levi

Mark M Churchland

Chethan Pandarinath

Stephen I Ryu

Stepehn J Smith

Caleb T Kemere

Daniella Meeker

Shiyan Cao

Vikash Gilja

Cynthia A Chestek

Systems and methods for decoding indented speech from neuronal activity in accordance with embodiments of the invention are illustrated. One embodiment includes a neuronal speech system for decoding intended speech from neuronal signals includes a neuronal signal recorder implanted into a user's brain, including a multielectrode array, controller circuitry, and a communication circuitry capable of transmitting data to a neuronal signal decoder, the neuronal signal decoder located externally from the user's body, including a processor, an input/output interface, and a memory, where the memory contains a neuronal speech application that directs the processor to obtain neuronal signal data from the neuronal signal recorder, where the neuronal signal data describes neuronal activity proximal to the implanted neuronal signal recorder, decode speech features from the neuronal signal data using a neuronal decoding model, construct a speech string from the decoded speech features; and output the constructed speech string via an output device.

Systems and methods for decoding intended speech from neuronal activity

Systems and methods for decoding intended symbols from neural activity in accordance with embodiments of the invention are illustrated. One embodiment includes a symbol decoding system for brain-computer interfacing, including a neural signal recorder implanted into a brain of a user, and a symbol decoder, the symbol decoder including a processor, and a memory, where the memory includes a symbol decoding application capable of directing the processor to obtain neural signal data from the neural signal recorder, estimate a symbol from the neural signal data using a symbol model, and perform a command associated with the symbol.

Systems and methods for decoding intended symbols from neural activity

Systems and methods decoding intended symbols from neural activity

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&#x2014;or not at all in some circumstances&#x2014;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.

Multiplicative recurrent neural network for fast and robust intracortical brain machine interface decoders

Systems and methods for using virtual keyboards for high dimensional controllers in accordance with embodiments of the invention are illustrated. One embodiment includes a virtual keyboard system including a processor, and a memory, including a virtual keyboard application, where the virtual keyboard application directs the processor to display a plurality of 3D keys in a virtual environment, where each 3D key represents at least one symbol via a display device, display a cursor in the virtual environment, where the cursor is movable in at least three dimensions via the display device, obtain a user input data from an input device, move the cursor to a 3D key in the plurality of 3D keys based on the user input data, and record the at least one symbol represented by the 3D key.

Systems and methods for virtual keyboards for high dimensional controllers

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.

Task-outcome error signals and their use in brain-machine interfaces

A brain machine interface (BMI) for restoring performance of poorly performing decoders is provided. The BMI has a decoder for decoding neural signals for controlling the brain machine interface. The decoder separates in part neural signals associated with a direction of movement and neural signals associated with a speed of movement of the brain machine interface. The decoder assigns relatively greater weight to the neural signals associated with a direction of movement.

Brain-machine interface utilizing interventions to emphasize aspects of neural variance and decode speed and angle using a kinematics feedback filter that applies a covariance matrix

A brain machine interface for control of prosthetic devices is provided. In its control, the interface utilizes parallel control of a continuous decoder and a discrete action state decoder. In the discrete decoding, we not only learn states affiliated with the task, but also states related to the velocity of the prosthetic device and the engagement of the user. Moreover, we not only learn the distributions of the neural signals in these states, but we also learn the interactions/transitions between the states, which is crucial to enabling a relatively higher level of performance of the prosthetic device. Embodiments according to this parallel control system enable us to reliably decode not just task-related states, but any 'discrete action state,' in parallel with a neural prosthetic 'continuous decoder,' to achieve new state-of-the-art levels of performance in brain-machine interfaces.

Brain machine interface utilizing a discrete action state decoder in parallel with a continuous decoder for a neural prosthetic device

A brain-machine interface is provided that incorporates a neural dynamical structure in the control of a prosthetic device to restore motor function and is able to significantly enhance the control performance compared to existing technologies. In one example, a neural dynamical state is inferred from neural observations, which are obtained from a neural implant. In another example, the neural dynamical state can be inferred from both the obtained neural observations and from the kinematics. A controller interfaced with the prosthetic device uses the inferred neural dynamical state as input to the controller to control kinematic variables of the prosthetic device.

Brain machine interfaces incorporating neural population dynamics

Artificial control of a prosthetic device is provided. A brain machine interface contains a mapping of neural signals and corresponding intention estimating kinematics (e.g. positions and velocities) of a limb trajectory. The prosthetic device is controlled by the brain machine interface. During the control of the prosthetic device, a modified brain machine interface is developed by modifying the vectors of the velocities defined in the brain machine interface. 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.

Brain machine interface

Characteristics of biological substances, such as cerebral cortex matter, are sensed. According to an example embodiment, the present invention is directed to a negligibly-intrusive, multi-layer integrated circuit arrangement for monitoring activity of an area of a cerebral cortex that would normally be covered by an anatomical layer. The multi-layer integrated circuit arrangement includes an optics layer located outside the cerebral cortex area that includes an emitter and a detector. The optics layer is adapted for implantation in the anatomical layer and for sensing at least one brain-activity parameter. The multi-layered integrated circuit arrangement also includes a data-processing layer that includes a digital-processing circuit that is adapted for assimilating neural data in response to the optics layer sensing at least one brain-activity parameter.

Implantable sensing arrangement and approach

A brain machine interface for decoding neural signals for control of a machine is provided. The brain machine interface estimates and then combines information from two classes of neural activity. A first estimator decodes movement plan information from neural signals representing plan activity. In one embodiment the first estimator includes an adaptive point-process filter or a maximum likelihood filter. A second estimator decodes peri-movement information from neural signals representing peri-movement activity. Each estimator is designed to estimate different aspects of movement such as movement goal variables or movement execution variables.

Decoding of neural signals for movement control

A prosthetic system may use a decoder to predict an intended action, such as a reach, from processed signals generated from measured neural activity. The decoder may included a cognitive state machine, which transitions between cognitive states based on transition rules.

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Palo Alto, CA, United States of America

Krishna V Shenoy