Autonomous Brain Machine Interface

An advanced system that enables patients to better control prosthetic devices. Background: Many pathologies lead to loss of the ability to use one or more limbs. Historically, the lost or damaged limb is replaced with a prosthetic device. In recent years prosthetic technology has been developed to enable the control of these devices via a neural signal from the patient. These brain/machine interfaces (BMI) are advancing towards true functional replacement of limbs. BMI technology uses mathematical algorithms to translate the patient's intentions via their neural activity. To date, most BMI systems are based on supervised learning, where the patient's intention, actual motion, and target are known. This usually requires somewhat confined conditions, such as those found in a laboratory. Technology Overview: The subject technology developed by SUNY Downstate Health Sciences University researchers is a BMI system that uses a "reward/expectation" signal derived from a motor cortex within the patient's brain. This allows the system to be updated without manual intervention from the experimenter. The technology incorporates a "policy" that determines how detected signals emanating from the patient's brain are translated into action. The system can provide a command signal resulting in a first action by the prosthetic device. It can also detect an evaluation signal emanating from the patient's brain in response to the first action. The system can adjust the policy based on the evaluation signal. This allows for more timely, precise, and natural control of the prosthetic device. Advantages:

Applications: The primary application for this technology is for patients to control their prosthetic devices through neural signals. Intellectual Property Summary: This technology is covered by the following patent: US10835146 B2 Autonomous Brain Machine Interface.

Stage of Development
Technology Readiness Level (TRL): 3 - Experimental proof of concept.

Licensing Status: This technology is available for licensing. This technology will be of value to any company or institution involved in working with prosthetic devices in patients. This includes: 

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