CN117952195B - Brain network construction method and display equipment based on task related brain electrical activity - Google Patents
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Abstract
The invention relates to the technical field of electroencephalogram, in particular to a brain network construction method and display equipment based on task related brain electrical activation. The brain network construction method comprises the following steps: acquiring an inspection matrix based on task related brain electrical activation of each channel; converting the inspection matrix into a space matrix conforming to the physical distribution of the channels; the joint matrix for the associated channels is obtained, i.e. the association coefficients of the channels are weighted with the physical distance in the spatial matrix. According to the invention, no electrical stimulation is required to be applied, and the degree of connection strength among the brain tissue nodes is obtained by checking the task-related brain electrical activity of the brain tissue with the function to be detected in the whole task time course and is combined with the physical position of each node, so that comprehensive and objective visualization of brain function area distribution and association conditions is realized, and the accurate judgment of the relation between the focus and the brain function area by a doctor is facilitated.
Description
Technical Field
The invention relates to the technical field of electroencephalogram, in particular to a brain network construction method and display equipment based on task related brain electrical activation.
Background
In the medical field, brain networks characterize the function of each brain tissue, assisting doctors in precisely locating the relationship between tumors, epileptic lesions and the like and brain functional areas, thereby carrying out excision surgery. In the prior art, an electroencephalogram signal generated by the current brain tissue along with different tasks is generally obtained through electrodes placed on the brain tissue, and then the brain tissue function is positioned in detail based on the analysis of the electroencephalogram signal, so that a brain network is constructed.
Among them, ESM (Electric Stimulation ) is used as the industry gold standard to locate brain function region. The ESM places positive and negative electrodes on brain tissue and applies current between them, so that the part of brain tissue temporarily loses function to simulate the resected effect of the brain tissue, only a pair of electrodes can be applied with electric stimulation each time, and each electric stimulation needs to be repeated by a patient to complete tasks, so that it is difficult to have enough time to select all the electrodes to perform electric stimulation sequentially in operation, and only the negative or positive results of the part of electrodes can be recorded, and thus the obtained brain functional area distribution and association condition are incomplete, and the brain network cannot be completely displayed.
Disclosure of Invention
The invention provides a brain network construction method and display equipment based on task related brain electrical activity, which are used for comprehensively and completely showing the distribution and association conditions of brain functional areas.
In order to solve the above technical problems, in a first aspect, the present invention provides a brain network construction method based on task related brain electrical activity, including: acquiring an inspection matrix based on task related brain electrical activation of each channel; converting the inspection matrix into a space matrix conforming to the physical distribution of the channels; acquiring a continuous edge matrix for associating channels, namely weighting association coefficients and physical distances of the channels in the space matrix; the acquisition of the test matrix based on the task related electroencephalogram activation degree of each channel comprises the following steps: dividing the electroencephalogram signal segments, namely dividing the electroencephalogram signal segments corresponding to task moments into baseline segments and task segments according to the front and the back of the task starting node; acquiring data characteristics, namely respectively calculating the data characteristics of a base line segment and a task segment; and (3) checking the difference degree of the distribution formed by the data characteristics of the task segment and the base segment to form a check matrix with dimensions respectively representing the channel and the task time. The method for acquiring the space matrix comprises the following steps: converting the check matrix into an n-order tensor, wherein the characterization of each dimension comprises: channel row, channel column, task time, channel group; and carrying out dimension reduction on the dimension average value of the task time to obtain a space matrix.
Further, element positions of channel row dimensions and channel column dimensions in the n-order tensor accord with channel physical distribution; the space matrix is an n-1 dimensional matrix with dimensions respectively representing channel rows, channel columns and channel groups.
Further, the space matrix is updated through convolution operation, namely, each element in the space matrix is combined with surrounding elements to obtain the space matrix.
Further, obtaining the conjoined edge matrix for the associated channel includes: setting a weighting equation, namely a weighting result=a×x+b×y, wherein x is the physical distance between channels, y is the association coefficient between channels, a is the distance weighting coefficient, and b is the association weighting coefficient; setting the association coefficient as any one of a pearson association coefficient, mutual information, cosine similarity and the same variation degree among channels.
In a second aspect, the present invention provides a brain network display method, including: the brain network is constructed by adopting the brain network construction method; displaying the edge connection form of the related channel.
Further, displaying the edge-connected morphology of the related channel includes: normalizing the element values in the edge matrix; setting a representation relation between a display effect of the edge connection form and element values after normalization processing; and mapping the element values in the continuous edge matrix into continuous edge forms of related channels according to the characterization relation to form a brain network image.
Further, the characterization relationship includes: when the normalized element value is smaller than the set blank threshold, the continuous edge form is characterized as blank, otherwise, the continuous edge form is characterized as a line according to a continuous edge equation; wherein, the edge equation is: edge morphology characterization effect = element value x morphology coefficient, the morphology coefficient comprising: depth coefficient, line width coefficient, line type coefficient, transparency coefficient.
In a third aspect, the present invention provides a brain network display device comprising: a processor and a display; wherein the processor is used for executing the steps of a brain network display method based on task related brain electrical activity; the display is used for displaying the connecting edge form of the related channel, namely the brain network image.
In a fourth aspect, the present invention provides a computer device comprising a memory storing a computer program and a processor implementing steps of a brain network construction method or a brain network display method when the computer program is executed by the processor.
In a fifth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a brain network construction method or a brain network display method.
The invention has the beneficial effects that:
According to the invention, no electrical stimulation is required to be applied, and the degree of connection strength among the brain tissue nodes is obtained by checking the task-related brain electrical activity of the brain tissue with the function to be detected in the whole task time course and is combined with the physical position of each node, so that comprehensive and objective visualization of brain function area distribution and association conditions is realized, and the accurate judgment of the relation between the focus and the brain function area by a doctor is facilitated.
Drawings
The invention will be further described with reference to the drawings and examples.
Fig. 1 is a flow chart of a brain network construction method of the present invention.
Fig. 2 is an algorithm flow chart of the check matrix of the present invention.
FIG. 3 is a schematic of an algorithm of the inspection matrix of the present invention.
FIG. 4 is a graph of electrode pair stimulation results data for comparative example 1 of the present invention.
Fig. 5 is a schematic diagram of the brain network of comparative example 1 of the present invention.
Fig. 6 is a schematic diagram of a brain network of embodiment 7 of the present invention.
Fig. 7 is a schematic diagram of a brain network according to embodiment 8 of the present invention.
Fig. 8 is a schematic diagram of a brain network at stage one of embodiment 9 of the present invention.
Fig. 9 is a schematic diagram of a brain network at stage two according to embodiment 9 of the present invention.
Fig. 10 is a schematic diagram of the brain network at stage three of embodiment 9 of the present invention.
Fig. 11 is a schematic diagram of the brain network at stage four of embodiment 9 of the present invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The present invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the invention, whereby the invention is not limited to the specific embodiments disclosed below.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
Example 1.
As shown in fig. 1, embodiment 1 provides a brain network construction method based on task related brain electrical activity, which includes the following steps: step S1, acquiring an inspection matrix based on task related brain electrical activation of each channel; s2, converting the inspection matrix into a space matrix conforming to the physical distribution of the channel; and step S3, acquiring a continuous edge matrix for associating channels, namely weighting the association coefficient of the channels and the physical distance in the space matrix.
It should be noted that, in embodiment 1 of the present invention, the channel physical distribution is the relative row and column positions of the channels included in all the set acquisition electrode nodes, and also corresponds to the node positions in the brain network image, the number of rows of the electrode channels is the number of channel rows, the number of columns is the number of channel columns, and the total number of channels=the number of channel rows×the number of channel columns.
It should be noted that, the test includes multiple tasks, the node at which each task starts is defined as task time, a fixed length or an interval of different lengths is set between the tasks (generally used between tasks of different types), then the electroencephalogram signal segments corresponding to each task time are divided into a base line segment and task segments according to the front and back of the task starting node, that is, the interval before the task time in each signal segment is the base line segment, the part activated by the task after each task time is the task segment, and the signal segment collected by each task is the base line segment before the task time and the task segment after the task time. The signal section is a two-dimensional matrix with the number of lines as the number of channels and the number of columns as the number of total sample points, the base line section is a two-dimensional matrix with the number of lines as the number of total channels and the number of columns as the number of base line sample points, and the task section is a two-dimensional matrix with the number of lines as the number of total channels and the number of columns as the number of task sample points.
As an alternative embodiment for obtaining the inspection matrix.
It should be noted that, step S1 is equivalent to time characterization based on the task related electroencephalogram activation degree of each channel, and specifically includes the following steps:
Step S11, dividing the electroencephalogram signal segments, namely dividing the electroencephalogram signal segments corresponding to task moments into a base line segment and a task segment according to the front and the back of a task starting node, and then assisting data feature extraction and activation degree inspection in the mode of overlapping sliding window slices in the embodiment 1 of the invention, wherein real-time performance is ensured, the base line segment is subjected to sliding window slicing to obtain a base line window, and the task segment is subjected to sliding window slicing to obtain a task window. Optionally, referring to fig. 3, the baseline window is a two-dimensional matrix, the number of rows is the total number of channels, the number of columns=window length×sampling rate=baseline sliding window number, the number of columns is the total number of channels, the number of columns=window length×sampling rate=task sliding window number, the number of columns and the number of columns are not limited, and the number of columns of the baseline window and the task window are guaranteed to correspond according to practical application; baseline sample number = baseline sliding window number x window length x sample rate, task sample number = task sliding window number x window length x sample rate, total sample number = baseline sample number + task sample number, typically the window lengths of the baseline and task windows are equal.
In step S12, the data characteristics of the base line segment and the task segment are calculated, respectively, and in this embodiment 1, the data characteristics of the base line window and the task window are calculated. The method for extracting the data features comprises the following steps: filtering, namely removing the power frequency and the frequency multiplication of the signals to obtain filtered signals; extracting features, performing FFT (fast Fourier transform) on the filtered signals, and taking absolute values to obtain envelope signals; log transformation is carried out on the data of the set response frequency band of the envelope signal, so that the data characteristics are obtained. In the embodiment 1 of the invention, HIGH GAMMA values capable of clearly reflecting the significance of the electroencephalogram activation can be selected as data characteristics, and differential entropy, HFD, amplitude perception permutation entropy, LZ complexity, approximate entropy, li index, coherent imaginary part, weighted PLI, relative PSD, absolute PSD, IAF, IAF-prox, SASI, asym, alpha power variability and the like can also be adopted as data characteristics.
In step S13, the degree of difference of the distributions of the data features of the task segments and the base line segments is checked to form an inspection matrix, and in order to increase the reliability of the data, the historical data information is added to perform correction while considering the real-time data in this embodiment, that is, the degree of difference of the distributions of the data features of the current and historical task windows and the data features of the current and historical base line windows is calculated to form an inspection matrix corresponding to each signal segment, where the degree of difference is calculated by a statistical inspection method (including any one or a combination of several of T inspection, KS inspection, KL divergence inspection, machine learning model inspection, etc.). Since the base line segment (baseline window) is a rest state without tasks, whether the change of the data characteristic in each task window is caused by task stimulation can be characterized by checking the difference degree between the data characteristic in the task window and the baseline window. Specifically, the inspection matrix is a two-dimensional matrix with dimensions respectively representing the channel and the task time; for example, as shown in fig. 2 and 3, the signal of each baseline window includes a total channel number data feature, the signal of each task window includes a total channel number data feature, the difference between the distribution formed by the data feature of the T test task window and the data feature of the baseline window forms an inspection matrix, and the number of rows of the obtained inspection matrix is the total channel number and the number of columns is the task sliding window number.
As an alternative embodiment to acquiring the spatial matrix.
It should be noted that, the step S2 of converting the inspection matrix into a spatial matrix conforming to the physical distribution of the channel is equivalent to spatial characterization, and the specific acquisition method is as follows: step S21, firstly, converting the inspection matrix into n-order tensors which can be used for representing channel rows, channel columns, task time and channel groups in each dimension, wherein the element positions of the channel row dimension and the channel column dimension in the n-order tensors are arranged according to actual physical distribution of electrode channels, the task time dimension is represented as a task sliding window number in the embodiment of the invention, and the channel group dimension is represented as the association between each channel and the channel group in the embodiment of the invention.
Specifically, the method for calculating the channel group dimension includes: classifying the channel groups according to the channel position, the brain function area or the custom mode, marking the serial number of each channel group, and marking the serial number of the channel group to which each channel belongs as the element of the corresponding channel group dimension. In practical application, for convenience of collection, large electrode plates containing a plurality of channels are usually used to be placed on brain tissues in different areas, namely, channel groups are classified according to channel positions, and each electrode plate is the channel group; or the verified channels belonging to the same brain function area are defined as abstract channel groups, and the connection among nodes in each brain function organization can be further calculated in a targeted manner through the connected edge matrix. It can be seen that the channel group dimension includes grouping information of channels, for example, the number of channels, channel positions, inter-channel distances, inter-electrode-sheet distances, inter-channel-combination distances, functional areas to which channels belong, and the like included in each electrode sheet or channel combination, so as to summarize the relationship between channel groups, and be used for ensuring that the edge connecting matrix can be constructed in a scene including a plurality of channel groups.
Specifically, in step S22, the average value of the n-order tensor in the dimension of the task time is calculated to reduce the dimension, and the average value of the corresponding position is used as a matrix element to obtain a space matrix of n-1 dimension of each dimension representing the channel row, the channel column and the channel group. For example, if only one channel group exists, converting the inspection matrix into a third-order tensor, wherein the number of the lines is the number of the channel lines, the number of the columns is the number of the channel columns, the depth is the number of the task sliding windows, then carrying out dimension reduction on the dimension average value of the number of the task sliding windows, and taking the average value of the corresponding position as a matrix element to obtain a two-dimensional space matrix of each dimension representing the channel line and the channel column; if a plurality of channel groups exist, the inspection matrix is converted into a fourth-order tensor, the dimension of the fourth-order tensor is the number of channel rows, the number of channel columns, the number of channel groups and the number of task sliding windows, then the average value of the number of task sliding windows is calculated, the dimension is reduced, and the average value of the corresponding positions is used as a matrix element to obtain a three-dimensional space matrix representing the channel rows, the channel columns and the channel groups in each dimension. Further, the difference of the number of channel rows and the number of channel columns in each channel group does not affect the calculation of the matrix, and only the space is needed to be filled with zero at the vacant position or the part of the storage space is not used, for example, 3 electrode plates (i.e. 3 channel groups) with channel specifications of [ 1*4 ], [ 2 x 2 ], [ 8 x 8 ], [ 8 x 3 ] are adopted in practical application, so that the space matrix with the dimension of 8 x 3 is obtained. The matrix rearrangement through the n-order tensor is beneficial to simply realizing the compression and extraction of the information in the whole task time, so that one element in the space matrix contains the stimulation degree information of the whole time course, and the time continuity of the brain network is ensured.
As an alternative embodiment for obtaining the edge matrix.
It should be noted that, step S3 of obtaining the edge matrix for the association channel includes: a weighting equation is set, i.e., a weighting result=a×x+b×y, where x is the physical distance between channels, y is the inter-channel correlation coefficient, a is the distance weighting coefficient, and b is the correlation weighting coefficient. The coefficients a and b are set according to the expected rule of constructing the brain network, for example, the physical distance between channels of the high-density electrode is smaller, the value of the corresponding a is larger, the importance of the association degree between channels of the deep electrode is strong, and the value of the corresponding b is larger for accurately distinguishing the channel calculation.
Specifically, the association coefficient is set to be any one of pearson association coefficient, mutual information, cosine similarity, same variation degree and the like between the associated channels. The row number of the edge matrix is the channel row number, the column number is the channel column number, the edge matrix is a symmetrical square matrix, and the j-th column element of the i-th row and the i-th column element of the j-th row have the same size and the same meaning, namely the association coefficient between the i-th channel and the j-th channel. The construction of the continuous edge matrix represents the real physical distance between the electrodes and the weighting of the association coefficient activated by the whole time interval between the two electrodes to control the sparseness of the brain network, and the test matrix obtained by the time characterization is merged into the space matrix obtained by the space characterization, so that the whole-process information of all the electrodes participating in the test is linked to construct a perfect objective brain network.
Example 2.
On the basis of embodiment 1, the spatial matrix described in this embodiment 2 is updated by convolution operation. For example, a convolution check of 3*3 (5*5, etc.) is used to convolve each element in the spatial matrix with surrounding elements (elements in the covered portion of the convolution kernel), and all the elements are updated to obtain the spatial matrix. Specifically, if the electrode is not located at an edge or corner, the 8 electrode convolutions around it are combined, if the electrode is located at an edge, the 5 electrode convolutions around it are combined, and if the electrode is located at four corners, the 3 electrode convolutions around it are combined.
Example 3.
On the basis of embodiment 1 or 2, embodiment 3 further provides a brain network display method, which includes: the brain network is constructed by adopting the brain network construction method; displaying the edge connection form of the related channel.
It should be noted that displaying the border form (i.e., brain network image) of the relevant channel includes: and carrying out normalization processing on the element values in the continuous edge matrix, specifically taking the global maximum element of the matrix, setting normalization parameters, and dividing the element value of each continuous edge matrix by the value of the maximum element for normalization. And setting a representation relation between the display effect of the continuous edge form and the element values after normalization processing, and finally mapping the element values in the continuous edge matrix into the continuous edge form of the related channel according to the representation relation to form a brain network image. Wherein, the characterization relationship comprises: when the normalized element value is smaller than the set blank threshold, the edge connecting form is characterized as blank, otherwise, the edge connecting form is characterized as a line according to an edge connecting equation, and the edge connecting equation is as follows: edge morphology characterization effect = element value morphology coefficient, morphology coefficient comprising: the depth coefficient, the line width coefficient, the line type coefficient and the transparency coefficient, that is to say, the edge-connecting form characterization effect can show different depths, thicknesses, line types, transparency and the like of the edge-connecting lines, and generally, the darker the color is, the thicker the line is, the higher the node activation degree represented by the edge connection is, the lighter the color is, the thinner the line is, and the lower the node activation degree represented by the edge connection is. The information of the depth, thickness, line type and the like of each continuous edge on the brain network image is related to the corresponding position elements in the continuous edge matrix, so that the global space of the connection strength among all nodes in the brain network is visualized, and the brain network which can reflect the activation degree of a large number of electrodes and is continuous in the time dimension is output in real time.
In particular, the positions of nodes in the brain network image correspond to the relative positions of elements in the spatial matrix in a one-to-one correspondence, that is, each element in the matrix corresponds to a channel on an electrode pad, for example, the information acquired by the electrode pad channel of the 3 rd row and 4 th column corresponds to the element of the 3 rd row and 4 th column of the spatial matrix.
It should be noted that, the relevant channels refer to any two channels that are obtained by taking out computation from all the acquisition channels, and in the embodiment of the present invention, the sparse brain network is calculated by selecting the adjacent channels. The selection of adjacent channels comprises the following steps: if the electrode channel is not positioned at the edges or corners of all channels, 4 channel nodes on the upper, lower, left and right sides of the electrode channel can be taken as adjacent channels, if the electrode channel is positioned at the edges of all channels, 3 channel nodes around the electrode channel are taken as adjacent channels, and if the electrode channel is positioned at the four corners of all channels, 2 channel nodes around the electrode channel are taken as adjacent channels.
Example 4.
On the basis of embodiment 3, this embodiment 4 also provides a brain network display device, including a processor and a display; the processor is used for executing the steps of the brain network construction method based on task related brain electrical activity; the display is used for displaying the connection edge form of the related channel, namely the brain network image.
In an alternative embodiment, the clinical application process of the invention can be as follows: firstly, estimating possible positions of a focus as a functional area to be detected according to technical means such as magnetic resonance imaging, epileptic discharge position judgment and the like, then performing craniotomy, placing electrode slices (including standard electrode slices with larger specification and interval, high-density electrode slices with smaller specification and interval, microelectrodes and the like) on brain tissues of the function to be detected or inserting deep electrodes inside for signal acquisition, and particularly, one acquisition electrode slice can comprise a plurality of channels, and the electrode slices or electrode channel combinations comprising the channels can be placed at a plurality of positions in one operation. Then, optionally, the subject is woken up during surgery to perform tasks, or is allowed to perform tasks during the treatment/experimental phase after implantation of the electrodes (tasks may be selected for naming by looking at the images, repeating the listening words, etc., typically each task is not performed independently by a single brain region, most likely in time series with visual, auditory, mental, linguistic, etc. areas in the brain functional areas). Finally, each electrode channel collects intracranial brain electrical signals in real time, and transmits the signals to computer equipment after digital-to-analog conversion and calculates the signals through a processor of the computer equipment, wherein the computer equipment stores a computer program capable of executing a brain network construction method based on task related brain electrical activity. The complete network comprising all nodes is constructed by collecting the brain electrical signals at the same time by all electrodes during the task rather than the traditional electrode pair sequential stimulation mode.
On the basis of embodiment 1 or 2 or 3, this embodiment 5 provides a computer device including a memory and a processor, the memory storing a computer program, the processor executing the steps of the brain network construction method or the brain network display method. Such as, but not limited to, a computer, tablet, server, etc.
On the basis of embodiment 1 or 2 or 3, the present embodiment 6 provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a brain network construction method or a brain network display method. Such as, but not limited to, a usb disk, hard disk, memory card, etc.
The technical effects of the present invention will be described below by way of specific examples.
Comparative example 1.
Stimulation results of 17 pairs of electrodes using ESM method. Referring to the data shown in FIG. 4, wherein stim1 is the first stimulating electrode, stim2 is the second stimulating electrode, and stim1 and stim2 are a pair of electrodes; value=0 is negative, which means that the subject lacking the electrode pair corresponding to the brain network function cannot complete the current task after receiving the electrical stimulation; value=1 is positive, which means that the subject lacking the electrode pair corresponding to the brain network function can complete the current task after receiving the electric stimulation, and the brain network diagram of fig. 5 is obtained through simulation in Matlab, and the horizontal and vertical coordinates in fig. 5 are the position coordinates of the brain network.
The brain network construction method of the present example 1 was adopted by using the electrode node data of the same patient and the same coordinates as those of the comparative example 1, but the brain network was not constructed by performing convolution operation on the space matrix, the obtained brain network is schematically shown in fig. 6, the horizontal and vertical coordinates in fig. 6 are the position coordinates of the brain network, more brain network nodes are shown in the graph than those of the comparative example 1, but more connecting side vacancies exist, the common parts of the two are compared, and the accuracy rate of the example 7 is 60% compared with that of the comparative example 1.
The electrode node data of the same patient and the same coordinates as those of the comparative example 1 are adopted, the brain network construction method of the embodiment 2 is adopted, namely, the space matrix is convolved in the process of constructing the brain network, the obtained brain network schematic diagram is shown in fig. 7, the horizontal coordinate and the vertical coordinate in fig. 7 are the position coordinates of the brain network, the accuracy of the comparison of the embodiment 8 and the comparative example 1 is 100%, more brain network nodes are clearly depicted by convolution in the embodiment 8, the complete network is better presented, and compared with the single negative or positive result, the intermediate state between significant activation and non-activation can be further characterized.
The same patient and the same coordinate electrode node data as those of comparative example 1 were used, the brain network display method of example 3 was used, normalized parameter=11 and blank threshold=0.8 were set, fig. 8 is a schematic diagram of the brain network in stage one of example 9, fig. 9 is a schematic diagram of the brain network in stage two of example 9, fig. 10 is a schematic diagram of the brain network in stage three of example 9, fig. 11 is a schematic diagram of the brain network in stage four of example 9, the horizontal and vertical coordinates in fig. 8 to 11 are the position coordinates of the brain network, and the accuracy of the result in stage four relative to comparative example 1 was 86.67%. Because the test person repeats the same task for a long time and is easy to fatigue to influence the result, tens of test times can be used as one stage in the experiment of carrying out the same task, the test times in each stage are the same, and rest time is reserved between the stages. Although the electrode activation degree is high or low each time, the data result obtained in each stage is overlapped and corrected on the result of the history stage, the activation weight of the area which is not important for a certain task in the brain network is reduced, the activation weight of the important area is increased, an accurate and stable brain network image can be obtained after a plurality of stages of experiments, and as can be seen from the reference to fig. 8 to 11, the brain network image of the stage one to the stage four tends to be stable, the condition that the accuracy is low or the stimulation result is unstable and switched between the full positive and the full negative is not existed, and meanwhile, some low-importance continuous edges are removed. Comparing the two common parts, comparative example 1, which is closer to example 9, shows brain network results, i.e., performing tasks in stages is beneficial to assist in determining the reliability of the results.
In summary, the invention combines the inspection matrix obtained by the time characterization with the space matrix obtained by the space characterization, so that connection is established between electrode channels and between time points and time points, discrete results are integrated by taking the electrode channels as units, the activation degree of all the electrodes in the task time can be recorded and calculated only by a single task, the continuity of the time dimension is ensured, meanwhile, the connection strength degree between all nodes in the brain network is realized by realizing space visualization based on the task-related brain electrical activation degree, the situation of the brain tissue under each electrode in the whole-course task excitation function is objectively and comprehensively represented, the situation is closer to the result perceived by an actual doctor, the misjudgment on the importance of a single channel is avoided to a certain extent, and the doctor is guided to avoid the excision of the functional brain region in the operation process as much as possible. In addition, the invention sets electrodes on or in brain tissues with all functions to be detected, and performs analysis and calculation based on task feedback brain electrical signals, thereby avoiding the side effects of post discharge, illness attack, pain and the like of the traditional electrical stimulation mode.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (11)
1. The brain network construction method based on task related brain electrical activity is characterized by comprising the following steps:
Acquiring an inspection matrix based on task related brain electrical activation of each channel;
converting the inspection matrix into a space matrix conforming to the physical distribution of the channels;
acquiring a continuous edge matrix for the association channel, namely weighting elements in the space matrix by using the association coefficient between channels and the physical distance;
the connected edge matrix controls the sparseness degree of the brain network; wherein,
Acquiring the test matrix based on the task related brain electrical activation of each channel comprises:
Dividing the electroencephalogram signal segments, namely dividing the electroencephalogram signal segments corresponding to task moments into baseline segments and task segments according to the front and the back of the task starting node;
acquiring data characteristics, namely respectively calculating the data characteristics of a base line segment and a task segment;
checking the difference degree of the distribution formed by the data characteristics of the task segment and the base segment to form an inspection matrix with dimensions respectively representing the channel and the task time;
the method for acquiring the space matrix comprises the following steps:
converting the check matrix into an n-order tensor, wherein the characterization of each dimension comprises: channel row, channel column, task time, channel group;
and carrying out dimension reduction on the dimension average value of the task time to obtain a space matrix.
2. The method for constructing a brain network according to claim 1, wherein,
Element positions of channel row dimension and channel column dimension in the n-order tensor accord with channel physical distribution;
The space matrix is an n-1 dimensional matrix with dimensions respectively representing channel rows, channel columns and channel groups.
3. The method for constructing a brain network according to claim 1, wherein,
The calculation of the channel group dimension in the n-order tensor comprises the following steps:
dividing at least one channel group according to the channel position and the brain function area and marking serial numbers;
the serial number of the channel group to which each channel belongs is marked as the element of the dimension of the corresponding channel group.
4. The method for constructing a brain network according to claim 1, wherein,
Acquiring the edge matrix for the associated channel comprises:
setting a weighting equation, namely a weighting result=a×x+b×y, wherein x is the physical distance between channels, y is the association coefficient between channels, a is the distance weighting coefficient, and b is the association weighting coefficient;
Setting the association coefficient as any one of a pearson association coefficient, mutual information, cosine similarity and the same variation degree among channels.
5. The method for constructing a brain network according to claim 1, wherein,
The spatial matrix is updated by convolution operation, i.e
And convolving each element in the space matrix in combination with surrounding elements to obtain the space matrix.
6. A brain network display method, comprising:
constructing a brain network using the brain network construction method of claim 1;
Displaying the edge connection form of the related channel.
7. The brain network display method according to claim 6, wherein,
Displaying the edge connection form of the related channel comprises the following steps:
Normalizing the element values in the edge matrix;
setting a representation relation between a display effect of the edge connection form and element values after normalization processing;
and mapping the element values in the continuous edge matrix into continuous edge forms of related channels according to the characterization relation to form a brain network image.
8. The brain network display method according to claim 7, wherein,
The characterization relationship includes:
when the normalized element value is smaller than the set blank threshold, the continuous edge form is characterized as blank, otherwise, the continuous edge form is characterized as a line according to a continuous edge equation; wherein,
The edge equation is: edge morphology characterization effect = element value x morphology coefficient, the morphology coefficient comprising: depth coefficient, line width coefficient, line type coefficient, transparency coefficient.
9. A brain network display device, comprising:
A processor and a display; wherein the method comprises the steps of
The processor is configured to perform the steps of the brain network display method of claim 6;
The display is used for displaying the edge connection form of the related channel.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method according to claim 1 or 6 when executing the computer program.
11. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to claim 1 or 6.
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