CN114564990A - Electroencephalogram signal classification method based on multi-channel feedback capsule network - Google Patents

Electroencephalogram signal classification method based on multi-channel feedback capsule network Download PDF

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CN114564990A
CN114564990A CN202210188370.7A CN202210188370A CN114564990A CN 114564990 A CN114564990 A CN 114564990A CN 202210188370 A CN202210188370 A CN 202210188370A CN 114564990 A CN114564990 A CN 114564990A
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李畅
赵禹阊
宋仁成
刘羽
成娟
陈勋
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Abstract

The invention discloses an electroencephalogram signal classification method based on a multi-channel feedback capsule network, which comprises the following steps: 1, carrying out data selection and slice preprocessing on original electroencephalogram data; 2, establishing a multi-channel feedback capsule network classification model; designing a loss function, and establishing a classification model optimization target; and 4, inputting data to train the network, and finishing electroencephalogram signal classification by using the trained optimal model. The invention combines the advantages of the feedback network and the capsule network, can automatically complete signal classification without manually extracting features or processing signals of the original electroencephalogram signals, and can remarkably improve the accuracy of classification of the electroencephalogram signals, thereby increasing the application value of the electroencephalogram signals in the fields of medical treatment and the like.

Description

Electroencephalogram signal classification method based on multi-channel feedback capsule network
Technical Field
The invention relates to the field of electroencephalogram signal classification, in particular to a method for automatically classifying and predicting original electroencephalogram data of a subject through a deep learning method.
Background
The brain is an indispensable part of human daily life, and the electrical activity in the cerebral cortex contains abundant information, and the electrical activity may contain information of different emotions, motor imagery and diseases of human beings. With the development of brain-computer interface field and intelligent medical treatment, electroencephalogram signals have been widely applied to various fields such as emotion calculation, motor imagery, medical health and the like. If the information of the electroencephalogram information can be fully mined, different electroencephalogram signals can be accurately classified, and the use value of the electroencephalogram signals in the fields of medical treatment and the like can be increased.
Electroencephalography (EEG) is a portable device that records electrical activity in the cerebral cortex and can detect a variety of information related to the function of the brain electricity. Intracranial EEG signals are acquired by electrodes placed under the scalp, while scalp EEG signals are acquired by electrodes placed on the surface of the scalp. The intracranial brain electricity is suitable for a long-term implantable monitoring system, generally has higher signal-to-noise ratio, and the scalp brain electricity does not need to be implanted, and is noninvasive for a patient, so the intracranial brain electricity is common in practical use. Studies of EEG data of subjects show that some activity related to the EEG signal begins to show signs several minutes to hours before onset, so we can predictively classify the related activity by capturing the information in the EEG signal. However, analysis of EEG signals often requires a great deal of expertise and expertise, which is a time-consuming and labor-intensive project; moreover, EEG signals are continuous in time, and subjects can output EEG signals at any time, so that a system capable of automatically predicting and classifying EEG signals is needed.
In the conventional prediction classification algorithm based on the EEG signal, a researcher generally denoises the EEG signal first, extracts relevant features, and then classifies the obtained features by using a classifier to obtain a prediction effect. Common features such as Hjorth parameters, statistical moments, cumulative energy, auto-regressive coefficients, Lyapunov indices, etc. Commonly used classifiers include support vector machines, bayesian classifiers, and the like. However, extracting these features also requires a great deal of expert experience, and the effect of classification also depends largely on the extracted features, which may result in poor generalization effect; and the traditional classifier also has the defects in the aspect of improving the classification performance of the electroencephalogram signals.
In recent years, a deep learning method is widely applied to the field of brain-computer interfaces, can automatically learn more suitable characteristics from input, can learn tasks of characteristic extraction and classification at the same time, and obtains more accurate prediction effect in an electroencephalogram signal classification task. At present, most deep learning methods for classification of electroencephalogram signals use Convolutional Neural Networks (CNNs), and feature preprocessing is performed first. Although CNNs of different structures show different advantages in classification, CNNs have difficulty in delineating the link between local features and pooling may cause it to lose more spatial information that may be critical to the task of multi-channel electroencephalogram classification. The feature preprocessing process generally converts raw electroencephalogram data into features in various forms, and also includes operations such as filtering, denoising and the like, so that although more 'clean' data can be obtained, some important information may be lost.
Disclosure of Invention
The invention provides an electroencephalogram signal classification method based on a multi-channel feedback capsule network to overcome the defects of the prior art, so that classification of electroencephalogram signals can be automatically realized, and the classification accuracy of the electroencephalogram signals can be remarkably improved, so that the application value of the electroencephalogram signals in the fields of medical treatment and the like is increased.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to an electroencephalogram signal classification method based on a multi-channel feedback capsule network, which is characterized by comprising the following steps of:
step 1, acquiring an electroencephalogram signal data set with labeled information, and preprocessing channel data selection and sample segmentation on an original electroencephalogram signal in the electroencephalogram signal data set, so as to obtain N segments of electroencephalogram signal samples with the time length of T and form a training sample set, wherein X is recorded as X ═ X1,X2,...,Xn,...,XNIn which Xn∈RW×HRepresenting the nth electroencephalogram signal sample, H representing the channel number of the electroencephalogram signal, W being T multiplied by s representing the number of sampling points, and s representing the sampling rate of the electroencephalogram signal used by the data set; let the nth EEG signal sample XnThe corresponding label type is marked as YnIf the training sample set X corresponds to a label set Y ═ Y1,Y2,...,Yn,...,YN}；
Step 2, establishing a multi-channel feedback capsule network model, wherein the multi-channel feedback capsule network model comprises a one-dimensional convolution layer, a feedback network and a capsule network;
the feedback network comprises: m feedback models, wherein each feedback model comprises a feedback module;
the capsule network comprises: a primary capsule layer, a state capsule layer;
step 2.1, initializing the model parameters:
initializing the weights of all convolution layers by using xavier _ uniform _ initialization, and initializing conversion matrixes in the capsule network state capsule layers by using random distribution which meets the standard positive distribution;
step 2.2, the nth EEG signal sample X is processedn∈RW×HInputting the data into the multi-channel feedback capsule network, and obtaining the nth characteristic sequence after the time characteristic extraction and the data dimension reduction operation of the one-dimensional convolution layer
Figure BDA0003524562550000021
Wherein,
Figure BDA0003524562550000022
an nth signature sequence representing the one-dimensional convolution output
Figure BDA0003524562550000023
The xth feature map of (1), C1Representing the n-th signature sequence
Figure BDA0003524562550000024
The number of characteristic graphs in (1);
step 2.3, iterative processing of the feedback model;
step 2.3.1, defining the serial number of the current feedback model as m, and initializing m to be 1;
step 2.3.2, defining the maximum iteration times as t _ max and the current iteration times as t; and initializing t as 0;
step 2.3.3, the nth characteristic sequence is processed
Figure BDA0003524562550000025
The characteristic diagram of the mth feedback model input at the t time is recorded as
Figure BDA0003524562550000026
The characteristic diagram of the feedback module defining the mth feedback model and output at the t time is
Figure BDA0003524562550000031
Step 2.3.4, the characteristic diagram of the mth feedback model to the tth input by utilizing a convolution layer
Figure BDA0003524562550000032
Processing to obtain hidden state characteristics
Figure BDA0003524562550000033
Step 2.3.5, after assigning t +1 to t, judging t>Whether t _ max is true or not, if yes, indicating that t _ max +1 hidden state features are obtained, and executing the step 2.3.7; otherwise, when t is 1, it will
Figure BDA00035245625500000316
Is assigned to
Figure BDA0003524562550000034
Then step 2.3.6 is executed;
step 2.3.6, the mth feedback model utilizes a feedback module to pair feature maps
Figure BDA00035245625500000317
And
Figure BDA0003524562550000035
processing to obtain feedback information
Figure BDA0003524562550000036
Reusing a convolutional layer for the feedback information
Figure BDA0003524562550000037
Processing to obtain hidden state characteristics
Figure BDA0003524562550000038
Then, returning to the step 2.3.5;
step 2.3.7, t _ max +1 hidden state features
Figure BDA0003524562550000039
The characteristic diagram of the mth feedback model output obtained after 1 × 1 convolution operation is recorded as
Figure BDA00035245625500000310
Wherein,
Figure BDA00035245625500000311
an nth signature sequence representing the output of the mth feedback model
Figure BDA00035245625500000318
The xth feature map of (1); cm+1In the nth signature sequence representing the output of the mth feedback model
Figure BDA00035245625500000319
The number of feature maps of (a);
step 2.3.8, after assigning m +1 to m, judging m>If M is true, executing step 2.4, otherwise, executing the n-th characteristic sequence
Figure BDA00035245625500000312
As the input of the mth feedback model, and returning to the step 2.3.2 for sequential execution;
step 2.4, processing the capsule network;
step 2.4.1, the primary capsule layer comprises: a convolutional operating layer, low-level capsule;
the status capsule layer comprises: advanced capsule, state capsule, dynamic routing;
step 2.4.2, characteristics of feedback network output
Figure BDA00035245625500000313
Inputting the data into the capsule network, extracting local space-time characteristics by a convolution operation layer in the primary capsule layer, converting the local space-time characteristics into a vector form, and storing the vector form in a low-level capsule un＝{un,1,un,2,...,un,k1In which k is1Indicating the number of low-grade capsules; u. un,k1K-th representing the n-th signature sequence1A lower grade capsule;
then storing the local space-time characteristics u in the form of vectors in the low-level capsulen＝{un,1,un,2,...,un,k1Inputting the data into a state capsule layer, and converting the local space-time characteristics u by using an initialized conversion matrixnSpatio-temporal features being tied to the whole
Figure BDA00035245625500000314
And stored in a high-grade capsule, wherein,
Figure BDA00035245625500000315
indicating that the nth signature sequence belongs to the kth state1A low-grade capsule, j ═ 1, 2.., k2，k2Representing the number of classifications;
step 2.4.3, defining the iteration times of the dynamic route as R, and the current iteration times as R; and initializing r to 0;
let coefficients of class j state of ith high-grade capsule of the r iteration
Figure BDA0003524562550000041
i＝1,2,...,k1；j＝1,2,...,k2；
Step 2.4.4, after r +1 is assigned to r, judging r>If R is true, then the activity vector is calculated
Figure BDA0003524562550000042
Is assigned to the nth EEG signal sample XnState of (v) capsulen＝{vn,1,vn,2,...,vn,k2}; wherein v isn,k2Denotes the kth2A state capsule of a class state; otherwise, the coefficients are processed by the "routing softmax" operation
Figure BDA0003524562550000043
Conversion into weight coefficients
Figure BDA0003524562550000044
And weighting and summing the space-time characteristics stored in the advanced capsule to obtain the activity vector
Figure BDA0003524562550000045
Wherein,
Figure BDA0003524562550000046
denotes the kth2Activity vectors of class states, and activity vectors
Figure BDA0003524562550000047
Is compressed to between 0 and 1, thereby obtaining a compressed activity vector
Figure BDA0003524562550000048
Wherein,
Figure BDA0003524562550000049
represents k2Class state pressureA reduced activity vector; finally, the compressed activity vector is utilized
Figure BDA00035245625500000410
Obtaining coefficient of the r iteration
Figure BDA00035245625500000411
And returning to the step 2.4.4;
step 2.5, for the state capsule vnCalculating L2Obtaining the nth EEG signal sample X by normnTo each state;
step 3, establishing an edge loss function L by using the formula (1)n：
Ln＝Yclassmax(0，m+-||vn||2)2+λ(1-Yclass)max(0，||vn||2-m-)2 (1)
In the formula (1), class represents a class, and classes belongs to {0,1}, YclassA tag value representing the brain electrical state of the class I; if class is 0, then Y class0; if class is 1, then Yclass＝1；||vn||2Representing the probability of the feedback capsule network predicting the two states; m is+And m-Is two threshold parameters, λ is the weight lost to misclassification for the two states;
and 4, training the multi-channel feedback capsule network model by using an Adam optimizer based on the training sample set X, calculating an edge loss function L, adjusting the learning rate in the training process by using an exponential decay method, and stopping after verifying that the loss is not reduced any more or the maximum training times is reached in continuous f times of training, so that the trained network model is obtained and is used for classifying the electroencephalogram signals.
The electroencephalogram signal classification method based on the multi-channel feedback capsule network is also characterized in that the feedback information in the step 2.3.6
Figure BDA00035245625500000412
Is obtained by the following steps:
the feedback module utilizes the feedback information
Figure BDA00035245625500000413
For characteristic diagram
Figure BDA00035245625500000415
Adjusting to obtain refined input characteristic diagram
Figure BDA00035245625500000414
And then a projection group is used to input the characteristic diagram
Figure BDA0003524562550000051
Obtaining a first downsampling characteristic after performing downsampling convolution operation
Figure BDA0003524562550000052
Then the first down-sampled feature
Figure BDA0003524562550000053
After the up-sampling convolution operation is carried out, a first reconstruction characteristic is obtained
Figure BDA0003524562550000054
Then to
Figure BDA0003524562550000055
And
Figure BDA0003524562550000056
performing downsampling convolution operation together to obtain second downsampled characteristic
Figure BDA0003524562550000057
Then to
Figure BDA0003524562550000058
And
Figure BDA0003524562550000059
performing an upsampling convolution operation to obtain a second multiplicityStructural features
Figure BDA00035245625500000510
In the same way, z reconstruction characteristics are obtained
Figure BDA00035245625500000511
Performing convolution operation with convolution kernel of 1 × 1 on z reconstruction features to obtain feedback information
Figure BDA00035245625500000512
1. The invention provides a multi-channel electroencephalogram feedback capsule network model based on deep learning, which is characterized in that a feedback network is used in electroencephalogram signal classification for the first time, higher-level characteristics are extracted by using low-level characteristics, the information of signals per se is explored more fully, and stronger characteristic representation is obtained, so that electroencephalogram signal information can be represented better, and the classification accuracy is improved.
2. The invention combines a feedback mechanism and a dynamic routing mechanism in the classification of the electroencephalogram signals for the first time, extracts stronger time information by using the feedback mechanism, combines the space information captured by a capsule network and other instantiation characteristics, overcomes the defects of the traditional CNN, and improves the classification performance of the electroencephalogram signals.
3. The invention is an end-to-end structure model, does not need to carry out manual denoising and characteristic preprocessing processes on an original EEG signal in advance, directly carries out training learning from the original EEG data, and is more in line with a deep learning data driving mode, thereby not needing a large amount of expert experience and professional knowledge and obtaining better generalization.
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FIG. 1 is a schematic diagram of a network architecture according to the present invention;
FIG. 2 is a conceptual diagram of a feedback model of the present invention;
FIG. 3 is a block diagram of a feedback module of the present invention;
FIG. 4 is a block diagram of dynamic routing of the present invention;
FIG. 5 is a graph comparing the effect of AUC in the classification of brain electrical signals in the CHB-MIT database;
FIG. 6 is a graph comparing the sensitivity effect of electroencephalogram classification in the CHB-MIT database;
FIG. 7 is a comparison graph of the effect of the electroencephalogram classification FPR in the CHB-MIT database.
Detailed Description
In this embodiment, an electroencephalogram signal classification method based on a multi-channel feedback capsule network mainly performs electroencephalogram signal classification by using a feedback network and a capsule network. The feedback network is used for extracting stronger time information, combines the correlation between the space information extracted by the capsule network and the local characteristics, and distributes the characteristic weight through a dynamic routing mechanism to finally achieve an accurate classification effect. As shown in fig. 1, specifically, the method comprises the following steps:
step 1, acquiring an electroencephalogram signal data set with labeled information, and preprocessing channel data selection and sample segmentation on an original electroencephalogram signal in the electroencephalogram signal data set, so as to obtain N segments of electroencephalogram signal samples with the time length of T and form a training sample set, wherein X is recorded as X ═ X1,X2,...,Xn,...,XNIn which Xn∈RW×HRepresenting the nth electroencephalogram signal sample, H representing the channel number of the electroencephalogram signal, W being T multiplied by s representing the number of sampling points, and s representing the sampling rate of the electroencephalogram signal used by the data set; let the nth EEG signal sample XnThe corresponding label type is marked as YnIf the training sample set X corresponds to a label set Y ═ Y1,Y2,...,Yn,...,YN}; the method uses a public EEG epilepsy data set CHB-MIT;
step 2, establishing a multi-channel feedback capsule network model, wherein the multi-channel feedback capsule network model comprises a one-dimensional convolution layer, a feedback network and a capsule network;
the feedback network includes: m feedback models, wherein each feedback model comprises a feedback module;
the capsule network comprises: a primary capsule layer and a state capsule layer;
step 2.1, initializing model parameters:
the weights of all the convolution layers are initialized by using a xavier _ uniform _ mode, and the conversion matrix in the capsule network state capsule layer is initialized by using a random mode which meets the standard and is distributed just over ten;
step 2.2, the nth EEG signal sample Xn∈RW×HInputting the data into a multi-channel feedback capsule network, and obtaining an nth characteristic sequence after the time characteristic extraction and the data dimension reduction operation of the one-dimensional convolution layer
Figure BDA0003524562550000061
Wherein,
Figure BDA0003524562550000062
n-th signature sequence representing one-dimensional convolution output
Figure BDA0003524562550000067
The xth feature map of (1), C1Representing the n-th signature sequence
Figure BDA0003524562550000068
The number of characteristic graphs in (1); because the original electroencephalogram signal is used, the signal contains noise information, the function of denoising can be achieved by using one-dimensional convolution, the data dimension can be reduced, the convolution kernel used in the experiment is 11 multiplied by 1 in size, the step length is 1, and the maximum pooling operation size is 8 multiplied by 1;
step 2.3, iterative processing of a feedback model; the feedback network comprises feedback models, each feedback model comprises a feedback module, and each feature extraction is a process of carrying out loop iteration;
step 2.3.1, defining the serial number of the current feedback model as m, and initializing m to be 1;
step 2.3.2, defining the maximum iteration times as t _ max and the current iteration times as t; and initializing t as 0;
step 2.3.3, the nth characteristic sequence
Figure BDA0003524562550000069
The characteristic diagram of the mth feedback model input at the t time is recorded as
Figure BDA00035245625500000610
(ii) a The characteristic diagram of the feedback module defining the mth feedback model at the t-th output is
Figure BDA0003524562550000063
Step 2.3.4, the mth feedback model utilizes a feature map of convolutional layer to the tth input
Figure BDA00035245625500000611
Processing to obtain hidden state characteristics
Figure BDA0003524562550000064
Hidden state here
Figure BDA0003524562550000065
Is composed of
Figure BDA0003524562550000066
The function is to connect more stable acquisition characteristic information as residual error;
step 2.3.5, after assigning t +1 to t, judging t>Whether t _ max is true or not, if yes, indicating that t _ max +1 hidden state features are obtained, and executing the step 2.3.7; otherwise, when t is 1, it will
Figure BDA00035245625500000715
Is assigned to
Figure BDA0003524562550000071
Then step 2.3.6 is executed;
step 2.3.6, the mth feedback model utilizes the feedback module to match the feature map
Figure BDA00035245625500000716
And
Figure BDA0003524562550000072
processing to obtain feedback information
Figure BDA0003524562550000073
Reusing a convolutional layer pair feedback information
Figure BDA0003524562550000074
Processing to obtain hidden state characteristics
Figure BDA0003524562550000075
Then, returning to the step 2.3.5; the specific calculation formula is as formula (2) and formula (3):
Figure BDA0003524562550000076
in formulae (2) and (3), fFBIndicating a feedback module, fConvThe convolution operation is represented, the size of a convolution kernel used in the method is 11 multiplied by 1, the step size is 1, and the maximum pooling size is 4 multiplied by 1;
step 2.3.7, t _ max +1 hidden state features
Figure BDA0003524562550000077
The characteristic diagram of the mth feedback model output obtained after 1 × 1 convolution operation is recorded as
Figure BDA0003524562550000078
Wherein,
Figure BDA0003524562550000079
n characteristic sequence representing m feedback model output
Figure BDA00035245625500000717
The xth profile of (1); cm+1In the nth signature sequence representing the output of the mth feedback model
Figure BDA00035245625500000718
The number of feature maps of (a); calculating as shown in equation (4):
Figure BDA00035245625500000710
in the formula (4), the reaction mixture is,
Figure BDA00035245625500000711
representing a convolution operation with a convolution kernel size of 1 and a step size of 1, fcatRepresenting a splicing operation; the feedback model extracts useful information of different levels by utilizing the characteristic information of each hidden state to obtain final characteristic output;
step 2.3.8, after m +1 is assigned to m, m is judged>If M is true, execute step 2.4, otherwise, execute the n-th characteristic sequence
Figure BDA00035245625500000712
As the input of the mth feedback model, and returning to the step 2.3.2 for sequential execution;
step 2.4, processing the capsule network;
step 2.4.1, the primary capsule layer comprises: a convolutional operating layer, low-level capsule;
the state capsule layer comprises: advanced capsule, state capsule, dynamic routing;
step 2.4.2, characteristics of feedback network output
Figure BDA00035245625500000713
Inputting into capsule network, extracting local space-time characteristics from convolution operation layer in primary capsule layer, converting into vector form, and storing in low-level capsule
Figure BDA00035245625500000714
In which k is1Indicating the number of low-grade capsules; u. ofn,iThe i-th low-level capsule representing the n-th signature sequence, i ═ 1,21(ii) a The traditional CNN convolution operation generates scalar quantities which can only represent local features, and the capsule network converts the convolved scalar quantities into vector forms, so that the relation among the features can be enriched, and the feature relation among the local features is stored; the convolution operation in the primary capsule layer is convolution with convolution kernel size of 6 x 6 and step size of 2 and convolution with convolution kernel size of 5 x 5 and step size of 2;
then storing the local space-time characteristics in the form of vectors in the low-level capsule
Figure BDA0003524562550000081
Inputting into the state capsule layer, converting the local space-time characteristics u by the initialized conversion matrixnSpatio-temporal features being tied to the whole
Figure BDA0003524562550000082
And stored in a high-grade capsule, wherein,
Figure BDA0003524562550000083
i-th low-level capsule indicating that the nth signature sequence belongs to the j-th state, j being 1,22，k2Representing the number of classifications; the relation between local features and the whole can be enriched through the transformation matrix, and more instantiated features are stored in a high-level vector; the calculation formula is as follows:
Figure BDA0003524562550000084
step 2.4.3, defining the iteration times of the dynamic route as R, and the current iteration times as R; and initializing r to 0;
let coefficients of class j state of ith high-grade capsule of the r iteration
Figure BDA0003524562550000085
Step 2.4.4, after r +1 is assigned to r, judging r>If R is true, then the activity vector is calculated
Figure BDA0003524562550000086
Is assigned to the nth EEG signal sample XnState of the capsule
Figure BDA0003524562550000087
Wherein,
Figure BDA0003524562550000088
denotes the kth2A state capsule of a class state; otherwise, the coefficients are processed by the "routing softmax" operation
Figure BDA0003524562550000089
Conversion into weight coefficients
Figure BDA00035245625500000810
And weighting and summing the space-time characteristics stored in the advanced capsule to obtain an activity vector
Figure BDA00035245625500000811
Wherein,
Figure BDA00035245625500000812
denotes the kth2Activity vectors of class states, and activity vectors
Figure BDA00035245625500000813
Is compressed to between 0 and 1, thereby obtaining a compressed motion vector
Figure BDA00035245625500000814
Wherein,
Figure BDA00035245625500000815
represents k2Class state compressed activity vectors; finally, the compressed activity vector is utilized
Figure BDA00035245625500000816
Obtaining the coefficient of the r-th iteration
Figure BDA00035245625500000817
And returning to the step 2.4.4; the calculation formulas are shown in (6) to (9):
Figure BDA00035245625500000818
Figure BDA0003524562550000091
the method initializes the equal initial prior probability of each capsule, and then carries out iterative computation; the weight of the information with distinction is increased through the dynamic routing process, the weight of the information without distinction is reduced, and a better classification effect is achieved;
step 2.5, capsule of right state
Figure BDA0003524562550000092
Calculating L2Obtaining the nth EEG signal sample X by normnTo each state;
step 3, establishing an edge loss function L by using the formula (10)n：
Figure BDA0003524562550000093
In the formula (1), class represents a class, and classes belongs to {0,1}, YclassA tag value representing the brain state of the class; if class is 0, i.e. one of the states, then Y isclass0; if class is 1, another state, then
Figure BDA0003524562550000094
Representing the probability of the feedback capsule network predicting both states. m is a unit of+And m-Is two threshold parameters and λ is the weight lost to misclassification for the two states.
And 4, training the multi-channel feedback capsule network model by using an Adam optimizer based on the training sample set X, calculating an edge loss function L, adjusting the learning rate in the training process by using an exponential decay method, and stopping after verifying that the loss is not reduced any more or the maximum training times is reached in continuous 10 times of training, so that the trained network model is obtained and is used for classifying the electroencephalogram signals.
In one embodiment, the feedback information in step 2.3.6
Figure BDA0003524562550000095
Is obtained by the following steps:
the feedback module utilizes the feedback information
Figure BDA0003524562550000096
For characteristic diagram
Figure BDA00035245625500000920
Adjusting to obtain refined input characteristic diagram
Figure BDA0003524562550000097
Then using a projection group to input the characteristic diagram
Figure BDA0003524562550000098
Obtaining down-sampling characteristics after performing down-sampling convolution operation
Figure BDA0003524562550000099
Then down-sampling features
Figure BDA00035245625500000910
After the up-sampling convolution operation is carried out, a reconstruction characteristic is obtained
Figure BDA00035245625500000911
Can be reused
Figure BDA00035245625500000912
And
Figure BDA00035245625500000913
performing downsampling convolution operation to obtain
Figure BDA00035245625500000914
And performing up-sampling convolution operation on the down-sampling features to obtain reconstruction features
Figure BDA00035245625500000915
Thereby using the z projection pairs to refine the input feature map
Figure BDA00035245625500000916
Processing to obtain z reconstruction characteristics
Figure BDA00035245625500000917
Performing convolution operation with convolution kernel of 1 × 1 on z reconstruction features to obtain feedback information
Figure BDA00035245625500000918
The calculation formula is shown in formula (11) to formula (14):
Figure BDA00035245625500000919
Figure BDA0003524562550000101
in the formula (11) to the formula (14), each projection layer includes an up-sampling and down-sampling operation, the down-sampling operation extracts feature information, and the up-sampling operation reconstructs features, so that the extracted features are more effective.
Figure BDA0003524562550000102
A down-sampled feature map is represented,
Figure BDA0003524562550000103
representing an up-sampled feature map. ConviAnd DeconviRepresenting the downsampling and upsampling operations for the ith projection layer, the convolution kernel size is 11 x 1 with a step size of 1.
In specific implementation, the multi-channel feedback capsule network (FB-CapsNet) is compared with some advanced electroencephalogram signal classification deep learning methods such as a deep convolutional neural network + a multi-layer perceptron (DCNN + MLP), a deep neural network + a bidirectional long-short term memory network (DCNN + Bi-LSTM), and a traditional capsule network model (CapsNet). The performance index on the CHB-MIT database is as follows:
TABLE 1 average performance of different methods on CHB-MIT database for classification of electroencephalograms
Sensitivity (%) AUC FPR(\h)
DCNN+MLP 85.9 0.844 0.370
DCNN+Bi-LSTM 87.7 0.877 0.275
CapsNet 87.4 0.877 0.224
FB-CapsNet 93.4 0.928 0.096
The leave-one-out cross-validation results for 19 subjects are shown in fig. 4, 5, and 6. And (4) analyzing results:
the experimental results in the table 1 show that compared with other deep learning methods DCNN + MLP and DCNN + Bi-LSTM in the field of electroencephalogram signal classification, FB-CapsNet improves all indexes, and reduces the false alarm times in the interval of onset while accurately predicting the early-stage type of onset on a CHB-MIT database. Compared with the original capsule network, the method also obviously improves the classification prediction performance, and verifies that the feedback network can better extract representative characteristics. In addition, as can be seen from fig. 4, 5 and 6, the model has obvious improvement on most subjects, and the type areas and signal distributions of different types of electroencephalogram signals are different for different subjects, which shows that the method has good identification capability and strong generalization effect on different subjects.
In conclusion, the invention fully utilizes rich EEG information contained in the original EEG signal, uses the feedback network to extract high-level time, combines the spatial characteristics and other instantiation information extracted by the capsule network, and then distributes the weight to the high-level characteristics through a dynamic routing mechanism, thereby achieving more accurate EEG signal classification effect. In the two-classification test of the public data set CHB-MIT, the electroencephalogram data of the early-stage attack class can be classified more accurately, the false alarm times in the inter-attack class are reduced, and the method is superior to the traditional convolutional neural network and the original capsule network.

Claims (2)

1. An electroencephalogram signal classification method based on a multi-channel feedback capsule network is characterized by comprising the following steps:
step 1, acquiring an electroencephalogram signal data set with labeled information, and preprocessing channel data selection and sample segmentation on an original electroencephalogram signal in the electroencephalogram signal data set, so as to obtain N segments of electroencephalogram signal samples with the time length of T and form a training sample set, wherein X is recorded as X ═ X1,X2,...,Xn,...,XNIn which Xn∈RW×HRepresenting the nth electroencephalogram signal sample, H representing the channel number of the electroencephalogram signal, W being T multiplied by s representing the number of sampling points, and s representing the sampling rate of the electroencephalogram signal used by the data set; let the nth EEG signal sample XnThe corresponding label type is marked as YnIf the training sample set X corresponds to a label set Y ═ Y1,Y2,...,Yn,...,YN}；
Step 2, establishing a multi-channel feedback capsule network model, wherein the multi-channel feedback capsule network model comprises a one-dimensional convolution layer, a feedback network and a capsule network;
the feedback network comprises: m feedback models, wherein each feedback model comprises a feedback module;
the capsule network comprises: a primary capsule layer, a state capsule layer;
step 2.1, initializing model parameters:
initializing the weights of all convolution layers by using a xavier _ uniform _ initialization, and initializing a conversion matrix in a capsule layer of the capsule network state by using a random distribution which meets a standard positive distribution;
step 2.2, the nth EEG signal sample X is processedn∈RW×HInputting the data into the multi-channel feedback capsule network, and obtaining the nth characteristic sequence after the time characteristic extraction and the data dimension reduction operation of the one-dimensional convolution layer
Figure FDA0003524562540000011
Wherein,
Figure FDA0003524562540000012
an nth signature sequence representing the one-dimensional convolution output
Figure FDA0003524562540000013
The xth feature map of (1), C1Representing the n-th signature sequence
Figure FDA0003524562540000014
The number of characteristic graphs in (1);
step 2.3, iterative processing of the feedback model;
step 2.3.1, defining the serial number of the current feedback model as m, and initializing m to be 1;
step 2.3.2, defining the maximum iteration times as t _ max and the current iteration times as t; and initializing t ═ 0;
step 2.3.3, the nth characteristic sequence
Figure FDA0003524562540000015
The characteristic diagram of the mth feedback model input at the t time is recorded as
Figure FDA0003524562540000016
The characteristic diagram of the feedback module defining the mth feedback model and output at the t time is
Figure FDA0003524562540000017
Step 2.3.4, the characteristic diagram of the mth feedback model to the tth input by utilizing a convolution layer
Figure FDA0003524562540000018
Processing to obtain hidden state characteristics
Figure FDA0003524562540000019
Step 2.3.5, after assigning t +1 to t, judging t>Whether t _ max is true or not, if yes, indicating that t _ max +1 hidden state features are obtained, and executing the step 2.3.7; otherwise, when t equals 1, it will
Figure FDA00035245625400000110
Is assigned to
Figure FDA00035245625400000111
Then step 2.3.6 is executed;
step 2.3.6, the mth feedback model utilizes a feedback module to pair feature maps
Figure FDA0003524562540000021
And
Figure FDA0003524562540000022
to carry outProcessing to obtain feedback information
Figure FDA0003524562540000023
Reusing a convolutional layer for the feedback information
Figure FDA0003524562540000024
Processing to obtain hidden state characteristics
Figure FDA0003524562540000025
Then, returning to the step 2.3.5;
step 2.3.7, t _ max +1 hidden state features
Figure FDA0003524562540000026
The characteristic diagram of the mth feedback model output obtained after 1 × 1 convolution operation is recorded as
Figure FDA0003524562540000027
Wherein,
Figure FDA0003524562540000028
an nth signature sequence representing the output of the mth feedback model
Figure FDA0003524562540000029
The xth feature map of (1); cm+1In the nth signature sequence representing the output of the mth feedback model
Figure FDA00035245625400000210
The number of feature maps of (a);
step 2.3.8, after m +1 is assigned to m, m is judged>If M is true, execute step 2.4, otherwise, execute the n-th characteristic sequence
Figure FDA00035245625400000211
As the input of the mth feedback model, and returning to the step 2.3.2 for sequential execution;
step 2.4, processing the capsule network;
step 2.4.1, the primary capsule layer comprises: a convolution operation layer, low-grade capsule;
the status capsule layer comprises: advanced capsule, state capsule, dynamic routing;
step 2.4.2, characteristics of feedback network output
Figure FDA00035245625400000212
Inputting the data into the capsule network, extracting local space-time characteristics by a convolution operation layer in the primary capsule layer, converting the local space-time characteristics into a vector form, and storing the vector form in a low-level capsule
Figure FDA00035245625400000213
In which k is1Indicating the number of low-grade capsules;
Figure FDA00035245625400000214
k-th representing the n-th signature sequence1A lower grade capsule;
then storing the local space-time characteristics in the form of vectors in the low-level capsule
Figure FDA00035245625400000215
Inputting into the state capsule layer, converting the local space-time characteristics u by the initialized conversion matrixnSpatio-temporal features being tied to the whole
Figure FDA00035245625400000216
And stored in a high-grade capsule, wherein,
Figure FDA00035245625400000217
indicating that the nth signature sequence belongs to the kth state1A low-grade capsule, j ═ 1, 2.., k2，k2Representing the number of classifications;
step 2.4.3, defining the iteration times of the dynamic route as R, and the current iteration times as R; and initializing r to 0;
let coefficients of class j state of ith high-grade capsule of the r iteration
Figure FDA00035245625400000218
Step 2.4.4, after r +1 is assigned to r, judging r>If R is true, then the activity vector is calculated
Figure FDA00035245625400000219
Is assigned to the nth EEG signal sample XnState of the capsule
Figure FDA00035245625400000220
Wherein,
Figure FDA00035245625400000221
denotes the kth2A state capsule of a class state; otherwise, the coefficients are processed by the "routing softmax" operation
Figure FDA0003524562540000031
Conversion into weight coefficients
Figure FDA0003524562540000032
And weighting and summing the space-time characteristics stored in the advanced capsule to obtain an activity vector
Figure FDA0003524562540000033
Wherein,
Figure FDA0003524562540000034
denotes the kth2Activity vectors of class states, and activity vectors
Figure FDA0003524562540000035
Is compressed to between 0 and 1, thereby obtaining a compressed motion vector
Figure FDA0003524562540000036
Wherein,
Figure FDA0003524562540000037
represents k2Class state compressed activity vectors; finally, the compressed activity vector is utilized
Figure FDA0003524562540000038
Obtaining coefficient of the r iteration
Figure FDA0003524562540000039
And returning to the step 2.4.4;
step 2.5, for the state capsule vnCalculating L2Obtaining the nth EEG signal sample X by normnTo each state;
step 3, establishing an edge loss function L by using the formula (1)n：
Ln＝Yclassmax(0，m+-||vn||2)2+λ(1-Yclass)max(0，||vn||2-m-)2 (1)
In the formula (1), class represents a class, and classes belongs to {0,1}, YclassA tag value representing the brain electrical state of the class I; if class is 0, then Yclass0; if class is 1, then Yclass＝1；||vn||2Representing the probability of the feedback capsule network predicting the two states; m is+And m-Is two threshold parameters, λ is the weight lost to misclassification for the two states;
and 4, training the multi-channel feedback capsule network model by using an Adam optimizer based on the training sample set X, calculating an edge loss function L, adjusting the learning rate in the training process by using an exponential decay method, and stopping after verifying that the loss is not reduced any more or the maximum training times is reached in continuous f times of training, so that the trained network model is obtained and is used for classifying the electroencephalogram signals.
2. The multi-based of claim 1The EEG signal classification method of the channel feedback capsule network is characterized in that the feedback information in the step 2.3.6 is
Figure FDA00035245625400000310
Is obtained by the following steps:
the feedback module utilizes the feedback information
Figure FDA00035245625400000311
For characteristic diagram
Figure FDA00035245625400000312
Adjusting to obtain refined input characteristic diagram
Figure FDA00035245625400000313
Then using a projection group to input the characteristic diagram
Figure FDA00035245625400000314
Obtaining a first downsampling characteristic after performing downsampling convolution operation
Figure FDA00035245625400000315
Then the first down-sampled feature
Figure FDA00035245625400000316
After the up-sampling convolution operation is carried out, a first reconstruction characteristic is obtained
Figure FDA00035245625400000317
Then to
Figure FDA00035245625400000318
And
Figure FDA00035245625400000319
performing downsampling convolution operation together to obtain a second downsampling characteristic
Figure FDA00035245625400000320
Then to
Figure FDA00035245625400000321
And
Figure FDA00035245625400000322
performing an upsampling convolution operation to obtain a second reconstruction feature
Figure FDA00035245625400000323
In the same way, z reconstruction characteristics are obtained
Figure FDA00035245625400000324
Performing convolution operation with convolution kernel of 1 × 1 on z reconstruction features to obtain feedback information
Figure FDA00035245625400000325
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