Patent Publication Number: US-2015081225-A1

Title: Method and System for the Visualization of Brain Activity

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. provisional patent application No. 61/838334 filed on 23 Jun. 2013, incorporated herein by reference in it&#39;s entirety. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to devices and methods that can transmit and view visual images seen by an animal, and more particularly, though not exclusively, a device that can receive data related to brain activity to reconstruct the image seen or visualized by a human. 
     BACKGROUND OF THE INVENTION 
     Current work in brain research has attempted to map storage location in the brain of calibrated images. A useful method in both therapy and non verbal communication is needed. Several methods exist to detect the small ionic current flows akin to that which occurs in biological systems. A non-limiting example is a SQUID, fMRI, Hall Effect Sensors, and Electroencephalography. Such sensors can be combined with additional sensors, for example thermocouples. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Exemplary embodiments of present invention will become more fully understood from the detailed description and the accompanying drawings, wherein: 
         FIGS. 1A-1C  illustrate training and calibration images; 
         FIG. 2  illustrates the viewed images being transmitted to a remote viewer; 
         FIG. 3  illustrates the transmission of the viewed image data to the receiver on the remote system and matching it to stored image calibration data; 
         FIG. 4  illustrates the transmission of the viewed image data to the receiver on the remote system and matching it to stored command/control calibration data; 
         FIG. 5  illustrates the transmission of the viewed image data to the receiver on the remote system and matching it to stored communication calibration data; and 
         FIG. 6  illustrates a non-limiting example of a sensor array. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     The following description of exemplary embodiment(s) is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. 
     If one can then obtain brain activity from a subject, the data can be correlated with shapes, color, sound, feel, taste and other sensory data associated with calibrated objects, and visual scenes to determine what the subject is seeing or visualizing and communicate such data to a remote device which can then be displayed for a user to view what the subject sees. 
     The attached figures illustrate various methods according to embodiments. Basically a subjects brain patterns have been saved as data. The data is linked to pattern associated with a visual image, and several of the senses. For example if the brain patterns show activity in the visual region and access to several memory locations, then the activity level (e.g., oxygen level) of the several memory locations can be matched to stored patterns and a visual image simulated that should be similar to what is seen by the subject. Note that the several memory locations can be different locations associated with various senses. For example, if the highest intensities is accounted with yellow, sour smell, sour taste, and size about 3-4 inches, and the visual signals are associated with yellow and oval, then the image displayed can be oval, yellow, . . . then a separate label giving probable objects (e.g., lemons). 
     Types of sensors that can be used are MRI, PET, E and B field sensors, infrared sensors. 
       FIGS. 1A-1C  illustrate training and calibration images.  FIG. 1A  illustrates a subject  120  viewing an image  110 , where a head sensor  130  records the brain currents associated with the image. The data is transmitted, for example via RF transmitter  150 , to a remote system. The data is stored as image calibrated data  130  in a data sensor set  140 , associated with the particular image  110 . The multiple sensor values (e.g., currents, temperatures, magnetic and electric field), which can occur in separate skull caps are stored.  FIG. 1B  illustrates a second image composed of image  150  combined with image  110 . The data set  130  associated with image  110  is stored as well as the data set  170  associated with image  150 . The entire sensor set  160  is the calibrated data associated with the combined images. Note that determination of values in the array can be in accordance to a threshold level. For example if the current is greater than 1 nanoamp a value of 1 can be assigned to the data position in the data matrix associate with the sensor taking the measurement. For example suppose the i=3, j=5 sensor measures 3 nanoamp and the threshold is 1 nanoamp. Then one possible data value from sensor i=3, j=5 would be 1, others values can be assigned also for example the actual value can be used.  FIG. 1C  illustrates the third image  180  with associated data  195 . 
       FIG. 2  illustrates the viewed images being transmitted to a remote viewer. A viewer  120  wearing a monitoring cap  130  transmits via a transmitter  150  data to a remote system (e.g., remote viewer  200 ).  FIG. 3  illustrates that transmission  220  of the data associated with viewing is sent to a system receiver  310 , which sends the data to a processor that compares the data to stored image calibration data. The processor then selects the most likely net image, for example by using least square comparison of all data sensor values  340 , sending the net image  330  onto the display  320 , for example onto a heads up display unit  210 . 
       FIG. 4  illustrates that transmission  420  of the data associated with a mental command is sent to a system receiver  310 , which sends the data to a processor that compares the data to stored command calibration data. The processor then selects the most likely net command to send to a system  440  (e.g., move artificial arm, rotate  450  mechanical lever from an initial position  460  to a new position  470 ), for example by using correlation comparison of all data sensor values  430  (e.g., choosing the command associated with an average correlation&gt;0.9), sending the net command  425  to the system&#39;s  440  processor, where the system  440  enacts the command. 
       FIG. 5  illustrates that transmission  520  of the data associated with a mental command/communication request is sent to a system receiver  310 , which sends the data to a processor that compares the data to stored command/communication request calibration data. The processor then selects the most likely, comparing net command/communication request to send to a communication device  550  (e.g., smart phone, TV,) for example by setting the calibrated values to 1 (values associated with 130 to 1.0) and the background to − 1  (values not associated with 130 to −1), then summing the transmitted data array with calibrated data arrays until the largest net sum is achieved upon which the communication request associated with that value is chosen, comparison of all data sensor values  430  with calibrated data, sending the net command/communication request  525  to the system&#39;s  550  processor, where the system  550  enacts the command/communication request (e.g., call friend). 
       FIG. 6  illustrates a non-limiting example of a sensor array. The noon-limiting example includes a mu-metal cap  610  (flexible or non-flexible), covering at least on sensor array cap(s)  620  (hall effect sensor array cap, individually or coupled with SQUID (using room temp near superconductor) sensor array cap), with a skin contact layer  630 . The sensors on the sensor array can be indexed by numerical location on a grid system (i,j). For example sensor  650 A at numerical location i=4, j=3, and sensor  650  A at numerical location i=1, j=1 can be mapped to an associated i,j data array position in calibrated data set. 
     Note that the calibrated data set can be set up as a data matrix of multiple dimensions, with each sensor type associated with a particular sensor on a particular sensor cap. For example suppose  620  is actually composed of two overlaying data caps where the first is a hall effect sensor cap, and the second is Electroencephalography sensor cap. Thus data set matrix M(1, i,j) can be associated with calibrated values of the hall sensor cap, and M(2,i,j) can be associated with Electroencephalography sensor cap data. 
     While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all modifications, equivalent structures and functions of the relevant exemplary embodiments. For example, terms such as correlation and least squares is used, and their common mathematical meaning between two matrix datasets are assumed to be incorporated by reference. 
     Thus, the description of the invention is merely exemplary in nature and, thus, variations that do not depart from the gist of the invention are intended to be within the scope of the exemplary embodiments of the present invention. Such variations are not to be regarded as a departure from the spirit and scope of the present invention.