CN113397559B - Stereotactic electroencephalogram analysis method, device, computer equipment and storage medium - Google Patents

Abstract

The application discloses a stereotactic electroencephalogram analysis method, a stereotactic electroencephalogram analysis device, computer equipment and a storage medium, wherein the stereotactic electroencephalogram analysis method comprises the following steps: acquiring original data of a stereotactic electroencephalogram, preprocessing the original data, and extracting to obtain an Epoch signal; performing time-frequency analysis on the Epoch signals by using a multi-window method or a wavelet algorithm, and performing connectivity analysis on electrodes corresponding to the Epoch signals by using a connectivity analysis method; and combining a time-frequency analysis result and a connectivity analysis result, and visualizing the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model to acquire the information of the brain region where the electrode is positioned. According to the application, through preprocessing the original data of the stereotactic electroencephalogram, performing time-frequency analysis and connectivity analysis on the extracted Epoch signals, and performing visual display on the analysis result, the analysis efficiency of the stereotactic electroencephalogram can be effectively improved, and the exploration of brain cognitive functions by using the stereotactic electroencephalogram can be facilitated.

Description

Stereotactic electroencephalogram analysis method, device, computer equipment and storage medium

technical field

The invention relates to the technical field of EEG detection, in particular to a stereotactic EEG analysis method, device, computer equipment and storage medium.

Background technique

In order to accurately locate the epileptogenic focus, patients with intractable epilepsy usually need to implant electrodes into the deep part of the brain—stereotactic electroencephalography (SEEG) to obtain spatial information inside the brain. Brings great convenience. However, at present, for the processing and analysis of cognitive SEEG data, it is usually necessary to write a large amount of code or use a variety of different methods to analyze SEEG, resulting in low analysis efficiency, and due to the use of a variety of different methods, this will lead to subsequent research work. more inconvenience.

Contents of the invention

Embodiments of the present invention provide a stereotactic electroencephalogram analysis method, device, computer equipment, and storage medium, aiming at improving the analysis efficiency of stereotactic electroencephalogram.

In a first aspect, an embodiment of the present invention provides a stereotaxic EEG analysis method, comprising:

Obtaining the raw data of the stereotaxic EEG, and preprocessing the raw data, extracting the Epoch signal;

Use the multi-window method or wavelet algorithm to analyze the time-frequency of the Epoch signal, and use the connectivity analysis method to analyze the connectivity of the electrodes corresponding to the Epoch signal;

Combining the results of time-frequency analysis and connectivity analysis, the position information of stereotaxic EEG was visualized through the pre-built 3D model of the brain to obtain the information of the brain area where the electrodes were located.

In a second aspect, an embodiment of the present invention provides a stereotaxic EEG analysis device, comprising:

A data preprocessing unit is used to obtain the raw data of the stereotaxic electroencephalogram, and preprocess the raw data to extract the Epoch signal;

The signal analysis unit is used to perform time-frequency analysis on the Epoch signal using a multi-window method or wavelet algorithm, and to perform connectivity analysis on electrodes corresponding to the Epoch signal through a connectivity analysis method;

The first visualization processing unit is used to combine the time-frequency analysis results and the connectivity analysis results to visualize the position information of the stereotaxic EEG through the pre-built three-dimensional model of the brain, so as to obtain the information of the brain area where the electrodes are located.

In a third aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the computer program, Realize the stereotaxic EEG analysis method as described in the first aspect.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the stereotaxic as described in the first aspect is realized. EEG analysis methods.

An embodiment of the present invention provides a stereotactic electroencephalogram analysis method, device, computer equipment, and storage medium, the method comprising: obtaining the original data of the stereotactic electroencephalogram, and preprocessing the original data, extracting and obtaining Epoch signal; use multi-window method or wavelet algorithm to conduct time-frequency analysis on Epoch signal, and conduct connectivity analysis on electrodes corresponding to Epoch signal through connectivity analysis method; combine time-frequency analysis results and connectivity analysis results, through pre-built The three-dimensional model of the brain visualizes the location information of the stereotaxic EEG to obtain the information of the brain area where the electrodes are located. The embodiment of the present invention preprocesses the raw data of the stereotaxic EEG, and performs data analysis on the Epoch signal extracted after the preprocessing, including frequency analysis and connectivity analysis, and visually displays the analysis results at the same time, which can effectively improve the For the analysis efficiency of stereotactic EEG, it can also bring convenience to the exploration of human brain cognitive function using stereotactic EEG.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. Ordinary technicians can also obtain other drawings based on these drawings on the premise of not paying creative work.

Fig. 1 is a schematic flow chart of a stereotaxic electroencephalogram analysis method provided by an embodiment of the present invention;

Fig. 2 is a schematic subflow diagram of a stereotaxic EEG analysis method provided by an embodiment of the present invention;

3 is a schematic diagram of another sub-flow of a stereotaxic EEG analysis method provided by an embodiment of the present invention;

4 is a schematic structural diagram of a brain template in a stereotaxic EEG analysis method provided by an embodiment of the present invention;

FIG. 5 is a schematic block diagram of a stereotactic EEG analysis device provided by an embodiment of the present invention;

FIG. 6 is a sub-schematic block diagram of a stereotactic EEG analysis method provided by an embodiment of the present invention;

FIG. 7 is another sub-schematic block diagram of a stereotaxic EEG analysis method provided by an embodiment of the present invention.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

It should be understood that when used in this specification and the appended claims, the terms "comprising" and "comprises" indicate the presence of described features, integers, steps, operations, elements and/or components, but do not exclude one or Presence or addition of multiple other features, integers, steps, operations, elements, components and/or collections thereof.

It should also be understood that the terminology used in the description of the present invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the present invention. As used in this specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural referents unless the context clearly dictates otherwise.

It should also be further understood that the term "and/or" used in the description of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations .

Please refer to FIG. 1 below. FIG. 1 is a schematic flowchart of a stereotaxic EEG analysis method provided by an embodiment of the present invention, which specifically includes steps S101 to S103.

S101. Obtain the raw data of the stereotaxic EEG, and preprocess the raw data to extract the Epoch signal;

S102. Perform time-frequency analysis on the Epoch signal by using a multi-window method or wavelet algorithm, and perform connectivity analysis on electrodes corresponding to the Epoch signal by a connectivity analysis method;

S103. Combining the time-frequency analysis results and the connectivity analysis results, visualize the position information of the stereotaxic EEG through the pre-built three-dimensional model of the brain, so as to obtain the information of the brain area where the electrodes are located.

In the present embodiment, at first the raw data of stereotaxic electroencephalogram (SEEG) that obtains is preprocessed, to extract and obtain corresponding Epoch signal (being time window signal), then carry out time-frequency analysis and connection to described Epoch signal Sexual analysis to explore the power of each frequency component in the Epoch signal over time, and determine the frequency band with stronger energy, and obtain the correlation/coherence between the electrodes, so as to obtain the correlation between the various brain regions / coherence. At the same time, the three-dimensional model of the brain is used to visualize the position information of SEEG to obtain the information of the brain area where the electrodes are located.

This embodiment preprocesses the SEEG raw data, and performs data analysis on the Epoch signals extracted after preprocessing, including frequency analysis and connectivity analysis, and visually displays the analysis results at the same time, which can effectively improve the stereotactic electroencephalogram. It can also bring convenience to the exploration of human brain cognitive function using stereotaxic EEG.

In an embodiment, as shown in FIG. 2 , the step S101 includes: steps S201-S206.

S201. Visualize the raw data by using a visualization function;

In this step, the SEEG data display function of the MNE library is invoked to obtain the SEEG raw data currently selected by the user, which is input into the MNE library as a parameter, and the visual display effect can be obtained by using the visualization function therein.

S202. Obtain the target frequency band of the user, and perform resampling processing on the visualized original data according to N times of the target frequency band;

In this step, due to the high sampling rate of the clinically obtained SEEG signal channel, the common sampling rate can reach 2000 Hz, resulting in large data volume, large storage space, and large amount of calculation and analysis time for subsequent data analysis. long. In this step, according to the Nyquist sampling theorem, it is only necessary to ensure that the data sampling rate is greater than N times the target frequency band, for example, greater than twice the maximum value of the target frequency band. After such resampling, the amount of data can be greatly reduced , reducing the computational cost. For example, to obtain a new sampling rate set by the user, such as 1000 Hz, if the sampling rate of the original signal is 2000 Hz, the original signal is sampled at intervals of 2.

S203. Use an FIR filter or an IIR filter to filter the resampled original data;

Since the signal acquisition process will be affected by noise, such as power frequency noise, it is necessary to filter the data. For the SEEG signal, due to its high signal-to-noise ratio, it is usually only necessary to filter out the power frequency interference, and then extract the signal of the frequency band of interest (ie, the target frequency band). In this step, FIR and IIR filters are respectively provided for signal filtering. Among them, the FIR filter directly calls the firwin function of the signal module in the scipy library, and the SEEG raw data and cutoff frequency are input into this function as parameters to perform FIR filtering. The window uses the default Hamming window, and the filter length is the shortest transition frequency band. The bandwidth is 6.6 times, and the positive and negative two-way filter design makes the filter delay compensated to realize the zero-phase filtering of the signal; the default IIR filter is the fourth-order Butterworth filter. The specific workflow of the FIR filter is as follows: obtain the cutoff frequency; obtain the transition frequency band bandwidth f through the cutoff frequency; set the Fourier transform length to 6.6f; filter the original data.

S204. Obtain the target channel name marked by the user in the raw data, and clear the channel signal corresponding to the target channel name in the filtered raw data;

This step is aimed at removing bad leads, that is, channels with poor signal quality. Specifically, the target channel name marked by the user is obtained first, and the target channel name can be stored in a list at the same time. When it is necessary to remove the bad guide, the corresponding target channel name is obtained from the list, and then the relevant target channel is cleared. name signal. At the same time, the original data of a new Raw type is reconstructed.

S205. Use a re-reference algorithm to perform re-reference processing on the original data of the clear channel signal to obtain target data; wherein, the re-reference algorithm includes a CAR algorithm, a GWR algorithm, an ESR algorithm, a Bipolar algorithm, a Monopolar algorithm, and a Laplacian algorithm;

Re-referencing the signal can reduce the correlation with the reference electrode, highlight the difference, and avoid the effective components of the task-related signal being obliterated due to the strong correlation between channels. Six re-referencing algorithms are provided in this section: CAR, GWR, ESR, Bipolar, Monopolar, and Laplacian. in particular:

The CAR algorithm is to take the average of all channels as a reference;

The GWR algorithm needs to import the MNI coordinate information of the electrode in advance (take the .txt file as an example). By calling the method module of Visbrain to partition the electrode, distinguish the gray and white matter information of the electrode, and take the mean value of all gray matter signals and the mean value of white matter for the gray matter and white matter electrodes respectively. As a reference, save the calculated re-reference signal;

The ESR algorithm takes the average of all signals on the same electrode needle as a reference. In SEEG data, electrodes on the same electrode needle are usually prefixed with the same letter as the electrode name, so that the signal can be divided according to the electrode needle, and re-referencing can be realized;

The Bipolar algorithm uses the electrode signal of the previous potential of the same electrode needle as a reference to make a difference;

The Monopolar algorithm takes the average of the two white matter electrode signals as a reference, and the reference electrode can be selected by yourself;

The Laplacian algorithm uses the average value of the front and back electrodes on the same electrode needle as a reference to make a difference.

S206. Extract an Epoch signal from the target data by using the pre-generated event code.

Task-state SEEG in cognitive experiments often involves event triggering. During the cognitive experiment, the device (such as a notebook) will automatically mark the stimulus when the stimulus is generated according to the experimental program, that is, generate the event code. When analyzing the data later, it is necessary to extract the corresponding data segment composition according to the event code. Epoch signal, so as to analyze the Epoch signal within the time period of the stimulus event.

In this embodiment, the purpose of performing signal cleaning and extraction on the original data is achieved by sequentially performing visualization processing, re-sampling processing, filtering processing, and re-referencing outlets on the original data. Meanwhile, the data formats supported by this embodiment include EDF, SET, and FIF.

In one embodiment, the step S206 includes:

Set the corresponding time information for the pre-generated event code;

Intercepting a corresponding target data segment from the target data in combination with the time information;

Based on the performance of the subjects during the acquisition of stereotaxic EEG signals, the baseline time period is set;

Taking the mean value of the signal amplitude in the baseline time period as a reference, the signal obtained by subtracting the reference from the target data segment is used as the Epoch signal.

Task-state SEEG in cognitive experiments often involves event triggering. During the cognitive experiment, the device (such as a notebook) will automatically mark the stimulus when the stimulus is generated according to the experimental program, that is, generate the event code. When analyzing the data later, it is necessary to extract the corresponding data segment composition according to the event code. Epoch signal, so as to analyze the Epoch signal within the time period of the stimulus event.

In this embodiment, when extracting the Epoch signal, first set the time window relative to the event code, such as (-0.5, 2), and obtain the time information, and then intercept the corresponding target data segment according to the time information, such as intercepting the event The target data segment from 0.5 seconds before the code to the next two seconds. Then, because the SEEG signal acquisition process will cause a certain baseline drift due to the subject's breathing, limb movement and hardware reasons, the baseline time period is set, and the average value of the signal amplitude within the baseline time period is used as the benchmark , subtracting the reference from the intercepted signal in the target data segment, reducing signal interference, and obtaining the Epoch signal.

In an embodiment, as shown in FIG. 3 , the step S102 includes: steps S301-S303.

S301. Using an event-related potential to remove noise, highlight or stimulation signal in the Epoch signal;

S302. Calculate the power spectral density of each channel in the Epoch signal by Welch wavelet method or multi-window method;

S303. Perform summing and averaging processing on the power spectral densities of all channels to obtain the total power spectral density of the Epoch signal.

Time-frequency analysis is a common analysis method for SEEG signals. This embodiment provides functions such as Event-Related Potential (ERP), ERP Image, time-frequency response, and power spectral density.

Event-related potential is the most common method in EEG analysis. This method superimposes and averages all data segments of the same event code to remove noise and highlight signals related to experimental stimuli, so as to explore what kind of stimulation the stimulus has on the brain. Influence. When calculating the power spectral density of Epoch signals, the Welch wavelet and Multitaper methods are used for calculation, and the power spectral densities of all channels of all Epochs are added and averaged to obtain the total power spectral density of all signals.

The time-frequency response can explore the power variation process of each frequency component with time, and can help to see which or which frequency bands have stronger energy. When calculating the time-frequency response, the inter-trial coherence can be calculated at the same time, which is a measure of the phase consistency between each data segment of the Epoch signal. In this embodiment, the Multitaper, Morlet wavelet or Stockwell method of the MNE library is used to calculate the time-frequency response, which method can be determined according to the actual situation.

In an embodiment, the step S102 further includes:

Calculate the frequency domain information of the Epoch signal by a connectivity analysis method;

Based on the frequency domain information, averaging all the connectivity of the frequency domain information corresponding to the target frequency band selected by the user to obtain the average connection strength of the target frequency band;

And based on the frequency domain information, utilize short-time Fourier transform to perform sliding window processing to obtain the connectivity results of each time point of the Epoch signal; or perform Fourier transform to the Epoch signal to obtain the Epoch signal Corresponding average connectivity analysis results over the entire time range.

In this embodiment, the processing for frequency bands may be divided into frequency band averaging or non-averaging. Frequency band averaging refers to averaging all connectivity in the frequency range selected by the user (ie, the target frequency band) to obtain a result representing the average connection strength between channel x and channel y within the frequency range. The time processing can be divided into sliding window and non-sliding window. If it is necessary to observe the change trend of connectivity over time, the short-time Fourier transform is used to perform sliding window processing on the Epoch signal, and the connectivity results at each time point are calculated. Otherwise, Fourier transform is performed on the entire Epoch signal to obtain the average connectivity analysis results in the entire time range.

The SEEG signal goes deep into the brain area to obtain the information of the deep brain area, and the connection analysis of each electrode of SEEG can obtain the correlation/coherence between each electrode, so as to obtain the correlation/coherence between each brain area. In this embodiment, the MNE library and the spectral_connectivity library are used to calculate the connectivity between electrodes. And provides seven connectivity analysis methods: Coherence, Imaginary Coherence, Phase-Lag Index, Weighted PLI, Unbiased Squared PLI, Unbiased Squared WPLI and Phase Log Value, each method can use Multitaper or Morlet for frequency domain information calculation. The calculation formula of each method is as follows:

Coherence:

Imaginary Coherence:

Phase-Lag Index:

C＝|E(sign(Im(S _xy )))|

Weighted Phase-Lag Index:

Unbiased Squared PLI:

Unbiased Squared WPLI:

Phase Log Value:

C＝|E(S _xy /|S _xy |)|

Among them, C represents frequency domain information, S represents the cross-spectral density of channel x and channel y, and E represents expectation. Through the above-mentioned connectivity analysis method, the connectivity is calculated for the selected channels of each data segment in the Epoch signal, and finally the results of all the data segments are averaged to obtain the final result.

In one embodiment, the step S103 includes:

Create a canvas for displaying a 3D model of the brain;

When the data file of the brain template is obtained, the brain template object is constructed according to the data file, and the construction result is displayed on the canvas;

Obtaining the electrode coordinate file of the subject, grouping the electrodes according to the electrode coordinate file to obtain the electrode axis information to which each electrode belongs, and performing color rendering on each electrode according to the motor axis information;

Obtain the brain region of interest selected by the user and the coordinate information corresponding to the brain region of interest, and perform color rendering on the brain region of interest according to the coordinate information.

In this embodiment, referring to FIG. 4 , considering that different research contents may require different brain templates for display, this embodiment provides five common brain templates of B1, B2, B3, inflated and white. First, a VisBrainCanvas canvas is automatically created so that the canvas can display various 3D models, then the name of the currently selected brain template is obtained, and the corresponding data file is read, and a BrainObj object is constructed and added to the VisBrainCanvas for display. After obtaining the electrode coordinate file (such as .txt format) of the subject, the electrodes are grouped according to the electrode name, and the electrode axis information to which each electrode belongs is obtained, and then the color provided by Matplotlib is used for rendering based on the motor axis information, so as to achieve The electrodes perform the distinguishing function. At the same time, after obtaining the ROI brain region selected by the user (that is, the brain region of interest), the coordinate information corresponding to the corresponding brain region can be read from the nii.gz file, and the data points corresponding to the obtained coordinate information Rendered in the same color to show this brain region. Of course, if there are multiple brain regions of interest, random sampling can be used to randomly extract different colors from the color scheme provided in the Matplotlib library for rendering, so as to achieve the effect of distinguishing brain regions.

SEEG electrodes penetrate deep into the brain of the subject, and its spatial position information is the biggest advantage of SEEG technology for cognitive neuroscience research. In order to facilitate users to view SEEG location information. The embodiment of the present invention designs the display function of the three-dimensional model of the brain, and calls the VisBrainCanvas, BrainObj, SourceObj and RoiObj modules of Visbrain to realize the brain template, SEEG electrode visualization and ROI visualization.

In an embodiment, the step S103 also includes:

Use the preset segmentation template to divide the brain template into brain regions;

When acquiring the electrode axis information to which each electrode belongs and/or the coordinate information corresponding to the brain region of interest, color rendering is performed based on the divided brain regions.

In this embodiment, the brain template is divided into brain regions through the preset segmentation template. When color rendering is performed on each electrode or the brain region of interest, corresponding rendering can be performed according to the divided brain regions, which can improve rendering efficiency. As well as the accuracy of rendering, avoid indiscriminate impact on irrelevant brain regions during rendering.

This embodiment provides three segmentation templates of Talairach, AAL and Brodmann, each of which divides the brain into different brain regions. Among them, the Talairach segmentation template was developed by Jean Talairach and is the earliest 3D brain template used. This template is the first to define the origin of space coordinates at the junction of the AC-PC line and the midline sagittal plane, the left and right directions are the X axis, and the front and back The direction is the Y axis, and the up and down direction is the space coordinate system of the Z axis. The full name of AAL is AnatomicalAutomatic Labeling, which is provided by the Montreal Neurological Institute (MNI). The AAL template has a total of 116 regions, 90 of which belong to the brain, and the remaining 26 belong to the cerebellum. The Brodmann segmentation template is a system that divides the cerebral cortex into a series of anatomical regions based on cellularity, which in neuroanatomy refers to the organization of neurons as observed in stained brain tissue. The Brodmann division consists of 52 regions in each hemisphere, some of which have been subdivided today, e.g. region 23 is divided into regions 23a and 23b, etc. After the MNI coordinates of the electrodes are imported, the position of the brain region where the electrode is located can be obtained by comparing the coordinate information of each brain region of the segmentation template with the electrode coordinate information.

FIG. 5 is a schematic block diagram of a stereotaxic EEG analysis device 500 provided by an embodiment of the present invention. The device 500 includes:

The data preprocessing unit 501 is used to obtain the raw data of the stereotactic EEG, and preprocess the raw data to extract the Epoch signal;

Signal analysis unit 502, for utilizing multi-window method or wavelet algorithm to carry out time-frequency analysis to Epoch signal, and carry out connectivity analysis to the electrode corresponding to Epoch signal by connectivity analysis method;

The first visualization processing unit 503 is configured to combine the results of the time-frequency analysis and the results of the connectivity analysis to visualize the position information of the stereotaxic EEG through the pre-built three-dimensional model of the brain, so as to obtain the information of the brain region where the electrodes are located.

In one embodiment, as shown in FIG. 6, the data preprocessing unit 501 includes:

The second visualization processing unit 601 is configured to perform visualization processing on the raw data by using a visualization function;

A resampling processing unit 602, configured to acquire the user's target frequency band, and perform resampling processing on the visualized original data according to N times the target frequency band;

A filter processing unit 603, configured to perform filter processing on the resampled original data by using an FIR filter or an IIR filter;

Remove the bad guide unit 604, for obtaining the target channel name marked by the user in the raw data, and clear the channel signal corresponding to the target channel name in the filtered raw data;

The re-reference processing unit 605 is used to perform re-reference processing on the original data of the clear channel signal by using a re-reference algorithm to obtain target data; wherein, the re-reference algorithm includes CAR algorithm, GWR algorithm, ESR algorithm, Bipolar algorithm, Monopolar algorithm and the Laplacian algorithm;

A signal extracting unit 606, configured to extract an Epoch signal from the target data through a pre-generated event code.

In one embodiment, the signal extraction unit 606 includes:

A time information setting unit, configured to set corresponding time information for pre-generated event codes;

A data segment interception unit, configured to intercept a corresponding target data segment of the target data in combination with the time information;

The time period setting unit is used to set the baseline time period based on the performance of the subject during the stereotaxic EEG signal acquisition process;

The data segment processing unit is configured to use the mean value of the signal amplitude in the baseline time period as a reference, and use the signal obtained by subtracting the reference from the target data segment as the Epoch signal.

In one embodiment, as shown in FIG. 7, the signal analysis unit 502 includes:

A signal processing unit 701, configured to use an event-related potential to remove noise, highlights or stimulation signals in the Epoch signal;

A power spectral density calculation unit 702, configured to calculate the power spectral density of each channel in the Epoch signal by a Welch wavelet method or a multi-window method;

The adding and averaging unit 703 is configured to add and average the power spectral densities of all channels to obtain the total power spectral density of the Epoch signal.

In an embodiment, the signal analysis unit 502 further includes:

A frequency domain information calculation unit, used to calculate the frequency domain information of the Epoch signal by a connectivity analysis method;

A connectivity averaging unit, configured to average all the connectivity of the frequency domain information corresponding to the target frequency band selected by the user based on the frequency domain information, to obtain the average connection strength of the target frequency band;

The Fourier transform unit is used for and based on the frequency domain information, utilizes the short-time Fourier transform to perform sliding window processing, and obtains the connectivity results of each time point of the Epoch signal; or performs Fourier on the Epoch signal leaf transformation, to obtain the average connectivity analysis result in the whole time range corresponding to the Epoch signal.

In an embodiment, the first visualization processing unit 503 includes:

a canvas creating unit, configured to create a canvas for displaying a three-dimensional model of the brain;

The object construction unit is used to construct the brain template object according to the data file when the data file of the brain template is obtained, and display the construction result on the canvas;

The first rendering unit is configured to obtain the electrode coordinate file of the subject, group the electrodes according to the electrode coordinate file to obtain the electrode axis information to which each electrode belongs, and perform color rendering on each electrode according to the motor axis information;

The second rendering unit is configured to acquire the brain region of interest selected by the user and coordinate information corresponding to the brain region of interest, and perform color rendering on the brain region of interest according to the coordinate information.

In an embodiment, the first visualization processing unit 503 further includes:

A brain region division unit, configured to divide the brain template into brain regions using a preset segmentation template;

The third rendering unit is configured to perform color rendering based on the divided brain regions when obtaining the electrode axis information to which each electrode belongs and/or the coordinate information corresponding to the brain region of interest.

Since the embodiment of the device part corresponds to the embodiment of the method part, please refer to the description of the embodiment of the method part for the embodiment of the device part, and details will not be repeated here.

An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the steps provided in the above-mentioned embodiments can be realized. The storage medium may include various media capable of storing program codes such as a U disk, a removable hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk.

An embodiment of the present invention also provides a computer device, which may include a memory and a processor. A computer program is stored in the memory. When the processor invokes the computer program in the memory, the steps provided in the above embodiments can be implemented. Of course, the computer equipment may also include components such as various network interfaces and power supplies.

Each embodiment in the description is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to each other. As for the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for the related information, please refer to the description of the method part. It should be pointed out that those skilled in the art can make some improvements and modifications to the application without departing from the principles of the application, and these improvements and modifications also fall within the protection scope of the claims of the application.

It should also be noted that in this specification, relative terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations There is no such actual relationship or order between the operations. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

Claims (6)

1. A stereotactic electroencephalogram analysis method, characterized in that, comprising: Obtaining the raw data of the stereotaxic EEG, and preprocessing the raw data, extracting the Epoch signal; The raw data of obtaining the stereotaxic EEG, and preprocessing the raw data, extracting the Epoch signal, including: Visualize the raw data by using a visualization function; Obtain the target frequency band of the user, and perform resampling processing on the visualized original data according to N times of the target frequency band; The original data after resampling processing is filtered by FIR filter or IIR filter; Obtain the target channel name marked by the user in the raw data, and clear the channel signal corresponding to the target channel name in the filtered raw data; Using a re-reference algorithm to perform re-reference processing on the original data of the clear channel signal to obtain target data; wherein the re-reference algorithm includes a CAR algorithm, a GWR algorithm, an ESR algorithm, a Bipolar algorithm, a Monopolar algorithm, and a Laplacian algorithm; Extracting an Epoch signal from the target data through a pre-generated event code; Use the multi-window method or wavelet algorithm to analyze the time-frequency of the Epoch signal, and use the connectivity analysis method to analyze the connectivity of the electrodes corresponding to the Epoch signal; Described utilizing multi-window method or wavelet algorithm to carry out time-frequency analysis to Epoch signal, comprising: Using event-related potentials to remove noise, highlights or stimulation signals in the Epoch signal; Calculate the power spectral density of each channel in the Epoch signal by Welch wavelet method or multi-window method; The power spectral densities of all channels are added and averaged to obtain the total power spectral density of the Epoch signal; Described carrying out connectivity analysis to the electrode corresponding to Epoch signal by connectivity analysis method, comprising: Calculate the frequency domain information of the Epoch signal by a connectivity analysis method; Based on the frequency domain information, averaging all the connectivity of the frequency domain information corresponding to the target frequency band selected by the user to obtain the average connection strength of the target frequency band; And based on the frequency domain information, utilize short-time Fourier transform to perform sliding window processing to obtain the connectivity results of each time point of the Epoch signal; or perform Fourier transform to the Epoch signal to obtain the Epoch signal The corresponding average connectivity analysis results over the entire time range; Combining the results of time-frequency analysis and connectivity analysis, the position information of stereotaxic EEG is visualized through the pre-built three-dimensional model of the brain to obtain the information of the brain area where the electrodes are located; Combining the time-frequency analysis results and connectivity analysis results, the position information of the stereotaxic EEG is visualized through the pre-built three-dimensional model of the brain, so as to obtain the information of the brain region where the electrodes are located, including: Create a canvas for displaying a 3D model of the brain; When the data file of the brain template is obtained, the brain template object is constructed according to the data file, and the construction result is displayed on the canvas; Obtaining the electrode coordinate file of the subject, grouping the electrodes according to the electrode coordinate file to obtain the electrode axis information to which each electrode belongs, and performing color rendering on each electrode according to the electrode axis information; Obtain the brain region of interest selected by the user and the coordinate information corresponding to the brain region of interest, and perform color rendering on the brain region of interest according to the coordinate information. 2. stereotactic electroencephalogram analysis method according to claim 1, is characterized in that, described target data is extracted Epoch signal by the event code that generates in advance, comprises: Set the corresponding time information for the pre-generated event code; Intercepting a corresponding target data segment from the target data in combination with the time information; Based on the performance of the subjects during the acquisition of stereotaxic EEG signals, the baseline time period is set; Taking the mean value of the signal amplitude in the baseline time period as a reference, the signal obtained by subtracting the reference from the target data segment is used as the Epoch signal. 3. the stereotactic electroencephalogram analysis method according to claim 1, is characterized in that, described in conjunction with time-frequency analysis result and connectivity analysis result, the positional information of stereotactic electroencephalogram by pre-built three-dimensional model of brain For visualization, also include: Use the preset segmentation template to divide the brain template into brain regions; When acquiring the electrode axis information to which each electrode belongs and/or the coordinate information corresponding to the brain region of interest, color rendering is performed based on the divided brain regions. 4. A stereotactic EEG analysis device, characterized in that it comprises: A data preprocessing unit is used to obtain the raw data of the stereotaxic electroencephalogram, and preprocess the raw data to extract the Epoch signal; The data preprocessing unit includes: A second visualization processing unit, configured to perform visualization processing on the raw data by using a visualization function; A resampling processing unit, configured to acquire the target frequency band of the user, and perform resampling processing on the visualized original data according to N times of the target frequency band; A filter processing unit, configured to filter the resampled original data by using an FIR filter or an IIR filter; removing the bad guide unit, which is used to obtain the target channel name marked by the user in the original data, and clear the channel signal corresponding to the target channel name in the filtered original data; A re-reference processing unit, configured to use a re-reference algorithm to perform re-reference processing on the original data of the clear channel signal to obtain target data; wherein the re-reference algorithm includes a CAR algorithm, a GWR algorithm, an ESR algorithm, a Bipolar algorithm, a Monopolar algorithm, and Laplacian algorithm; A signal extraction unit, configured to extract an Epoch signal from the target data through a pre-generated event code; The signal analysis unit is used to perform time-frequency analysis on the Epoch signal using a multi-window method or wavelet algorithm, and to perform connectivity analysis on electrodes corresponding to the Epoch signal through a connectivity analysis method; The signal analysis unit includes: A signal processing unit for removing noise, highlights or stimulation signals in the Epoch signal by using an event-related potential; A power spectral density calculation unit, used to calculate the power spectral density of each channel in the Epoch signal by Welch wavelet method or multi-window method; Adding and averaging unit, used to add and average the power spectral densities of all channels to obtain the total power spectral density of the Epoch signal; The signal analysis unit also includes: A frequency domain information calculation unit, used to calculate the frequency domain information of the Epoch signal by a connectivity analysis method; A connectivity averaging unit, configured to average all the connectivity of the frequency domain information corresponding to the target frequency band selected by the user based on the frequency domain information, to obtain the average connection strength of the target frequency band; The Fourier transform unit is used for and based on the frequency domain information, utilizes the short-time Fourier transform to perform sliding window processing, and obtains the connectivity results of each time point of the Epoch signal; or performs Fourier on the Epoch signal Leaf transformation, the average connectivity analysis result in the whole time range corresponding to the Epoch signal is obtained; The first visualization processing unit is used to combine the time-frequency analysis results and the connectivity analysis results to visualize the position information of the stereotaxic EEG through the pre-built three-dimensional model of the brain, so as to obtain the information of the brain area where the electrodes are located; The first visual processing unit includes: a canvas creating unit, configured to create a canvas for displaying a three-dimensional model of the brain; The object construction unit is used to construct the brain template object according to the data file when the data file of the brain template is obtained, and display the construction result on the canvas; The first rendering unit is configured to obtain the electrode coordinate file of the subject, group the electrodes according to the electrode coordinate file to obtain the electrode axis information to which each electrode belongs, and perform color rendering on each electrode according to the electrode axis information; The second rendering unit is configured to acquire the brain region of interest selected by the user and coordinate information corresponding to the brain region of interest, and perform color rendering on the brain region of interest according to the coordinate information. 5. A computer device, characterized in that it comprises a memory, a processor, and a computer program stored on the memory and operable on the processor, and when the processor executes the computer program, the computer program according to the claim is realized. The stereotaxic EEG analysis method described in any one of 1 to 3. 6. A computer-readable storage medium, characterized in that, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the stereoscopic display according to any one of claims 1 to 3 is realized. Directional EEG Analysis Methods.