Neural interface system and method for neural control of multiple devices

Abstract

A system and method for a neural interface system with a unique identification code includes a sensor including a plurality of electrodes to detect multicellular signals, an processing unit to process the signals from the sensor into a suitable control signal for a controllable device such as a computer or prosthetic limb. The unique identification code is embedded in one or more discrete components of the system. Internal and external system checks for compatibility and methods of ensuring safe and effective performance of a system with detachable components are also disclosed.

Description

DESCRIPTION OF THE INVENTION [0001] This application claims the benefit of priority under 35 U.S.C. § 119(e) of U.S. provisional application No. 60 / 524,969, filed Nov. 25, 2003.FIELD OF THE INVENTION [0002] The present invention relates to neural interface systems with unique embedded identifiers, and, more particularly, to systems and methods whereby a neural interface system utilizes the unique embedded electronic signature or identifier to assure compatibility of a multiple component system. DESCRIPTION OF RELATED ART [0003] Neural interface devices are currently under development for numerous applications including restoration of lost function due to traumatic injury or neurological disease. Sensors, such as electrode arrays, implanted in the higher brain regions that control voluntary movement can be activated voluntarily to generate electrical signals that can be processed by a neural interface device to create a thought invoked control signal. Such control signals can be used...

AI Technical Summary

Problems solved by technology

Early attempts to utilize signals directly from neurons to control an external prosthesis encountered a number of technical difficulties.

The ability to identify and obtain stable electrical signals of adequate amplitude was a major issue.

Another problem that has been encountered is caused by the changes that occur to the neural signals that occur over time, resulting in a degradation of system performance.

Neural interface systems that utilize other neural information, such as electrocorticogram (ECOG) signals, local field potentials (LFPs) and electroencephalogram (EEG) signals have similar issues to those associated with individual neuron signals.

Since all of these signals result from the activation of large groups of neurons, the specificity and resolution of the control signal that can be obtained is limited.

Commercialization of these neural interfaces has been extremely limited, with the majority of advances made by universities in a preclinical research setting.

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