Office Action 1: Non-Final Rejection (2024-09-23)
Rejection types: 101 - Patent-Eligible Subject Matter

CLAIMS:
CLAIMS

1\. A computer-implemented method for voxelizing a 3D structural medical image of a human's brain, each voxel being suitable for running a simulator of an electrical activity of a 3D volume of the human's brain being enclosed by the voxel, the method comprising:
obtaining a 3D structural medical image of the human's brain, the 3D structural medical image comprising a reference frame;
generating a voxelized 3D structural medical image;
obtaining parameters of at least one EEG electrode sensor and, for each EEG electrode sensor:
a localization in a reference frame of the voxelized 3D structural medical image, and
a sensor detection distance;
obtaining a 3D grid of voxels, each voxel of the 3D grid joining a same finite number of voxels of the voxelized 3D structural medical image, the number of voxels of the 3D grid being smaller than the number of voxels of the voxelized 3D structural medical image; and
for each voxel of the 3D grid, iteratively subdividing the voxel while the
distance between the voxel and localization of any electrode sensor is smaller than or equal to the sensor detection distance and while a size of the voxel is greater than a predetermined length, each subdivided voxel joining a finite number of voxels of the voxelized 3D structural medical image.

2\. The computer-implemented method of claim 1, wherein the iteratively subdividing further comprises:
computing for each voxel of the 3D grid a bounding sphere;
computing for each EEG electrode sensor, a sphere being centered at the EEG electrode sensor and a radius being equal to the sensor detection distance;
determining, for each computed bounding sphere, when the computed bounding sphere is intersecting with the computed spheres; and for each voxel, subdividing the voxel when the respective computed bounding sphere is intersecting with one or more computed sphere while a size of the voxel is greaterthan the predetermined length, each subdivided voxel joining a finite number of voxels of the voxelized 3D structural medical image.

3\. The computer-implemented method of claim 1, wherein each voxel of the 3D grid having 2" voxels of the voxelized 3D structural medical image wherein n E N.

4\. The computer-implemented method according to claim 1, further comprising removing the voxels of the voxelized 3D structural medical image that are not enclosing a brain grey matter in the voxelized 3D structural medical image.

5\. The computer-implemented method of claim 4, further comprising, for each voxel of the 3D grid:
determining, among the voxels of the voxelized 3D structural medical image encompassed in the voxel of the 3D grid, when one or more voxels of the voxelized 3D structural medical image having no neighbor voxel of the voxelized 3D structural medical image, thereby each voxel forming a new volume added to the 3D grid;
determining, among the voxels of the voxelized 3D structural medical image
encompassed in the voxel of the 3D grid, whether at least two voxels of the voxelized 3D structural medical image are neighbors, thereby the at least two neighbor voxels forming a new volume added to the 3D grid; and
removing the voxel of the 3D grid.

6\. The computer-implemented method of claim 5, further comprising, for each volume of the 3D grid having less than a first predetermined minimum number of voxels of the voxelized 3D structural medical image, merging the volume with a neighbor volume of the 3D grid.
30 7. The computer-implemented method of claim 5, further comprising, for each volume of the 3D grid, discarding the volume having less than a second predetermined minimum number of voxels of the voxelized 3D structural medical image.

8\. The computer-implemented method of claim 7, wherein the first and second predetermined minimum number of voxels rounding down a value representing 10% of a same finite number of voxels of the voxelized 3D structural medical image.

9\. The computer-implemented method according to claim 1, wherein each generated voxel of the voxelized 3D structural medical image being cubic.

10\. The computer-implemented method according to claim 1, wherein all generated voxels of the voxelized 3D structural medical image having the same volume.

11\. The computer-implemented method according to claim 1, wherein each dimension of the voxelized 3D structural medical image has a same and even number of generated voxels.

12\. The computer-implemented method according to claim 1, wherein the sensor detection distance is between 20 to 30 millimeters.

13\. A non-transitory computer readable storage medium having recorded thereon a computer program having instructions for performing a method for voxelizing a 3D structural medical image of a human's brain, each voxel being suitable for running a simulator of an electrical activity of a 3D volume of the human's brain being enclosed by the voxel, the method comprising:
obtaining a 3D structural medical image of the human's brain, the 3D structural medical image comprising a reference frame;
generating a voxelized 3D structural medical image;
obtaining parameters of at least one EEG electrode sensor and, for each EEG electrode sensor: a localization in a reference frame of the voxelized 3D structural medical image, and
a sensor detection distance;
obtaining a 3D grid of voxels, each voxel of the 3D grid joining a same finite
number of voxels of the voxelized 3D structural medical image, the number of voxels of the 3D grid being smaller than the number of voxels of the voxelized 3D structural medical image; and
for each voxel of the 3D grid, iteratively subdividing the voxel while the distance between the voxel and localization of any electrode sensor is smaller than
or equal to the sensor detection distance and while a size of the voxel is greater than a predetermined length, each subdivided voxel joining a finite number of voxel(s) of the voxelized 3D structural medical image.

14\. The non-transitory computer readable storage medium of claim 13, wherein the iteratively subdividing comprises:
computing for each voxel of the 3D grid a bounding sphere;
computing for each EEG electrode sensor, a sphere being centered at the EEG electrode sensor and a radius being equal to the sensor detection distance;
determining, for each computed bounding sphere, when the computed
bounding sphere is intersecting with the computed spheres; and
for each voxel, subdividing the voxel when the computed bounding sphere is intersecting with one or more computed sphere while a size of the voxel is greater than the predetermined length, each subdivided voxel joining a finite number of voxels of the voxelized 3D structural medical image.

15\. The non-transitory computer readable storage medium of claim 13, wherein each voxel of the 3D grid having 2" voxels of the voxelized 3D structural medical image wherein n E N.
30 16. The non-transitory computer readable storage medium of claim 13, wherein the method further comprises removing the voxels of the voxelized 3D structural medical image that are not enclosing a brain grey matter in the voxelized 3D structural medical image.

17\. An apparatus comprising:
a processor; and
a database having a non-transitory computer readable storage medium having recorded thereon a computer program having instructions for voxelizing a 3D structural medical image of a human's brain, each voxel being suitable for running a simulator of an electrical activity of a 3D volume of the human's brain being enclosed
by the voxel that when executed by the processor causes the processor to be configured to:
obtain a 3D structural medical image of the human's brain, the 3D structural medical image comprising a reference frame;
generate a voxelized 3D structural medical image;
obtain parameters of at least one EEG electrode sensor and, for each EEG electrode sensor:
a localization in a reference frame of the voxelized 3D structural medical image, and
a sensor detection distance;
obtain a 3D grid of voxels, each voxel of the 3D grid joining a same finite number of voxels of the voxelized 3D structural medical image, the number of voxels of the 3D grid being smaller than the number of voxels of the voxelized 3D structural medical image; and
for each voxel of the 3D grid, iteratively subdivide the voxel while the distance
between the voxel and localization of any electrode sensor is smaller than or equal to the sensor detection distance and while a size of the voxel is greater than a predetermined length, each subdivided voxel joining a finite number of voxel(s) of the voxelized 3D structural medical image.
30 18. The apparatus of claim 17, wherein the processor is further configured to iteratively subdivide by being configured to: compute for each voxel of the 3D grid a bounding sphere, compute for each EEG electrode sensor, a sphere being centered at the EEG electrode sensor and a radius being equal to the sensor detection distance, determine, for each computed bounding sphere, whether the computed bounding sphere is intersecting with the computed spheres, and
for each voxel, subdivide the voxel when the respective computed bounding sphere is intersecting with one or more computed sphere while a size of the voxel is greaterthan the predetermined length, each subdivided voxel joining a finite number of voxels of the voxelized 3D structural medical image.

19\. The apparatus of claim 17, wherein each voxel of the 3D grid having 2" voxels of the voxelized 3D structural medical image wherein n E N.

20\. The apparatus of claim 17, wherein the processor is further configured to remove the voxels of the voxelized 3D structural medical image that are not enclosing a brain grey matter in the voxelized 3D structural medical image. 5 10 15 20 25 30

OFFICE ACTION:
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
In regarding claims 1, 13 and 17:
Step 1:
Claims 1, 13 and 17 are directed towards a process, machine, manufacture or composition of matter which is/are statutory subject matter.
Step 2A:
Prong 1:
Claims 1, 13 and 17 are directed an idea for voxelizing a 3D structural medical image of a human's brain, which is an abstract idea.
Consideration of the claimed elements:
The claims in the instant application include:
obtaining a 3D structural medical image of the human's brain, the 3D structural medical image comprising a reference frame;
generating a voxelized 3D structural medical image;
obtaining parameters of at least one EEG electrode sensor and, for each EEG electrode sensor:
a localization in a reference frame of the voxelized 3D structural medical image, and
a sensor detection distance;
obtaining a 3D grid of voxels, each voxel of the 3D grid joining a same finite number of voxels of the voxelized 3D structural medical image, the number of voxels of the 3D grid being smaller than the number of voxels of the voxelized 3D structural medical image; and
for each voxel of the 3D grid, iteratively subdividing the voxel while the distance between the voxel and localization of any electrode sensor is smaller than or equal to the sensor detection distance and while a size of the voxel is greater than a predetermined length, each subdivided voxel joining a finite number of voxels of the voxelized 3D structural medical image.
Regarding “generating a voxelized 3D structural medical image”, it can be interpreted as a person observes 3D structural medical image of the human's brain, and use pen and paper to voxelize (make grid) for the 3D structural medical image. It can be categorized as a mental process.
Regarding “obtaining a 3D grid of voxels, each voxel of the 3D grid joining a same finite number of voxels of the voxelized 3D structural medical image, the number of voxels of the 3D grid being smaller than the number of voxels of the voxelized 3D structural medical image”, it can be interpreted as the person in the voxelize (make grid) process, make the grid according to certain joining rules. It can be categorized as a mental process.
Regarding “for each voxel of the 3D grid, iteratively subdividing the voxel while the distance between the voxel and localization of any electrode sensor is smaller than or equal to the sensor detection distance and while a size of the voxel is greater than a predetermined length, each subdivided voxel joining a finite number of voxels of the voxelized 3D structural medical image”, it can be interpreted as if a voxel (cubic) satisfies a condition (the distance between the voxel and localization of any electrode sensor is smaller than or equal to the sensor detection distance and while a size of the voxel is greater than a predetermined length), iteratively subdividing the voxel. It can be categorized as a mental process.
Prong 2:
The claims include additional elements of:
obtaining a 3D structural medical image of the human's brain, the 3D structural medical image comprising a reference frame
obtaining parameters of at least one EEG electrode sensor and, for each EEG electrode sensor:
a localization in a reference frame of the voxelized 3D structural medical image, and
a sensor detection distance;
a non-transitory computer readable storage medium (claims 13 -16);
and a processor, and a database (claims 17 – 20).
A non-transitory computer readable storage medium, a processor, and a database are general computer elements.
Regarding two “obtaining” steps, it is considered as data gathering.
The above additional elements are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
Moreover, the claim limitations that are not indicative of integration into a practical application.
Thus, the recited generic additional hardware (e.g., a processor, a database, a non-transitory computer readable storage medium and data gathering) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a generic memory and processor are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Accordingly, the claims are directed to an idea of itself, and therefore not patent eligible.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception such as improvements to another technology or technical field, or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Moreover, the claim language that may be separate from the abstract idea (i.e., additional elements) include computer processors, computer-readable storage media.
The additional hardware (e.g., a processor, a database, a non-transitory computer readable storage medium and data gathering) perform only basic function, which would be common to every additional hardware (e.g., a processor, a database, a non-transitory computer readable storage medium and data gathering).
Thus, the recited generic additional hardware (e.g., a processor, a database, a non-transitory computer readable storage medium and data gathering) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a generic memory are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Consequently, the identified additional elements taken into consideration individually or in combination fails to amount of significantly more than the abstract idea above.
Regarding claims 2 – 12, 14 - 16 and 18 – 20, the rejection is based on the same rationale described for claims 1, 13 and 17, because the claims include/inherit the same/similar type of problematic limitation(s) as claims 1, 13 and 17, wherein limitations regarding “computing … “, determining ... , “subdividing …”, “having  …”,  removing... ,  merging ... , “discarding …”, “rounding …”, “being …”, “has …” and/or  is ... , is/are of sufficient breadth that it would be substantially directed to or reasonably interpreted as a part of the “mental processes” as the abstract idea (similar to claims 1, 13 and 17 as stated above). It is noted that further additional limitation is merely generic/conventional computer component/steps to implement the abstract idea, which is, individually or in combination, not sufficient to amount to significantly more than the judicial exception. Therefore, the claimed invention as a whole is directed to an ineligible subject matter.
Claim Rejections - 35 USC § 102/103
No reference is found.
The current invention is directed to a system/method for voxelizing a 3D structural medical image of a human's brain wherein generating a voxelized 3D structural medical image; obtaining parameters of at least one EEG electrode sensor and, for each EEG electrode sensor: a localization in a reference frame of the voxelized 3D structural medical image, and a sensor detection distance; obtaining a 3D grid of voxels, each voxel of the 3D grid joining a same finite number of voxels of the voxelized 3D structural medical image, the number of voxels of the 3D grid being smaller than the number of voxels of the voxelized 3D structural medical image; and for each voxel of the 3D grid, iteratively subdividing the voxel while the distance between the voxel and localization of any electrode sensor is smaller than or equal to the sensor detection distance and while a size of the voxel is greater than a predetermined length, each subdivided voxel joining a finite number of voxels of the voxelized 3D structural medical image.
The closest reference Owen et al. (“A Gaussian Process Model of Human Electrocorticographic Data, IDS) discloses a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. the approach makes the simplifying assumptions that different people’s brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. the approach provides a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings (abstract).
Michel et al. (“EEG Source Imaging: A Practical Review of the Analysis Steps”) discloses a practical review of the analysis steps for EEG source imaging (abstract).
However, neither Owen nor Michel, alone or in combination, teach all the limitations recited in the independent claims of the instant application.
Conclusion


NEXT OFFICE ACTION: Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
Claims 1 – 20 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
The Applicant’s argument (pages 10 – 12) is persuasive. The claims limitations overcome the 101 issues. The claims are in condition for allowance.
Conclusion

CITED REFERENCE SUMMARIES: Ref CN104545897: Claims:
An EEG recording reference conversion device, comprising an EEG acquisition module (1), a head image information processing module (2), an EEG model input module (3), an EEG reference conversion module (4) and an EEG The signal display module (5) is characterized in that the head image information processing module (2) is used to divide the image of the brain source functional area into a grid, and number the grid of the solution space to establish the head to be tested. Model; the EEG acquisition module (1) includes left and right ear reference electrodes and the acquisition electrodes for collecting head surface potential, the EEG acquisition module (1), head image information processing module (2) and brain The output ends of the power supply model input module (3) are all connected to the input ends of the EEG signal conversion module (4), and the collected EEG signals and the spatial position coordinates of all electrodes, the head model data to be measured and the brain The power model data is input to the EEG reference conversion module (4); the EEG signal display module (5) is connected with the output end of the EEG reference conversion module (4), for displaying the converted Reference EEG recordings; The EEG reference conversion module (4) includes a central processing unit (6), a power supply module (7) and a memory (8), and the output end of the power supply module (7) is connected to the central processing unit (6), It is used to provide power to the central processing unit (6), the output end of the memory (8) is connected to the central processing unit (6), and is used to provide the central processing unit (6) with left, right, The EEG signal of the right ear is converted into the conversion program of the EEG signal with infinity as the unified reference, and the central processing unit (6) outputs the converted EEG signal to the signal display module (5 ); The EEG reference conversion module (4) adopts the following method to convert EEG signals: A: Calculate the transfer matrix G determined by the head model data to be tested, the spatial position coordinate data of each electrode of the EEG acquisition system, and the EEG model data. The specific method is as follows: place the unit on the solution space position numbered 1 Source, calculate the potential distribution generated by the unit source in the solution space at each acquisition electrode position of the EEG detection system, the obtained potential distribution constitutes the first column in the transfer matrix G, traverse all the solution spaces in turn according to the above method and place the unit source After that, the complete transfer matrix G can be obtained; B: Determine the transfer matrix G lr of the head surface potential V lr with the left and right ear electrodes as reference, the specific formula is as follows: Among them, G l and G r are the matrices formed by the rows corresponding to the head surface potentials collected by the collection electrodes with the left and right ear electrodes as references in the transfer matrix G, are the column vectors with the size of p l Ã1 and p r Ã1 respectively, each element of the column vector is unit 1, p l and p r are respectively the number of acquisition electrodes with reference to the left and right ear electrodes, respectively the transposition of C: Calculating the generalized inverse G lr + of the transfer matrix G lr , thus obtaining the standardized matrix R lr = GG lr + of the head surface potential V lr with the left and right ear electrodes as references respectively; D: Using the obtained standardized matrix R lr and the head surface potential V lr recorded with the left and right ear electrodes as references, the head surface potential V'=R lr V lr with infinity as a unified reference is calculated.
An EEG recording reference conversion device, comprising an EEG acquisition module (1), a head image information processing module (2), an EEG model input module (3), an EEG reference conversion module (4) and an EEG The signal display module (5) is characterized in that the head image information processing module (2) is used to divide the image of the brain source functional area into a grid, and number the grid of the solution space to establish the head to be tested. Model; the EEG acquisition module (1) includes left and right ear reference electrodes and the acquisition electrodes for collecting head surface potential, the EEG acquisition module (1), head image information processing module (2) and brain The output ends of the power supply model input module (3) are all connected to the input ends of the EEG signal conversion module (4), and the collected EEG signals and the spatial position coordinates of all electrodes, the head model data to be measured and the brain The power model data is input to the EEG reference conversion module (4); the EEG signal display module (5) is connected with the output end of the EEG reference conversion module (4), for displaying the converted Reference EEG recordings;
The EEG reference conversion module (4) includes a central processing unit (6), a power supply module (7) and a memory (8), and the output end of the power supply module (7) is connected to the central processing unit (6), It is used to provide power to the central processing unit (6), the output end of the memory (8) is connected to the central processing unit (6), and is used to provide the central processing unit (6) with left, right, The EEG signal of the right ear is converted into the conversion program of the EEG signal with infinity as the unified reference, and the central processing unit (6) outputs the converted EEG signal to the signal display module (5 );
The EEG reference conversion module (4) adopts the following method to convert EEG signals:
A: Calculate the transfer matrix G determined by the head model data to be tested, the spatial position coordinate data of each electrode of the EEG acquisition system, and the EEG model data. The specific method is as follows: place the unit on the solution space position numbered 1 Source, calculate the potential distribution generated by the unit source in the solution space at each acquisition electrode position of the EEG detection system, the obtained potential distribution constitutes the first column in the transfer matrix G, traverse all the solution spaces in turn according to the above method and place the unit source After that, the complete transfer matrix G can be obtained;
B: Determine the transfer matrix G lr of the head surface potential V lr with the left and right ear electrodes as reference, the specific formula is as follows:
Among them, G l and G r are the matrices formed by the rows corresponding to the head surface potentials collected by the collection electrodes with the left and right ear electrodes as references in the transfer matrix G, are the column vectors with the size of p l Ã1 and p r Ã1 respectively, each element of the column vector is unit 1, p l and p r are respectively the number of acquisition electrodes with reference to the left and right ear electrodes, respectively the transposition of
C: Calculating the generalized inverse G lr + of the transfer matrix G lr , thus obtaining the standardized matrix R lr = GG lr + of the head surface potential V lr with the left and right ear electrodes as references respectively;
D: Using the obtained standardized matrix R lr and the head surface potential V lr recorded with the left and right ear electrodes as references, the head surface potential V'=R lr V lr with infinity as a unified reference is calculated.
EEG recording reference conversion device according to claim 1, characterized in that, when said head image information processing module (2) is carrying out grid division to head image, its grid accuracy is 10mm/grid .
The EEG recording reference conversion device according to claim 1, characterized in that, the EEG acquisition module (1) is a 16, 21, 32, 64, 128 or 256-lead EEG signal recording system.
EEG recording reference conversion device according to claim 1, is characterized in that, described EEG model input module (3) can provide point charge model, dipole to described EEG signal conversion module (4) One of three types of model data: model or multipole source model.
A method for EEG recording reference conversion, specifically comprising the following steps: Step 1: Establish a head model to be tested; scan the head of the subject to be tested to obtain image information of the anatomical structure of the head, extract the image information of the source functional area of the brain, and divide the image information of the source functional area into grids Carry out subdivision, determine the dimension of the solution space and the spatial position serial number of each solution space grid, thus establish the real head model; Step 2: Acquire the head surface potential V 1N with more than two electrodes as the reference through the EEG detection system, where N is the number of reference electrodes, and record the spatial position coordinates of the reference electrodes and each acquisition electrode used to collect the potential, and select the brain power model; Step 3: using the boundary element method to calculate the transfer matrix G determined by the real head model, the spatial position coordinates of each electrode and the equivalent distribution source model of EEG; Step 4: Determine the transfer matrix G 1N of the head surface potential V 1N with more than two electrodes as references; Step 5: Calculating the generalized inverse G 1N + of the transfer matrix G 1N , thereby obtaining the standardized matrix R 1N = GG 1N + of the head surface potential with more than two electrodes as reference; Step 6: Using the obtained standardized matrix R 1N and the head surface potential V 1N recorded with two or more electrodes as references, calculate the head surface potential V'=R 1N V 1N with infinity as a unified reference.
A method for EEG recording reference conversion, specifically comprising the following steps:
Step 1: Establish a head model to be tested; scan the head of the subject to be tested to obtain image information of the anatomical structure of the head, extract the image information of the source functional area of the brain, and divide the image information of the source functional area into grids Carry out subdivision, determine the dimension of the solution space and the spatial position serial number of each solution space grid, thus establish the real head model;
Step 2: Acquire the head surface potential V 1N with more than two electrodes as the reference through the EEG detection system, where N is the number of reference electrodes, and record the spatial position coordinates of the reference electrodes and each acquisition electrode used to collect the potential, and select the brain power model;
Step 3: using the boundary element method to calculate the transfer matrix G determined by the real head model, the spatial position coordinates of each electrode and the equivalent distribution source model of EEG;
Step 4: Determine the transfer matrix G 1N of the head surface potential V 1N with more than two electrodes as references;
Step 5: Calculating the generalized inverse G 1N + of the transfer matrix G 1N , thereby obtaining the standardized matrix R 1N = GG 1N + of the head surface potential with more than two electrodes as reference;
Step 6: Using the obtained standardized matrix R 1N and the head surface potential V 1N recorded with two or more electrodes as references, calculate the head surface potential V'=R 1N V 1N with infinity as a unified reference.
The EEG recording reference conversion method according to claim 5, wherein the specific calculation method of transfer matrix G described in step 3 is as follows: place a unit source on the solution space position numbered 1, and calculate the solution space The potential distribution generated by the unit source at each acquisition electrode position of the EEG detection system, the obtained potential distribution constitutes the first column in the transfer matrix G, and after traversing all the solution spaces and placing the unit source in turn according to the above method, a complete The transfer matrix G of .
EEG recording reference conversion method according to claim 5, is characterized in that, the specific formula of transfer matrix G described in step 4 is as follows: Among them, G i is a matrix composed of rows corresponding to the potential collected by the collection electrode with the i-th reference electrode as a reference in the transfer matrix G, is a column vector with a size of p i Ã1, each element of which is unit 1, p i is the number of collection electrodes referenced by the i-th reference electrode, yes transpose.
EEG recording reference conversion method according to claim 5, is characterized in that, the specific formula of transfer matrix G described in step 4 is as follows:
Among them, G i is a matrix composed of rows corresponding to the potential collected by the collection electrode with the i-th reference electrode as a reference in the transfer matrix G, is a column vector with a size of p i Ã1, each element of which is unit 1, p i is the number of collection electrodes referenced by the i-th reference electrode, yes transpose.
The EEG recording reference conversion method according to claim 5, characterized in that the generalized inverse G 1N + of the transfer matrix G 1N described in step 5 is obtained by calculation using singular value decomposition.
Ref WO2015185911: Claims:
A method of processing an EEG and/or MEG signal to generate image data representing a 3D current distribution, J, within the brain, the method comprising:
capturing a plurality of electric and/or magnetic measurements from the exterior of the head;
solving an integral equation for a part of said current distribution to generate said image data representing said 3D current distribution, wherein said integral equation comprises an integral of a first function representing said part of said current distribution and of a second function (V T v s (r, x) ) representing the geometry and conductivity of the head independent of said current distribution;
wherein said solving comprises:
modelling the head as at least two regions separated by at least one internal boundary, and
solving a set of partial differential equations, one for each said internal region, each partial differential equation comprising a geometry-conductivity function (w(r, x) ) representing the geometry and conductivity of the respective region, wherein said solving is subject to a boundary condition that either i) the gradients of the functions across the or each said internal boundary are smooth when conductivity is taken into account, or ii) a normal component of the electric field of said part of said current distribution is continuous across the or each said internal boundary, and wherein said geometry-conductivity function for an outermost said region of said head defines said second function ( V T v s (r, Ï) ).
A method as claimed in claim 1 wherein said set of partial differential equations comprise a set of partial differential equations dependent said geometry and conductivity of the respective regions and independent of said 3D current distribution, each representing the effect of geometry and conductivity on an electric field in the region from the next inner region, and wherein the electric field in an innermost said region comprises a field generated by a monopole source.
A method as claimed in claim 2 wherein said innermost region contains said 3D current distribution and wherein a partial differential equation for said innermost said region is non-Laplacian.
A method as claimed in claim 1 , 2 or 3 wherein said set of partial differential equations comprises a partial differential equation for an innermost said region in the form: o c 2 w c oc Î½.Î²<5 (Î - Ï) where r is a position vector within said innermost region, Ï is a dummy variable, Q is a charge, Î´ is the delta function, w c \s the geometry-conductivity function for said innermost region and u c \s an electrical conductivity of said innermost region; and wherein one or more partial differential equations for subsequent successively more outer regions j have the form:
A method as claimed in claim 4 wherein said boundary conditions define that dw, l dw .
Ï , Î¹ ââ = Ï â - dn dn where j-1 denotes a region within region j, and where n denotes a unit vector outward normal to the surface of the boundary between regions j-1 and j.
A method as claimed in any preceding claim wherein said geometry- conductivity function for said outermost region of said head. M^ (r, x) , defines said second function (V T v s (r, x) ) through the relationship that w s (r, x) is proportional to said second function (w s (r, Ï) oc V T v s (r, Ï) ).
A method as claimed in any preceding claim wherein said integral equation comprises one or both of:
or an equation derivable therefrom, where u s (r) defines measured electric potential on a boundary of said outermost region of the head [the scalp] at position r, Q c defines an innermost said region, Q s defines a surface of said outermost region of the head, v s (r, Ï) is an integral of said second geometry-conductivity function V T v s (r, Ï) for said outermost region of said head, and Ï is a scalar value representing an irrotational part of said 3D current distribution; and
Î¼ Ji Â¾ I râ T I
+â f (V 2x Â¥(z))r - H(r,T)dV(z) r e Q e ,
or an equation derivable therefrom, where B(r) defines a measured magnetic field at a sensor position r, Q c defines said innermost region of the head, and Q e defines an external region beyond said outermost said region of said head, H(r,r) is a vectorial geometry-conductivity function for said head defined by said geometry-conductivity functions for said regions of said head ( Î½ (Î¹ Ï) ), Ï is a scalar value representing an irrotational part of said 3D current distribution and Ï Â· Î(Ï) defines a radial component of a solenoidal part of said 3D current distribution.
A method as claimed in any preceding claim further comprising determining a scalar value, Ï, representing an irrotational part of said 3D current distribution, J, from said plurality of electric and/or magnetic measurements.
A method as claimed in claim 8 wherein said determining of said scalar value, Ï, uses an expansion of Ï in terms of expansion basis functions having respective unknown coefficients defined by a vector Î», where b = Î.Î», where b comprises a vector representation of said measurements of electric potential, b(r ; . ) at positions r ; . , and where elements E(i ) of matrix E are defined by a combination of a yth respective basis function and said geometry-conductivity function for said outermost region of said 

 head. M^ (r, x) at position r j . , wherein E represents the geometry and conductivity of the head independent of said current distribution, the method comprising determining an estimate of said unknown coefficients, Î», for a solution of b = Î.Î» to determine said representation of Ï.
A method as claimed in claim 8 or 9 wherein said EEG/MEG signal is an EEG signal, wherein said plurality of electric and/or magnetic measurements comprises a plurality of measurements of electric potential on the scalp (b), and wherein said image data includes a representation of Ï.
1. A method as claimed in any preceding claim wherein said EEG/MEG signal comprises an MEG signal, the method comprising determining a radial component of a solenoidal part of said 3D current distribution, and wherein said image data comprises a representation of said radial component of a solenoidal part of said 3D current distribution.
A method as claimed in claim 11 wherein said determining of said radial component 
[Truncated to input limit]