Overview

Existing assistive devices for people with severe motor disabilities rely heavily on residual motor function. Recent brain-computer interfaces (BCIs), which decode neural activity directly from the brain, provide disabled persons with limited ability to autonomously control assistive devices. We have invented a neural interface system that can form a bridge across the compromised neural pathway, restoring motor function of disabled persons to functional level.

Market Opportunity

Existing assistive devices for people with severe motor disabilities are inherently limited, as they rely heavily on residual motor function. More recently, brain-computer interfaces (BCIs) have been used to control assistive devices such as computer cursors or robotic arms by decoding neural activity directly from the brain. These BCIs, however, provide disabled persons with limited ability to autonomously control their devices. A method to enable disabled persons to autonomously control assistive devices could greatly improve their quality of life.

Innovation and Meaningful Advantages

We have invented a neural interface system that can form a bridge across the compromised neural pathway, restoring motor function of disabled persons to functional level. Examples include control of prosthetic limbs and remotely controlled robotic arms. With our method, which is based on context-aware calibration in biosignal-controlled systems, an object is moved along an actual decoded direction, determined by the output of a decoder configured to correlate for at least one time segment in the intended direction. Our method controls assistive devices with minimal or no need for task interruption. It can also be used to provide a bridged neural pathway for able-bodied persons and to measure biological signals for diagnostic purposes.

Collaboration Opportunity

We are interested in exploring 1) startup opportunities with investors in the medical device space; 2) research collaborations with leading medical device companies; and 3) licensing opportunities with medical device companies.

Principal Investigator

IP Information

US Utility 9,851,795 B2, Issued December 26, 2017

Publications

Jarosiewicz B, Masse NY, Bacher D, Cash SS, Eskandar E, Friehs G, Donoghue JP, and Hochberg LR. Advantages of closed-loop calibration in intracortical brain–computer interfaces for people with tetraplegia. Journal of Neural Engineering 2013 July 10;10(4). doi: 10.1088/1741-2560/10/4/046012.

Jarosiewicz B, Sarma AA, Bacher D, Masse NY, . . . and Hochberg LR. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Science Translational Medicine 2015 Nov 11;7(313):313ra179. doi: 10.1126/scitranslmed.aac73.

Contact