BRAIN IMAGING SYSTEM AND METHODS FOR DIRECT PROSTHESIS CONTROL
First Claim
1. A method for controlling a prosthesis, the method comprising:
- receiving two or more input signals from at least two distinct brain imagers, wherein the input signals corresponds to brain activity at a portion of the motor cortex;
using a neural network to map the input signals to an output signal, wherein the neural network is trained to map the input signals associated with the portion of the motor cortex to an output signal that corresponds with a muscle group; and
coupling the output signal with the prosthesis, wherein the prosthesis is configured to respond to the output signal.
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Abstract
Methods and systems for controlling a prosthesis using a brain imager that images a localized portion of the brain are provided according to one embodiment of the invention. For example, the brain imager can provide motor cortex activation data using near infrared imaging techniques and EEG techniques among others. EEG and near infrared signals can be correlated with brain activity related to limbic control and may be provided to a neural network, for example, a fuzzy neural network that maps brain activity data to limbic control data. The limbic control data may then be used to control a prosthetic limb. Other embodiments of the invention include fiber optics that provide light to and receive light from the surface of the scalp through hair.
56 Citations
20 Claims
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1. A method for controlling a prosthesis, the method comprising:
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receiving two or more input signals from at least two distinct brain imagers, wherein the input signals corresponds to brain activity at a portion of the motor cortex; using a neural network to map the input signals to an output signal, wherein the neural network is trained to map the input signals associated with the portion of the motor cortex to an output signal that corresponds with a muscle group; and coupling the output signal with the prosthesis, wherein the prosthesis is configured to respond to the output signal. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A prosthesis control system, comprising:
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one or more light sources configured to irradiate light into a first portion of the brain; one or more photodiodes configured to detect a portion of the light transmitted into the first portion of the brain, wherein the detected light travels at least from the one or more light sources through a plurality of sub-portions of the brain and is detected at the plurality of photodiodes; one or more electrodes configured to detect action potentials from neurons within a second portion of the brain; and a controller coupled with the one or more photodiodes, the one or more electrodes, and the prosthesis, wherein the controller is configured to receive a plurality of inputs from the plurality of photodiodes and the one or more electrodes, wherein the controller is configured to; determine the brain activity at a plurality of sub-portions of the first portion of the brain from the input from the plurality of photodiodes; determine the brain activity at a plurality of sub-portions of the second portion of the brain from the inputs from the electrodes; and determine a plurality of limbic control signals from the brain activity within the first portion and the second portion of the brain. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
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17. A method for training a prosthesis system, wherein the prosthesis system comprises a neural network, a brain imaging system, and a prosthesis, the training utilizing a muscle activity detector, the method comprising:
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receiving brain activity data from the brain imaging system; receiving muscular response data from the muscle activity detector, wherein the muscular response data corresponds with the brain activity; and training the neural network to produce the muscular response data from the brain activity data. - View Dependent Claims (18, 19, 20)
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Specification