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Method and system for training a visual prosthesis

Method and system for training a visual prosthesis description/claims

This application is a continuation of U.S. Application 10/837,163, filed Apr. 30, 2004, which claims priority to U.S. Provisional Patent Application No. 60/467,037, entitled “Blind Patient in the Loop Optimization Algorithm for Electrical Stimulation Patterns for Retinal Implants Electrode Arrays” filed on May 1, 2003, the entirety of which is hereby incorporated by reference.

The invention relates generally to visual prosthesis technology, and in one embodiment, to methods for training retinal implants.

A healthy individual's visual perception process begins with the individual's retina(s) receiving stimulation in the form of light. The individual's nerve cells within the retina communicate a signal corresponding to the stimulation to the optic nerve. The optic nerve subsequently transmits a corresponding signal to the visual cortex through the lateral geniculate nucleus. For a vision impaired patient, visual perception may be induced by providing electrical stimulation at one or more of these locations, depending on the nature patient's given impairment.

Previous studies have shown that controlled electrical stimulation of the retina induces visual perception in blind patients. A healthy retina has over 100 million photoreceptors. Far fewer, however, are required to restore at least low resolution vision in blind individuals. For example, to enable a blind person to attain unaided mobility and large print reading, two important quality of life indicators, tests have shown that such abilities can be provided with orders of magnitude fewer photoreceptors being active. Implants, such as those developed by Second Sight, LLP of Sylmar, Calif., or described, for example, in U.S. Pat. No. 5,935,155 by Humayan et al. and U.S. Pat. No. 5,109,844 to De Juan, Jr. et al., which include arrays of electrodes coupled to nerve cells of a patient's retina, have been shown to be able to restore low resolution visual perception to blind patients.

While current implant technology has been demonstrated to stimulate some amount of visual perception, each implant needs to be trained for its individual patient in order to effectively elicit the appropriate visual perception. Prime candidates for the retinal implants are previously sighted individuals who have had their normal retinal nerve activity damaged by various conditions, for example macular degeneration or retinitis pigmentosa. However, due to the retinal damage in such candidates, predicting in advance the perception induced by a retinal implant in a particular candidate has proven difficult.

The invention addresses the deficiencies in the art by, in one aspect, providing a method for training a visual prosthesis (e.g., a retinal implant, a cortical implant, a lateral geniculate nucleus implant, or an optical nerve implant) to adapt to the patient in which it is implanted. According to this aspect, the method includes providing a non-visual reference stimulus to a patient having a visual prosthesis based on a reference image. The non-visual reference stimulus is intended to provide the patient an expectation of the visual image the visual prosthesis will induce. Non-visual reference stimuli include, without limitation, a pinboard, Braille text, or a verbal communication. The visual prosthesis stimulates the patient's nerve cells with a series of stimulus patterns attempting to induce a visual perception that matches the patient's expected perception derived from the non-visual reference stimulus. The patient provides feedback to indicate which of the series of stimulus patterns induces a perception that most closely resembles the expected perception. The invention employs the patient feedback as a fitness function (also referred to as a cost function or an energy function). Subsequent stimuli provided to the patient through the visual prosthesis are based, at least in part, on the previous feedback of the patient as to which stimulus pattern(s) induce the perception that best matches the expected perception. According to one embodiment, the subsequent stimulus patterns are also based, at least in part, on a fitness function optimization algorithm. In one embodiment, the fitness function optimization algorithm is a simulated annealing algorithm. In another implementation, the fitness function optimization algorithm is a genetic algorithm.

According to one feature, the invention stores the reference image and the series of stimulus patterns presented to the patient, along with the choices indicated by the patient, as a first training set. The invention may generate additional training sets using additional reference images and series of corresponding stimulus patterns. In one embodiment, the fitness function optimization is modified between the stimulation of each series of stimulation patterns. In other embodiments, the fitness function optimization algorithm is static. According to one feature, a neural network analyzes the training set offline to determine a vision solution for the visual prosthesis.

In another aspect, the invention provides a system for training a visual prosthesis such as a retinal implant, a cortical implant, a lateral geniculate nucleus implant, or an optical nerve implant. The system includes a training processor configured to generate and present series of stimulation patterns corresponding to a reference image. As in the above-described method, subsequent stimulation patterns are based, at least in part, on patient input and/or a fitness function optimization algorithm. The system includes a tool for identifying to a patient the reference image that the patient should expect to perceive using a non-visual reference stimulus. The system includes a user input for the patient to identify which of a set of stimulation patterns in each series induces a perception most closely resembling the expected perception derived from the non-visual reference stimulus.

The foregoing discussion will be understood more readily from the following detailed description of the invention with reference to the following drawings.

FIG. 1 is a schematic depiction of a retinal implant according to an illustrative embodiment of the invention.

FIG. 2 is a block diagram of a system for retinal implant training according to an illustrative embodiment of the invention.

FIG. 3 is a flowchart of a method of training a retinal implant according to an illustrative embodiment of the invention.

FIG. 4 is an illustrative non-visual reference identification tool according to an illustrative embodiment of the invention.

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