Brain imaging system and methods for direct prosthesis control

• US 8,457,705 B2
• Filed: 10/24/2007
• Issued: 06/04/2013
• Est. Priority Date: 10/25/2006
• Status: Active Grant

• Current Assignee: University of Denver
• Original Assignee: University of Denver
• Inventors: Shoureshi, Rahmat A., Aasted, Christopher M.
• Primary Examiner(s): Laballe, Clayton E
• Assistant Examiner(s): BUTLER, KEVIN C
• Application Number:
  US12/447,428
• Publication Number:
  US 20100191079A1
• Time in Patent Office: 2,050 Days
• Field of Search: 600/323, 600/476
• US Class Current: 600/323
• CPC Class Codes: A61B 2562/0233 Special features of optical sensors or probes classified in A61B5/00
• CPC Class Codes: A61B 2562/046 in a matrix array
• CPC Class Codes: A61B 5/0042 for the brain
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/14553 specially adapted for cerebral tissue
• CPC Class Codes: A61B 5/4064 Evaluating the brain A61B5/031, A61B5/369, A61B5/14553 take precedence
• CPC Class Codes: A61B 5/4519 Muscles A61B5/389, A61B5/224 take precedence
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: A61B 5/6814 Head
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61F 2/72 Bioelectric control, e.g. myoelectric
• CPC Class Codes: A61F 2002/7675 for measuring oxygen content
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

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. The brain imager provides motor cortex activation data by illuminating the motor cortex with near infrared light (NIR) and detecting the spectral changes of the NIR light as passes through the brain. These spectral changes 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.

Claims

1. A method for controlling a prosthesis, the method comprising:

receiving a brain-activity signal from a brain imager, wherein the brain-activity signal corresponds to brain activity within a portion of the motor cortex;

mapping the brain-activity signal to a limbic-control signal using a neural network that is trained to map the brain-activity signal to the limbic-control signal; and

coupling the limbic-control signal with the prosthesis, wherein the prosthesis is configured to respond to the limbic-control signal.

2. The method according to claim 1, further comprising:

illuminating light on a portion of the brain using a light source;

receiving a light signal at the scalp using a photodiode, wherein the detected light travels from the light source through a sub-portion of the brain and is detected at the photodiode; and

outputting a brain-activity signal.

3. The method according to claim 2, wherein the light source illuminates a portion of the brain with near infrared light.

4. The method according to claim 1, wherein the neural network comprises a fuzzy neural network.

5. The method according to claim 2, wherein the light source comprises at least one or more LEDs and one or more lasers.

6. The method according to claim 2, wherein the illuminating comprises channeling light from the light source to the scalp.

7. A prosthesis control system, the prosthesis control system comprising:

an optical brain imager configured to;

irradiate a first portion of the brain with one or more light sources;

detect, using one or more photodiodes, 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;

determine the relative concentration of oxy-hemoglobin within the first portion of the brain from the plurality of photodiodes inputs;

determine the relative concentration of hemoglobin within the first portion of the brain from the plurality of photodiodes inputs; and

determine the brain activity at a plurality of sub-portions of the first portion of the brain from the relative concentrations of oxy-hemoglobin and hemoglobin;

a neural network that is trained to map brain activity to limbic-control signals; and

a controller configured to receive the limbic control signals from the neural network and configured to operate the prosthesis in response to the limbic control signals.

8. The prosthesis control system according to claim 7, wherein the one or more light sources comprise at least one or more LEDs and one or more lasers.

9. The prosthesis control system according to claim 7, further comprising a plurality of fiber optics, wherein the fiber optics are configured to channel the light from the light sources to the surface of the scalp.

10. The prosthesis control system according to claim 7 further comprising a plurality of fiber optics, wherein the fiber optics are configured to channel light from the surface of the scalp to the photodiodes.

11. The prosthesis control system according to claim 7, wherein the plurality of light sources generates near infrared light and the photodiodes receive near infrared light.

12. The prosthesis control system according to claim 7, wherein the plurality of light sources generates light between about 700 nm and about 775 nm.

13. The prosthesis control system according to claim 7, wherein the plurality of light sources generates light between about 800 nm and about 900 nm.

14. The prosthesis control system according to claim 7 further comprising a headset coupled with at least one of an end of the fiber optics and the photodiodes.

15. The prosthesis control system according to claim 14, wherein the headset is configured to adjust the positioning of the fiber optics and the photodiodes over different portions of the brain.

16. The prosthesis control system according to claim 7, wherein the neural network is a fuzzy neural network.

17. The prosthesis control system according to claim 7, further comprising a muscle activity sensor, wherein the controller utilizes the limbic-control signals as feedforward signals and muscle activation as recorded by the muscle activity sensor as feedback signals.

18. A fiber optic for transmitting light into the brain through the scalp and past hair, the fiber optic comprising:

an elongated optical fiber body comprising a distal end, a proximal end and elongated fiber body, wherein the proximal end is configured to receive light from a light source and the elongated fiber body is configured to channel light received from the light source at the proximal end to the distal end; and

a bulb coupled with the distal end of the optical fiber, wherein the bulb is configured to transmit light from the elongated fiber optic body into the brain through the scalp and past hair.

19. The fiber optic according to claim 18, wherein the bulb comprises a substantially spherical shape.

20. The fiber optic according to claim 18, wherein the bulb and the optical fiber comprise a continuous first material.

21. A method for training a prosthesis system, wherein the prosthesis system comprises a man-made neural network, a brain imaging system, and a prosthesis, the training utilizing a muscle activity detector, the method comprising:

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 man-made neural network to produce the muscular response data from the brain activity data.

22. The method according to claim 21, wherein the neural network is a fuzzy neural network.

23. The method according to claim 21, wherein the muscle activity detector comprises an electromyograph.