Calibration systems and methods for neural interface devices

Abstract

A system and method for a neural interface system with integral calibration elements may include a sensor including a plurality of electrodes to detect multicellular signals, an interface to process the signals from the sensor into a suitable control signal for a controllable device, such as a computer or prosthetic limb, and an integrated calibration routine to efficiently create calibration output parameters used to generate the control signal. A graphical user interface may be used to make various portions of the calibration and signal processing configuration more efficient and effective.

Description

[0001] This application claims the benefit of priority under 35 U.S.C. § 119(e) of U.S. provisional application No. 60 / 519,047, filed Nov. 9, 2003.DESCRIPTION OF THE INVENTION [0002] 1. Field of the Invention [0003] The present invention relates to systems and methods for calibrating neural interface devices, and, more particularly, to calibration systems and methods for neural interface devices employing mufti-electrode sensors for detecting neuronal activity. [0004] 2. Description of Related Art [0005] Neural interface devices are currently under development for numerous applications involving 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 to control numerous dev...

AI Technical Summary

Benefits of technology

[0011] In another aspect, the system includes one or more safety checks regarding successful completion of the calibration routine. For example, the operator is qualified by performing a mock calibration utilizing data included in the calibration apparatus, either synthetic data or previously recorded human data. Alternatively or additionally, operator secured access is provided preventing inadvertent or malicious changes in calibration being performed by improper or unqualified individuals.

[0012] In still another aspect, multiple calibration routines are embedded in the system. The multiple routines can be utilized for comparative purposes, routines can be specific to a particular controlled device and can differentiate an initial calibration from subsequent calibration. In a preferred embodiment, multiple calibration routines are performed, and a check is performed to select the best performance. In one embodiment, specific calibration routines are linked to specific controlled devices. In still another embodiment, the neural interface system includes one or more initial calibration routines, and one or more subsequent calibration routines. The subsequent calibration routines have a reduced number of steps resulting in reduced calibration duration, and may utilize data captured from previous calibrations including date from the initial calibration.

[0013] In some aspects, the calibration routine includes preset limits for either input variables or output variables of the calibration routine. In one embodiment, these limits are adjustable by a subset of potential operators, such as only by the clinician. This tiered approach offers the potential of both safe and efficient calibration of the system, allowing less qualified operators to make fine adjustments only.

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.

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