公开(公告)号：US20180177619A1

公开(公告)日：2018-06-28

申请号：US15850625

申请日：2017-12-21

Applicant: California Institute of Technology

Inventor： Carey Y. Zhang ,  Tyson Aflalo ,  Richard A. Andersen

IPC: A61F2/72 ,  G06F3/01 ,  G06N3/06

CPC classification number: A61F2/72 ,  A61B5/04001 ,  A61B5/4851 ,  A61B5/6868 ,  A61B5/7264 ,  A61F4/00 ,  A61F2002/6827 ,  A61F2002/704 ,  G06F3/015 ,  G06N3/061

Abstract: In an embodiment, the invention relates to neural prosthetic devices in which control signals are based on the cognitive activity of the prosthetic user. The control signals may be used to control an array of external devices, such as prosthetics, computer systems, and speech synthesizers. Data obtained from a 4×4 mm patch of the posterial parietal cortex illustrated that a single neural recording array could decoded movements of a large extent of the body. Cognitive activity is functionally segregated between body parts.

公开(公告)号：US20210100663A1

公开(公告)日：2021-04-08

申请号：US17121218

申请日：2020-12-14

Applicant: California Institute of Technology

Inventor： Carey Y. Zhang ,  Tyson Aflalo ,  Richard A. Andersen

IPC: A61F2/72 ,  G06F3/01 ,  G06N3/06 ,  G06N20/00 ,  A61F4/00

Abstract: In an embodiment, the invention relates to neural prosthetic devices in which control signals are based on the cognitive activity of the prosthetic user. The control signals may be used to control an array of external devices, such as prosthetics, computer systems, and speech synthesizers. Data obtained from a 4×4 mm patch of the posterial parietal cortex illustrated that a single neural recording array could decoded movements of a large extent of the body. Cognitive activity is functionally segregated between body parts.

公开(公告)号：US20240046071A1

公开(公告)日：2024-02-08

申请号：US18230448

申请日：2023-08-04

Applicant: California Institute of Technology

Inventor： Tyson Aflalo ,  Benyamin A Haghi ,  Richard A Andersen ,  Azita Emami

IPC: G06N3/045 ,  G16H40/67 ,  G16H50/30 ,  G06N3/084 ,  G06F3/01

CPC classification number: G06N3/045 ,  G16H40/67 ,  G16H50/30 ,  G06N3/084 ,  G06F3/015

Abstract: An apparatus and method for a feature extraction network based brain machine interface is disclosed. A set of neural sensors sense neural signals from the brain. A feature extraction module is coupled to the set of neural sensors to extract a set of features from the sensed neural signals. Each feature is extracted via a feature engineering module having a convolutional filter and an activation function. The feature engineering modules are each trained to extract the corresponding feature. A decoder is coupled to the feature extraction module. The decoder is trained to determine a kinematics output from a pattern of the plurality of features. An output interface provides control signals based on the kinematics output from the decoder.