KR20100026426A - An eeg-based real-time functional cortical connectivity monitoring system - Google Patents

Abstract

PURPOSE: An EEG(Electroencephalogram) based real time functional cortical connectivity monitoring system is provided to monitor the functional connectivity between brain cortical regions in real time using a mathematical modeling. CONSTITUTION: An EEG detector(110) detects an EEG signal and includes a brain electrode. A position information detector(150) detects the position information about an anatomical boundary between the brain electrode and the cerebral cortex. The position information detector includes a digitizer. An operation processor(300) detects the connectivity between the cerebral cortices by operating the position information from the position information detector and the EEG signal from the EEG detector. A display unit(410) displays the output of the operation processor.

Description

EEG-based real-time functional cortical connectivity monitoring system

The present invention relates to an EEG-based real-time functional cortical connectivity monitoring system capable of observing functional connectivity between brain cortical regions in real time using EEG, and more specifically, the brain cortex rather than the head surface through mathematical modeling. The present invention relates to an EEG-based real-time functional cortical connectivity monitoring system that can calculate the connectivity at the surface and visualize in real time.

Humans use their electrical signals to think, judge, and act. The functional roles that each brain area plays are different, but in general, no matter how simple the brain does, different areas of the brain interact. Therefore, it is possible to know which brain regions interact with each other when the brain performs a specific task through connectivity between brain regions.

For example, when a person receives a visual response such as looking at a picture, the occipital lobe responsible for the starting area of the brain is activated, and the frontal lobe responsible for thinking is activated when the picture looks handsome or pretty. . In this case, electrical signals from the brain can be thought to propagate from the occipital lobe responsible for vision to the frontal lobe responsible for thinking, and there is a connection between the two areas.

Recently, attention has been focused on observing connectivity between brain regions for the purpose of investigating brain function and diagnosing brain diseases. In other words, the subjects can present a specific stimulus or perform an action and then observe the connections between the brain regions associated with them, or observe brain-related diseases such as Alzheimer's, dementia, schizophrenia, autism and depression. By comparing and observing the connectivity between a patient suffering from a particular brain region of a normal patient, the difference in connectivity between the brain regions observed in a normal person and a patient can be used to diagnose a disease.

These studies have been studied by people involved in brain-related cognitive studies. However, most of the previous studies on the connectivity between brain regions showed the connectivity between brain regions after measuring EEG, and then post-processing. Conventionally, the connectivity between brain regions could not be seen in real time while measuring EEG.

Most previous studies also used connectivity values measured by electrodes attached to the subject's scalp surface to observe connectivity at the scalp surface. However, the EEG signals measured on the scalp surface are distorted due to the low electrical conductivity of the skull, so the measurement potential distribution on the scalp surface varies according to the direction of the EEG signal source. It is not neurophysiologically desirable.

In addition, in the conventional study, since EEG measurement and connectivity calculation were performed in separate procedures, it was impossible to calculate connectivity at the same time as EEG measurement.

Accordingly, when monitoring the connectivity between brain regions, a monitoring system that avoids distortion due to EEG measurement on the scalp surface and can observe the connectivity of accurate brain regions in real time is desired.

Therefore, the present invention calculates the connectivity at the cortical surface of the brain rather than the head surface at the same time through the mathematical modeling, and EEG-based real-time functional cortical connectivity monitoring to monitor the connectivity between brain regions by visualizing the observation results in real time Provide a system.

An object of the present invention is to provide an EEG-based real-time functional cortical connectivity monitoring system that calculates the connectivity between brain regions on the cortical surface of the brain through mathematical modeling.

Another object of the present invention is to provide an EEG-based real-time functional cortical connectivity monitoring system capable of observing changing connectivity by real-time visualizing functional connectivity between brain cortical regions.

Another problem to be solved by the present invention, it is possible to observe the real-time connectivity at the same time as the EEG measurement, mathematically interpret the forward problem (inverse problem) and inverse problem (inverse problem) signal of the electrode measured on the surface of the brain cortex By calculating the potential value and showing the connectivity, it provides an EEG-based real-time functional cortical connectivity monitoring system that reflects the signal of the original signal source that can be distorted by the low electrical conductivity of the skull.

According to an embodiment of the present invention for achieving the above object, the real-time EEG connectivity monitoring system, EEG detection unit for detecting an EEG signal having a brain electrode; provided with a digitizer anatomical boundary of the brain electrode and the cerebral cortex ( A location information detector for detecting location information of a cerebral cortex model; an operation of detecting connectivity between the cerebral cortex areas by calculating and processing the brain wave signal output from the electroencephalogram detection unit and the location information output from the location information detection unit; And a display unit for visualizing and outputting the output of the operation processor.

It further comprises a storage unit for storing the output of the operation processing unit.

According to another embodiment of the present invention for achieving the above object, the real-time EEG connectivity monitoring system, EEG detection unit for detecting the EEG signal having a brain electrode, brain electrode and cerebral cortex model (cerebral cortex anatomical In the real-time EEG connectivity monitoring system having a position information detection unit for detecting position information for the boundary), A / D conversion unit having an A / D conversion unit converts and receives the EEG signal output from the EEG detection unit into a digital signal, the position information A data collection unit which receives the location information from a detection unit; The inverse operator is generated by arithmetic processing of the position information received from the data collection unit, and the area of the brain to be observed is classified using the cerebral cortex model received from the data collection unit. A preprocessing unit; A fast Fourier transform unit for FFT converting the EEG signal in the time domain received from the data collection unit into an EEG signal in the frequency domain; And a connectivity calculator configured to calculate connectivity between brain regions classified by the preprocessor using an EEG signal output from the fast Fourier transform unit and an inverse operator output from the preprocessor.

And a visualization processor for generating a signal for visualizing the output signal from the connectivity calculator.

And a display unit for displaying the output from the visualization processor and a storage unit for displaying the output from the visualization processor.

 And a data input unit for inputting a threshold required for calculating the connectivity of the cerebral cortex and a frequency band to be observed.

The data input unit may further input MRI data.

And a data setting unit for storing the threshold value and the frequency band received from the data input unit and generating a cerebral cortical surface using a standard brain model.

And a data setting unit for storing the threshold value and the frequency band received from the data input unit and re-extracting an approximated cerebral cortex surface from the MRI data received from the data input unit.

The arrangement of the brain electrode is characterized in that using the 10/20 system of international electrode placement standard (MCN system).

In the preprocessing unit, the inverse operator (W) is

(Where A is a leadfield matrix obtained through a true problem analysis, R is a covariance matrix of a signal source, and C is a covariance matrix obtained, and is a normalization parameter).

The connectivity calculator is configured to calculate the connectivity between the cerebral cortical regions using a phase difference.

The connectivity calculation unit calculates the phase φ _i (t) at time t.

Where i represents the i-th region of the brain divided by the preprocessor, t represents time, and Im _i represents the imaginary part of the average signal source estimated through inversion in the i-th region, and Re _i represents the real part of the average signal source estimated through inversion in the i-th region).

The connectivity calculation unit pairs randomly divided brain regions divided by two regions (two regions), calculates connectivity using the phase value, and the phase difference between two regions is smaller than the preset threshold. In the same case, it is defined as having connectivity, otherwise it is characterized by being defined as having no connectivity.

The connectivity calculator calculates connectivity between cerebral cortical regions using one of calculation methods using coherence, mean phase coherence, and correlation.

According to another embodiment of the present invention for achieving the above object, the real-time EEG connectivity monitoring method, before starting the EEG measurement, the threshold value used in the calculation of connectivity, the frequency band to be observed, brain region An initialization step of receiving the MRI data information of the subject, the position of the brain electrode of the subject, and the position information of the cerebral cortex model; After the initialization step, the EEG measuring step of detecting the EEG of the subject; An inverse operator generation step of generating an inverse operator through Equation 1 using the position of the brain electrode position and the cerebral cortex model of the subject received in the initialization step; A cortical mapping step of converting the EEG signal of the scalp surface detected in the EEG measurement step into a signal of the brain cortical surface using the inverse operator generated in the inverse operator generation step; A fast Fourier transform step of FFT processing the EEG measured in the EEG measurement step; The phase difference is calculated using the EEG signal converted into the signal in the frequency domain in the fast Fourier transform step and the inverse operator generated in the inverse operator generation step, and whether connectivity is determined based on the threshold value set in the input setting step. The connectivity calculation step of determining; characterized in that consisting of.

A connectivity visualization step of visualizing connectivity between brain cortical regions calculated as having connectivity in the connectivity calculation step in real time through a continuous line between each region; displaying data visualized in the connectivity visualization step on a display and storing in a storage unit Displaying and storing data; It is determined whether the end command is input, and if the end command is input, and if the end command is not input, the system termination determination step of returning to the EEG measurement step; characterized in that it further comprises.

The EEG-based real-time functional cortical connectivity monitoring system of the present invention calculates connectivity between brain regions on the cortical surface of the brain through mathematical modeling and visualizes it in real time to observe the changing connectivity.

In addition, the EEG-based real-time functional cortical connectivity monitoring system of the present invention is capable of observing EEG and real-time connectivity at the same time, mathematically interprets the forward and inverse problems, the signal of the electrode measured brain By calculating what potential value is on the cortical surface and showing connectivity, the signal from the original signal source can be well reflected by the low electrical conductivity of the skull.

EEG-based real-time functional cortical connectivity monitoring system of the present invention can be used in the brain-computer interface. In the brain-computer interface that controls external machines with only human thought, we use various algorithms to extract and classify features from EEG signals to distinguish human thoughts. Using the connectivity pattern of the user can determine the intention.

In addition, the EEG-based real-time functional cortical connectivity monitoring system of the present invention can be used to diagnose brain-related diseases such as Alzheimer's, dementia, schizophrenia, autism and depression in real time, when the same behavior is performed by the general public and patients. EEG can be measured to determine other connectivity patterns. Since it is difficult to repeat experiments in patients with brain diseases, it is possible to observe the connectivity between brain regions in real time in the process of collecting EEG data using the present invention, more efficient real-time diagnosis is possible.

Hereinafter, with reference to the accompanying drawings, the configuration and operation of the EEG-based real-time functional cortical connectivity monitoring system according to an embodiment of the present invention will be described in detail.

1 is a block diagram schematically illustrating the configuration of an EEG-based real-time functional cortical connectivity monitoring system according to an embodiment of the present invention, an electroencephalogram detector 110, a location information detector 150, and a data input unit 170. , A data collection unit 200, a calculation processing unit 300, a display unit 410, and a storage unit 450.

The electroencephalogram detection unit 110 is a means for detecting brain waves by attaching a brain electrode to a subject, and includes a brain electrode unit 120, an amplifying unit 130, and a filter unit 140.

The brain electrode unit 120 includes one or more brain electrodes, which are scalp electrodes in contact with the scalp of the subject, to detect electroencephalogram (EEG). The placement of the brain electrodes may use the 10/20 system, which is an international electrode placement standard (MCN system).

As shown in FIG. 4, in the brain electrode unit 120, the brain electrodes are Fp1, Fp2, F3, F4, C5, C6, CP3, CP4, P5, P6, in the 10-20 system that is an international electrode placement standard (MCN system). It may be located at O1, O2, the position and number of the brain electrode unit 120 can be arbitrarily changed according to the portion to see the connectivity.

The amplifier 130 amplifies the EEG signal received from the brain electrode unit 120.

The filter unit 140 removes noise such as power supply noise from the EEG signal received from the amplifier 130.

The electroencephalogram detection unit 110 may further include an A / D conversion unit in some cases. In some cases, the A / D conversion unit may be embedded in the data collection unit 200.

The location information detector 150 is a means for detecting location information on the anatomical boundary of the brain electrode and the brain of the subject, and includes a three-dimensional digitizer 160.

The data input unit 170 inputs a threshold required for calculating the connectivity of the cerebral cortex before the EEG measurement, a frequency band (frequency band) to be observed, and the MRI data (magnetic resonance imaging, magnetic resonance imaging). In some cases, the data input unit 170 may exclude input of the MRI data of the subject.

The data collector 200 includes an A / D converter 210 to convert an analog signal received from the electroencephalogram detector 110 into a digital signal, and transmits the analog signal to the arithmetic processor 300, and also provides the location information detector 150. The signal is received from the data input unit 170 and transmitted to the calculation processing unit 300.

The calculation processing unit 300 calculates a signal received from the data collection unit 200. In addition, the calculation processing unit 300 receives the data input from the data input unit 170 to generate the data necessary for the calculation processing and stores it in the storage unit 450.

The display unit 410 displays the output of the operation processor 300 through a display unit. That is, the display unit 410 receives the output signal from the visualization processing unit 370 of the operation processing unit 300 to indicate the connectivity between the brain cortical regions on the screen.

The storage unit 450 stores the output of the operation processor 300. In addition, the storage unit 450 stores threshold values required for calculating the connectivity of the cerebral cortex, frequency bands to be observed, and the like. That is, the storage unit 450 receives the output signal from the operation processing unit 300 and stores the connectivity information and setting values between the brain cortical regions.

FIG. 2 is an explanatory diagram for schematically illustrating a configuration of an operation processor of FIG. 1, and includes a preprocessor 310, a fast Fourier transform unit 330, a connectivity calculator 350, a visualization processor 370, and a data setter ( 390).

The preprocessor 310 is a means for calculating an inverse operator necessary for mapping the EEG signals measured on the scalp surface of the subject to the cerebral cortex, and appropriately classifying brain regions. That is, the preprocessor 310 receives the electroencephalogram, the MRI data information, and the positional information about the brain electrodes and the anatomical boundaries of the brain of the subject from the data collection unit 200, and generates an EEG signal from the cerebral cortex. In order to determine the distribution of the signal source, an inverse operator is generated through linear estimation of the position of the received electrode and the location of the cerebral cortex model. The inverse operator W is obtained through Equation 1.

Where A is a leadfield matrix obtained through correct problem analysis, R is a covariance matrix of the signal source, C is a covariance matrix taken, and is a normalization parameter.

Here, the leadfield matrix is calculated by a boundary element method (BEM) or an approximate electrical conductivity calculation method.

In addition, the preprocessing unit 310 includes dividing the area of the brain to be observed according to the intention of the user by using the information of the brain model of the subject.

The cerebral cortical surface extracted from the MRI data has an accuracy of several millimeters or less, but using all the points on the extracted cortex is computationally inefficient, so the present invention is used to re-extract and analyze the approximate cortical plane.

In the present invention, as shown in Figure 4, the brain region is arbitrarily divided into 16, Figure 3 (a) is the front of the brain, Figure 3 (b) is the left side of the brain, Figure 3 (c) is the brain 3, (d) shows the back of the brain, respectively. This can be done by dividing the area of the brain into smaller pieces if you want a higher spatial resolution, depending on your needs, and vice versa.

The fast Fourier transform unit 330 receives an EEG signal from the A / D converter 150 of the data collector 200 and performs a Fast Fourier Transform (FFT). That is, the fast Fourier transform unit 330 FFTs the EEG signal in the time domain received from the data collection unit 200 and converts the EEG signal in the frequency domain into an EEG signal in the frequency domain.

The connectivity calculator 350 is a means for calculating connectivity between brain regions classified by the preprocessor 310 using the EEG signal received from the data collector 200 and information previously defined and calculated by the preprocessor 310. to be. That is, the connectivity calculator 350 distinguishes the preprocessing unit 310 by using the EEG signal converted into the signal in the frequency domain by the fast Fourier transform unit 330 and the inverse operator W generated by the preprocessor 310. The connectivity between the regions of the cerebral cortex is calculated using the phase difference. The phase φ _i (t) at time t is obtained through equation (2).

I denotes an i-th region of the brain divided by the preprocessor, t denotes time, and Im _i denotes an imaginary part of an average signal source estimated through inversion in the i-th region, and Re _i denotes i The real part of the average signal source estimated through inversion in the first region.

The connectivity calculation unit 350 randomly pairs the brain regions separated by the preprocessor 310, and calculates the connectivity using the pre-calculated phase values for all possible combinations. If the phase difference between the two regions is less than or equal to the threshold value, it is defined as having connectivity, otherwise it is defined as having no connectivity.

Although the connectivity between the cerebral cortical regions was calculated by the connectivity calculation unit 350 using a phase difference, in some cases (necessary), the connectivity calculator 350 coherences and averages the phase interference between the brain regions. It can also be defined by calculation using phase coherence or correlation. In other words, it is noted that the present invention is not limited to calculating connectivity using only the phase difference.

The visualization processor 370 receives the output signal from the connectivity calculator 350, generates a signal for visualization in real time, and outputs the signal to the display unit 410 and the storage unit 450. The maximum time resolution is 250ms, and the time resolution can be adjusted according to user needs. The frequency band to be observed is read from the storage 450, and accordingly, visualization data of the output of the connectivity calculator 350 is generated.

The data setting unit 390 is a means for receiving data input from the data input unit 170 to generate data necessary for arithmetic processing in the connectivity calculation unit 350 and storing the data in the storage unit 450. The data setting unit 390 stores the threshold required for calculating the connectivity of the cerebral cortex and the frequency band to be observed in the storage unit 450 before the EEG measurement received from the data input unit 170, and also stores the data input unit ( Approximated cerebral cortical surfaces are re-extracted from the MRI data of the subject received from 170 and stored in the storage unit 450. If the MRI data is not input to the data input unit 170, a cerebral cortical surface is generated using a standard brain model and stored in the storage unit 450.

That is, the data setting unit 390 stores the threshold value required for calculating the connectivity of the cerebral cortex and the frequency band to be observed before the EEG measurement from the user, and the data setting unit 390 sets the brain region. Based on the MRI data, the subject's MRI data is input and the approximate cerebral cortex is extracted from the MRI data, and if there is no MRI data, a cerebral cortex is generated using a standard brain model. .

Although the cerebral cortical surface extracted from the MRI data has an accuracy of several millimeters or less, it is inefficient to use every point on the extracted cortex, so the present invention is used to re-extract and analyze the approximate cortical plane.

5A and 5B illustrate examples of real-time connectivity between brain regions by an EEG-based real-time functional cortical connectivity monitoring system according to an exemplary embodiment of the present invention, and FIG. 5A illustrates a subject looking at a blank screen monitor. 5b shows the connectivity of the brain when the subject is familiar with the subject's photograph, that is, visual stimulus.

The frequency band used for the observations of FIGS. 5A and 5B is gamma, specifically 30 Hz.

It can be seen that the connectivity between brain regions when the visual stimulus of FIG. 5B is more complicated than the connectivity between brain regions when looking at the blank screen of FIG. 5A.

6 is a flowchart illustrating an operation of the EEG-based real-time functional cortical connectivity monitoring system of FIG.

In the initialization phase, before starting the EEG measurement, the threshold value used in the calculation of connectivity, the frequency band to be observed, and the brain region are set, and the MRI data information of the subject, the location of the brain electrode of the subject, and the cerebral cortex model are set. Receive the location information (S310).

That is, the data setting unit 390 sets the threshold required for calculating the connectivity of the cerebral cortex before the EEG measurement, and sets a specific frequency region of the EEG desired by the user. In addition, the brain region is set by dividing the brain region. When using the subject's MRI data, the subject's MRI data is also input. When there is no MRI data, the cerebral cortical plane is generated and input using a standard brain model.

The reason for setting the frequency band is that when the FFT is performed by the fast Fourier transform unit 330, the EEG signal in the time domain is converted into the EEG signal in the frequency domain, and the band of a specific frequency to be observed in the signal in this frequency domain. To observe the bay, the frequency band must be set. For example, if it is set to 8 ~ 12hz, only the signal in this area will be used when calculating connectivity. Brain region setting refers to the task of classifying brain regions.

In the EEG measurement step, after the initialization step (S310), the brain wave of the subject to be detected (S320).

In the step of generating an inverse operator, an inverse operator is generated through Equation 1 using the position information of the brain electrode position of the subject and the cortical model received in the initialization step S310 (S330).

In the cortical mapping step, the EEG signal of the scalp surface detected in the EEG measurement step (S320) is converted into a signal of the brain cortical surface using the inverse operator generated in the inverse operator generation step (S330) (S340).

In the fast Fourier transform step, the EEG measured in the EEG measurement step (S320) is FFT (S350). That is, the measured time series EEG signals are converted into signals in the frequency domain through the FFT.

In the connectivity calculation step, the phase difference is calculated through Equation 2 using the EEG signal converted into the signal in the frequency domain in the fast Fourier transform step (S350) and the inverse operator generated in the step S340 generation step (S340), and input. It is determined whether the connectivity is based on the threshold value set in the setting step (S310) (S360).

That is, the connectivity between the EEG signal converted into the signal in the frequency domain in the fast Fourier transform step (S350) and the cortical region mapped in the cortical mapping step (S340) is calculated using the phase difference, and in the input setting step (S310). The connectivity is determined based on the set threshold. If the phase difference between the cerebral cortex regions is less than or equal to the preset threshold, the connectivity is defined as being. If the phase difference is greater than the preset threshold, the connectivity is defined as being non-connecting.

In the connectivity visualization step, the connectivity between the areas of the brain cortex calculated as having connectivity in the connectivity calculation step (S360) is visualized through a continuous line between each region in real time (S370).

That is, the connectivity visualization step (S370) visualizes the connectivity between the regions showing significant connectivity by comparing the threshold calculated in the initialization step (S310) with the connectivity calculated in the connectivity calculation step (S360).

In the data display and storage step, the data visualized in the connectivity visualization step S370 is displayed on the display and stored in the storage unit S380.

In the system shutdown determination step (S390), if a shutdown command is input, the system is terminated, and if the shutdown command is not input, the procedure returns to the brain wave measurement step (S320). Here, the termination command may cause the termination command to be input by software when the EEG detection signal is no longer detected, the termination switch (not shown) is turned off, or a predetermined time passes.

That is, the reason for returning to the EEG measurement step (S320) is that when the connectivity is visualized in real time after the connectivity calculation, the above work is performed in parallel for the next visualization.

The present invention is not limited to the above described and illustrated in the drawings, and of course, more modifications and variations are possible to those skilled in the art within the scope of the following claims.

1 is a block diagram schematically illustrating the configuration of an EEG-based real-time functional cortical connectivity monitoring system according to an embodiment of the present invention.

FIG. 2 is an explanatory diagram for schematically illustrating a configuration of an arithmetic processing unit of FIG. 1.

Figure 3 is an explanatory diagram for explaining the area of the brain in the EEG-based real-time functional cortical connectivity monitoring system according to an embodiment of the present invention.

4 is an explanatory diagram of a general 10/20 system.

5A illustrates an example of real-time connectivity between brain regions by the EEG-based real-time functional cortical connectivity monitoring system of the present invention.

Figure 5b is another example showing the real-time connectivity between brain regions by the EEG-based real-time functional cortical connectivity monitoring system of the present invention.

6 is a flowchart illustrating an operation of the EEG-based real-time functional cortical connectivity monitoring system of FIG.

<Explanation of symbols for the main parts of the drawings>

110: electroencephalogram detection unit 120: brain electrode unit

130: amplification unit 140: filter unit

150: location information detection unit 160: digitizer

170: data input unit 200: data collection unit

210: A / D conversion unit 300: arithmetic processing unit

310: preprocessor 330: fast Fourier transform unit

350: connectivity calculator 370: visualization processor

390: data setting unit 410: display unit

450: storage

Claims (17)

An electroencephalogram detector comprising an electroencephalogram to detect an EEG signal; A location information detector having a digitizer for detecting location information on an anatomical boundary (cortical model) of the brain electrode and the cerebral cortex; An arithmetic processing unit that detects connectivity between the cerebral cortical regions by arithmetic processing the EEG signal output from the EEG detecting unit and the positional information output from the positional information detecting unit; A display unit for visualizing and displaying the output of the operation processor; Real-time EEG connectivity monitoring system comprising a. The method of claim 1, Real-time EEG connectivity monitoring system further comprises a storage unit for storing the output of the calculation processing unit. In the real-time EEG connectivity monitoring system having an electroencephalogram detection unit for detecting an EEG signal having a brain electrode, and a position information detection unit for detecting position information of the brain electrode and the cerebral cortex model (anatomical boundary of the cerebral cortex), A data collection unit including an A / D conversion unit for converting and receiving an EEG signal output from the EEG detection unit into a digital signal, and receiving the position information from the position information detection unit; The inverse operator is generated by arithmetic processing of the position information received from the data collection unit, and the area of the brain to be observed is classified using the cerebral cortex model received from the data collection unit. A preprocessing unit; A fast Fourier transform unit for FFT converting the EEG signal in the time domain received from the data collection unit into an EEG signal in the frequency domain; A connectivity calculator configured to calculate connectivity between brain regions classified by the preprocessor using an EEG signal output from the fast Fourier transform unit and an inverse operator output from the preprocessor; Real-time EEG connectivity monitoring system comprising a. The method of claim 3, And a visualization processor configured to generate a signal for receiving and visualizing the output signal from the connectivity calculator. The method of claim 4, wherein A display unit which displays an output from the visualization processing unit, And a storage unit for displaying the output from the visualization processing unit. The method of claim 5,  And a data input unit for inputting a threshold required for calculating the connectivity of the cerebral cortex and a frequency band to be observed. The method of claim 6, The data input unit is a real-time EEG connectivity monitoring system, characterized in that for further inputting MRI data. The method of claim 6, And a data setting unit configured to store the threshold value and the frequency band received from the data input unit, and generate a cerebral cortical surface using a standard brain model. The method of claim 7, wherein And a data setting unit for storing the threshold value and the frequency band received from the data input unit and re-extracting an approximated cerebral cortex surface from the MRI data received from the data input unit. system. The method according to any one of claims 1 to 3, The brain electrode arrangement is a real-time EEG connectivity monitoring system, characterized in that using the International Electrode Placement Standard (MCN system) 10/20 system. The method of claim 3, In the preprocessing unit, the inverse operator (W) is

(Where A is a leadfield matrix obtained by corrective problem analysis, R is a covariance matrix of the signal source, C is a covariance matrix that is caught, and is a normalization parameter) Real-time EEG connectivity monitoring system, characterized in that obtained through. The method of claim 3, The connectivity calculation unit Real-time EEG connectivity monitoring system, characterized in that for calculating the connectivity between the cerebral cortical region using a phase difference (phase difference). The method of claim 12, The connectivity calculation unit calculates the phase φ _i (t) at time t.

Where i represents the i-th region of the brain divided by the preprocessor, t represents time, and Im _i represents the imaginary part of the average signal source estimated through inversion in the i-th region, and Re _i represents by the real part of the average signal source estimated through inversion in the i region) Real-time EEG connectivity monitoring system, characterized in that the calculation using. The method of claim 13, The connectivity calculation unit pairs randomly two (two regions) brain regions separated by the preprocessor, and calculates connectivity using the phase value. A real-time EEG connectivity monitoring system, characterized in that if the phase difference between the two areas is less than or equal to the preset threshold, the connectivity is defined, otherwise the connectivity is defined as no connectivity. The method of claim 3, The connectivity calculation unit Real-time EEG connectivity monitoring system, characterized in that the connectivity between the cerebral cortical region is calculated using one of the calculation method using the coherence, mean phase coherence, correlation. Before starting the EEG measurement, set the threshold value used in the calculation of connectivity, the frequency band to be observed, and the brain region. An initialization step of receiving; After the initialization step, the EEG measuring step of detecting the EEG of the subject; An inverse operator generation step of generating an inverse operator through Equation 1 using the position of the brain electrode position and the cerebral cortex model of the subject received in the initialization step; A cortical mapping step of converting the EEG signal of the scalp surface detected in the EEG measurement step into a signal of the brain cortical surface using the inverse operator generated in the inverse operator generation step; A fast Fourier transform step of FFT processing the EEG measured in the EEG measurement step; The phase difference is calculated using the EEG signal converted into the signal in the frequency domain in the fast Fourier transform step and the inverse operator generated in the inverse operator generation step, and whether connectivity is determined based on the threshold value set in the input setting step. Determining a connectivity calculation step; Real-time EEG connectivity monitoring method comprising the. The method of claim 16, A connectivity visualization step of visualizing connectivity between areas of the brain cortex calculated as having connectivity in the connectivity calculation step through a continuous line between each area in real time; A data display and storage step of displaying the data visualized in the connectivity visualization step on a display and storing the data in a storage unit; A system termination determination step of determining whether a termination command is input, ending if a termination command is input, and returning to the brain wave measurement step if the termination command is not input; Real-time EEG connectivity monitoring method characterized in that it further comprises.

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