Leland Stanford Junior University

Sergey Stavisky

Krishna V Shenoy

Jonathan C Kao

David Sussillo

Jaimie M Henderson

Nir Even-Chen

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

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.

Systems and methods for intracortical brain machine interface decoding

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

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.

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.

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San Francisco, CA, United States of America

Sergey Stavisky