CN116541751B - Electroencephalogram signal classification method based on brain function connection network characteristics - Google Patents
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Abstract
The application provides an electroencephalogram signal classification method based on brain function connection network characteristics, which comprises the following steps of: s1, acquiring motor imagery electroencephalogram signals; s2, preprocessing the acquired motor imagery electroencephalogram signals, and dividing the acquired motor imagery electroencephalogram signals into training set data and test set data; s3, selecting an important electroencephalogram channel in the motor imagery process through mutual information characteristics and network centrality of a brain connection network; s4, extracting the data which accords with the important electroencephalogram channel selected in the step S3 from the preprocessed training set data and the preprocessed test set data by using a co-space mode method; s5, training an SVM model through the training set characteristic data. The application has the beneficial effects that: the electroencephalogram data is subjected to channel selection and vector model training, an important region of brain activity in the tested motor imagery process is found, and the electroencephalogram channel signals of the region are used for carrying out on-line recognition on multiple motor imagery electroencephalogram signals and classifying the motor imagery electroencephalogram signals.
Description
Technical Field
The application belongs to the technical field of brain-computer interfaces, and particularly relates to an electroencephalogram signal classification method based on brain function connection network characteristics.
Background
The motor imagery technique is developed by researching event related desynchronization and event related synchronization phenomena, and the core of the technique is to research potential activities of the brain in imagery rather than actual movements, motor imagery brain electrical signals can reflect the activities of the brain in imagery, different motor imagery tasks can trigger responses of different brain cortex areas, and after the motor imagery brain electrical signals are classified, a computer can generate control signals for a brain-computer interface system. The motor imagery technique has been widely used in medical fields such as rehabilitation, and the motor imagery signal can be converted into a control signal through a motor imagery brain-computer interface system, so that control of external equipment is realized, for example, the motor imagery technique is used for limb motor recovery in rehabilitation, and the motor imagery signal is generated by training a patient in imagery movement and then is converted into a limb motor control signal, so that the patient is helped to recover limb movement capability. Motor imagery techniques may also be applied in the field of pain management, etc., by alleviating pain sensation by imagining pain.
The brain connection network refers to the connection relation between different areas in the brain, can be analyzed and researched by using graph theory and network science methods, is generally constructed and analyzed based on brain imaging technologies, such as functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and Magnetoencephalography (MEG), and the like, and can be used for deeply knowing the structure and function of the brain and exploring the change and influence of the brain connection network under different diseases and cognitive states through the analysis of the brain connection network. With the continuous development and perfection of MI technology, the application prospect in the medical field is wider. However, the identification of the motor imagery task and the multiple motor imagery electroencephalogram signals of the patient on line at present has become a problem to be solved in the design of rehabilitation robots.
Disclosure of Invention
In view of the above, the present application aims to provide an electroencephalogram signal classification method based on brain function connection network characteristics, so as to solve at least one of the above-mentioned technical problems.
In order to achieve the above purpose, the technical scheme of the application is realized as follows:
the application provides an electroencephalogram signal classification method based on brain function connection network characteristics, which comprises the following steps of:
s1, acquiring motor imagery electroencephalogram signals;
s2, preprocessing the acquired motor imagery electroencephalogram signals, and dividing the acquired motor imagery electroencephalogram signals into training set data and test set data;
s3, selecting an important electroencephalogram channel in the motor imagery process through mutual information characteristics and network centrality of a brain connection network;
s4, extracting the preprocessed training set data and the data conforming to the important electroencephalogram channels selected in the step S3 from the test set data by using a co-space mode method, and generating brain training set feature data and training set feature data;
s5, training an SVM model through the training set characteristic data, testing the SVM model by using the training set characteristic data, and selecting the SVM model with the highest accuracy as an electroencephalogram signal recognition model;
s6, acquiring motor imagery electroencephalogram signals of a plurality of channels of a patient;
s7, inputting the acquired motor imagery electroencephalogram signals into a motor imagery electroencephalogram signal identification model, and identifying motor imagery electroencephalogram signals.
Further, the step S2 includes the following steps:
s21, performing 8-30Hz filtering processing on the acquired motor imagery electroencephalogram signals;
s22, performing 250Hz downsampling on the data obtained in the step S21;
s23, carrying out baseline correction processing on the data obtained in the step S22;
s24, carrying out data segmentation processing according to time on the data obtained in the step S23, and extracting 0.5-2.5 seconds of data as analysis data;
s25, analyzing the data according to 7: the scale of 1 is divided into training set data and test set data.
Further, the step S3 includes the following steps:
s31, defining an electrode coverage area corresponding to each EEG lead as a node;
s32, calculating an entropy value of an electroencephalogram signal during motor imagery, wherein a calculation formula is as follows:
；
x and Y are two different lead brain electrical signals in a motor imagery period, p (X) is the probability of the signal value X, namely the edge distribution probability of the signal X, and H (X) is the entropy of the signal;
the mutual information between the two lead signals can be calculated by using the entropy values of the two signals, and the calculation formula is as follows:
；
wherein I (X, Y) is mutual information between two lead signals, and H (X, Y) is joint entropy of the signals X and Y;
the mutual information between the two lead signals is standardized, and the calculation formula is as follows:
；
NMI (X, Y) is the standardized mutual information of signals X and Y, and represents the synchronization between the signals X and Y;
s33, calculating standardized mutual information between two channels in each test time to obtain a motor imagery electroencephalogram signal standardized mutual information network;
s34, calculating the intermediacy centrality of all nodes of the electroencephalogram signal standardized mutual information network;
s35, counting channels with medium centrality values larger than 0 in all test orders, and performing descending order arrangement on the channels to generate channel importance ranking;
s36, sorting channel importance middle and frontThe individual channels serve as important electroencephalogram channels.
Further, the formula for calculating the center of the intermediary in step S34 is as follows:
；
s, t, i represent nodes in the network,representing the number of all shortest paths from node s to node t,BC representing the number of paths through node i in all shortest paths from node s to node t i BC representing the extent to which node i acts as a bridge in the network i The larger the value the higher the importance of node i in the network.
Further, in the step S1, a plurality of types of motor imagery electroencephalogram signals of 64 leads are acquired by using an electroencephalogram acquisition device.
Further, the step S5 includes the following steps:
s51, training an SVM model by using training set data feature vectors through a 5-fold cross validation method;
s52, testing the SVM model by using the feature vector of the test set, counting the number of correctly classified labels in the output action labels of the SVM model, and dividing the number by the total number of the labels to obtain the SVM model with the highest classification accuracy rate, and selecting the SVM model with the highest classification accuracy rate as the motor imagery electroencephalogram signal identification model.
A second aspect of the application provides an electronic device comprising a processor and a memory communicatively coupled to the processor for storing instructions executable by the processor for performing the method of the first aspect.
A third aspect of the application provides a server comprising at least one processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor to cause the at least one processor to perform the method of the first aspect.
A fourth aspect of the application provides a computer readable storage medium storing a computer program which when executed by a processor implements the method of the first aspect.
Compared with the prior art, the brain function connection network feature-based brain electrical signal classification method has the following beneficial effects:
(1) According to the electroencephalogram signal classification method based on the brain function connection network characteristics, the electroencephalogram data is subjected to channel selection to train a vector model, an important region of brain activity in a tested motor imagery process is found, and brain electric channel signals of the region are used for carrying out on-line recognition on multiple types of motor imagery electroencephalogram signals and classifying the motor imagery electroencephalogram signals.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
fig. 1 is a schematic flow chart of an electroencephalogram signal classification method based on brain function connection network characteristics according to an embodiment of the application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
The application will be described in detail below with reference to the drawings in connection with embodiments.
Embodiment one:
as shown in fig. 1, an electroencephalogram signal classification method based on brain function connection network characteristics includes the following steps:
s1, acquiring motor imagery electroencephalogram signals;
collecting 64-lead multi-type motor imagery electroencephalogram signals by using an electroencephalogram collecting device; setting the sampling rate of an electroencephalogram signal acquisition system to be 1000Hz, wearing a 64-channel electroencephalogram cap conforming to the international standard 10-20 lead system, and acquiring electroencephalogram signals of the whole test process by a patient according to motor imagination of a test prompt at a distance of 1m from a display.
S2, preprocessing the acquired motor imagery electroencephalogram signals, and dividing the acquired motor imagery electroencephalogram signals into training set data and test set data;
s3, selecting an important electroencephalogram channel in the motor imagery process through mutual information characteristics and network centrality of a brain connection network;
s4, extracting the preprocessed training set data and the data conforming to the important electroencephalogram channels selected in the step S3 from the test set data by using a co-space mode method, and generating brain training set feature data and training set feature data;
the specific operation steps are as follows:
and extracting motor imagery electroencephalogram characteristics by using a co-space mode method.
S41, preprocessing the EEG signal data, wherein each timeIn one test, extracting brain electrical data conforming to the brain electrical channel important in the step S3, and extracting the selected brain electrical data by using a CSP methodElectroencephalogram signal characteristics of the individual channels.
S42, selecting the brain electrical data of the brain electrical channel important in the step S3 for the brain electrical signal data of all test times, and extracting the selected brain electrical data by using a CSP methodElectroencephalogram signal characteristics of the individual channels.
S5, training an SVM model through the training set characteristic data, testing the SVM model by using the training set characteristic data, and selecting the SVM model with the highest accuracy as an electroencephalogram signal recognition model;
s6, acquiring motor imagery electroencephalogram signals of a plurality of channels of a patient;
s7, inputting the acquired motor imagery electroencephalogram signals into a motor imagery electroencephalogram signal identification model, and identifying motor imagery electroencephalogram signals.
The step S2 is as follows:
s21, performing 8-30Hz filtering processing on the acquired motor imagery electroencephalogram signals;
s22, performing 250Hz downsampling on the data obtained in the step S21;
s23, carrying out baseline correction processing on the data obtained in the step S22; taking the mean value of the brain electrical signals of 200ms before the test as a baseline, subtracting the baseline of each electrode from the brain electrical signals of each electrode obtained in the step S22, and obtaining the brain electrical signals after baseline correction.
S24, carrying out data segmentation processing according to time on the data obtained in the step S23, and extracting 0.5-2.5 seconds of data as analysis data;
s25, analyzing the data according to 7: the scale of 1 is divided into training set data and test set data.
The step S3 includes the steps of:
s31, defining an electrode coverage area corresponding to each EEG lead as a node;
s32, measuring the correlation between nodes by using standardized mutual information, wherein the mutual information is a method for measuring the degree of correlation between two random variables, and can be used for analyzing the correlation between different channels in an electroencephalogram signal and the correlation between the electroencephalogram signal and motor imagery in the field of brain-computer interfaces.
The entropy value of the electroencephalogram signal during the motor imagery is calculated, and the calculation formula is as follows:
；
x and Y are two different lead brain electrical signals in a motor imagery period, p (X) is the probability of the signal value X, namely the edge distribution probability of the signal X, and H (X) is the entropy of the signal;
the mutual information between the two lead signals can be calculated by using the entropy values of the two signals, and the calculation formula is as follows:
；
wherein I (X, Y) is mutual information between two lead signals, and H (X, Y) is joint entropy of the signals X and Y;
the mutual information between the two lead signals is standardized, and the calculation formula is as follows:
；
NMI (X, Y) is the standardized mutual information of signals X and Y, and represents the synchronization between the signals X and Y;
s33, calculating standardized mutual information between two channels in each test time to obtain a motor imagery electroencephalogram signal standardized mutual information network;
s34, calculating the intermediacy centrality of all nodes of the electroencephalogram signal standardized mutual information network; the mediating center of a node refers to the number or degree of information transfer between different nodes in a network, and the higher the mediating center of a node, the stronger the ability of the node to transfer information in the network, and the more important the operation of the whole network.
S35, counting channels with medium centrality values larger than 0 in all test orders, and performing descending order arrangement on the channels to generate channel importance ranking;
s36, sorting channel importance middle and frontThe individual channels serve as important electroencephalogram channels.
The formula for calculating the intermediacy in step S34 is as follows:
；
s, t, i represent nodes in the network,representing the number of all shortest paths from node s to node t,BC representing the number of paths through node i in all shortest paths from node s to node t i BC representing the extent to which node i acts as a bridge in the network i The larger the value the higher the importance of node i in the network.
In the step S1, the electroencephalogram acquisition equipment is used for acquiring 64-lead multi-type motor imagery electroencephalogram signals.
The step S5 includes the steps of:
the extracted features are classified using a support vector machine. Specifically, the brain training set feature data and the training set feature data obtained in the step S4 are selected, a radial basis function is used as a kernel function, an SVM model is built, and the universality of the model is verified by using the test set data.
S51, training an SVM model by using training set data feature vectors through a 5-fold cross validation method;
s52, testing the SVM model by using the feature vector of the test set, counting the number of correctly classified labels in the output action labels of the SVM model, and dividing the number by the total number of the labels to obtain the SVM model with the highest classification accuracy rate, and selecting the SVM model with the highest classification accuracy rate as the motor imagery electroencephalogram signal identification model.
The electroencephalogram data is subjected to channel selection and vector model training, an important region of brain activity in the tested motor imagery process is found, and the electroencephalogram channel signals of the region are used for carrying out on-line recognition on multiple motor imagery electroencephalogram signals and classifying the motor imagery electroencephalogram signals.
Embodiment two:
an electronic device comprising a processor and a memory communicatively coupled to the processor for storing instructions executable by the processor for performing the method of the first embodiment.
Embodiment III:
a server comprising at least one processor and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor to cause the at least one processor to perform the method of embodiment one.
Embodiment four:
a computer readable storage medium storing a computer program which when executed by a processor performs the method of embodiment one.
Those of ordinary skill in the art will appreciate that the elements and method steps of each example described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the elements and steps of each example have been described generally in terms of functionality in the foregoing description to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed methods and systems may be implemented in other ways. For example, the above-described division of units is merely a logical function division, and there may be another division manner when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. The units may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present application.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the application.
Claims (8)
1. An electroencephalogram signal classification method based on brain function connection network characteristics is characterized by comprising the following steps:
s1, acquiring motor imagery electroencephalogram signals;
s2, preprocessing the acquired motor imagery electroencephalogram signals, and dividing the acquired motor imagery electroencephalogram signals into training set data and test set data;
s3, selecting an important electroencephalogram channel in the motor imagery process through mutual information characteristics and network centrality of a brain connection network;
s4, extracting the preprocessed training set data and the data conforming to the important electroencephalogram channels selected in the step S3 from the test set data by using a co-space mode method, and generating brain training set feature data and training set feature data;
s5, training an SVM model through the training set characteristic data, testing the SVM model by using the training set characteristic data, and selecting the SVM model with the highest accuracy as an electroencephalogram signal recognition model;
s6, acquiring motor imagery electroencephalogram signals of a plurality of channels of a patient;
s7, inputting the acquired motor imagery electroencephalogram signals into a motor imagery electroencephalogram signal identification model, and identifying motor imagery electroencephalogram signals;
the step S3 includes the steps of:
s31, defining an electrode coverage area corresponding to each EEG lead as a node;
s32, calculating an entropy value of an electroencephalogram signal during motor imagery, wherein a calculation formula is as follows:
；
x and Y are two different lead brain electrical signals in a motor imagery period, p (X) is the probability of the signal value X, namely the edge distribution probability of the signal X, and H (X) is the entropy of the signal;
the mutual information between the two lead signals can be calculated by using the entropy values of the two signals, and the calculation formula is as follows:
；
wherein I (X, Y) is mutual information between two lead signals, and H (X, Y) is joint entropy of the signals X and Y;
the mutual information between the two lead signals is standardized, and the calculation formula is as follows:
；
NMI (X, Y) is the standardized mutual information of signals X and Y, and represents the synchronization between the signals X and Y;
s33, calculating standardized mutual information between two channels in each test time to obtain a motor imagery electroencephalogram signal standardized mutual information network;
s34, calculating the intermediacy centrality of all nodes of the electroencephalogram signal standardized mutual information network;
s35, counting channels with medium centrality values larger than 0 in all test orders, and performing descending order arrangement on the channels to generate channel importance ranking;
s36, sorting channel importance middle and frontThe individual channels serve as important electroencephalogram channels.
2. The brain function connection network feature-based electroencephalogram signal classification method according to claim 1, wherein the step of step S2 is as follows:
s21, performing 8-30Hz filtering processing on the acquired motor imagery electroencephalogram signals;
s22, performing 250Hz downsampling on the data obtained in the step S21;
s23, carrying out baseline correction processing on the data obtained in the step S22;
s24, carrying out data segmentation processing according to time on the data obtained in the step S23, and extracting 0.5-2.5 seconds of data as analysis data;
s25, analyzing the data according to 7: the scale of 1 is divided into training set data and test set data.
3. The brain function connection network feature-based brain electrical signal classification method according to claim 1, wherein: the formula for calculating the intermediacy in step S34 is as follows:
；
s, t, i represent nodes in the network,representing the number of all shortest paths from node s to node t, +.>BC representing the number of paths through node i in all shortest paths from node s to node t i BC representing the extent to which node i acts as a bridge in the network i The larger the value the higher the importance of node i in the network.
4. The brain function connection network feature-based brain electrical signal classification method according to claim 1, wherein: in the step S1, the electroencephalogram acquisition equipment is used for acquiring 64-lead multi-type motor imagery electroencephalogram signals.
5. The brain function connection network feature-based brain electrical signal classification method according to claim 1, wherein: step S5 comprises the steps of:
s51, training an SVM model by using training set data feature vectors through a 5-fold cross validation method;
s52, testing the SVM model by using the feature vector of the test set, counting the number of correctly classified labels in the output action labels of the SVM model, and dividing the number by the total number of the labels to obtain the SVM model with the highest classification accuracy rate, and selecting the SVM model with the highest classification accuracy rate as the motor imagery electroencephalogram signal identification model.
6. An electronic device comprising a processor and a memory communicatively coupled to the processor for storing processor-executable instructions, characterized in that: the processor is configured to perform the method of any of the preceding claims 1-5.
7. A server, characterized by: comprising at least one processor and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor to cause the at least one processor to perform the method of any of claims 1-5.
8. A computer-readable storage medium storing a computer program, characterized in that: the computer program implementing the method of any of claims 1-5 when executed by a processor.
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