CN113157094A - Electroencephalogram emotion recognition method combining feature migration and graph semi-supervised label propagation - Google Patents

Electroencephalogram emotion recognition method combining feature migration and graph semi-supervised label propagation Download PDF

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CN113157094A
CN113157094A CN202110428950.4A CN202110428950A CN113157094A CN 113157094 A CN113157094 A CN 113157094A CN 202110428950 A CN202110428950 A CN 202110428950A CN 113157094 A CN113157094 A CN 113157094A
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李文政
黄文娜
王文娟
彭勇
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Abstract

The invention discloses an electroencephalogram emotion recognition method combining feature migration and image semi-supervised label propagation. The method comprises the specific steps of guiding a testee to watch electroencephalogram data with obvious emotional tendency, preprocessing the electroencephalogram data, extracting characteristics, and generating a sample matrix. And constructing a learning model combining feature transfer learning and state estimation, wherein the learning model comprises a single mapping domain adaptation model and a semi-supervised label propagation model, and obtaining a joint optimization objective function. And then, according to the target function, joint optimization is realized by fixing two variables and updating the rule of the other variable, and a subspace is shared by continuous iterative optimization features, so that a better migration effect is obtained, and the accuracy of emotion recognition is improved. The method can be used for emotion recognition across test migrations.

Description

Electroencephalogram emotion recognition method combining feature migration and graph semi-supervised label propagation
Technical Field
The invention belongs to the technical field of electroencephalogram signal processing, and particularly relates to an electroencephalogram emotion recognition method combining feature migration and graph semi-supervised label propagation.
Background
Emotion is a corresponding physiological expression produced by the brain when people are stimulated by the outside world in daily life and work, and has the functions of information transmission and behavior regulation. For a machine, the emotion recognition method can automatically and accurately recognize human emotion, and better realize emotion human-computer interaction is a research hotspot in the fields of current information science, psychology, cognitive neuroscience and the like. However, human emotional expression is affected by such factors as environmental situation, expression object and thinking cognition, thereby causing difficulty in emotion recognition, and recognition of current emotional state can be mainly classified into four categories: the facial expression, the text, the voice and the physiological signal are non-physiological signals, and the non-physiological signals have disguise property and cannot ensure the reliability of a recognition result, so the physiological signals are generally adopted for recognition. The electroencephalogram signal is a physiological signal which is not easy to disguise, and the electroencephalogram signal is very important for improving the accuracy and reliability of emotion recognition in human-computer interaction.
Common migratory learning methods can be generally classified into four categories: model-based, feature-based, sample-based, relationship-based. Among them, the feature-based method is the most widely used migration method, and this class of methods aims to learn a shared feature representation, i.e. a shared subspace, and map the target domain and source domain data into the shared subspace by means of projection in combination with some metric strategies, such as maximum mean difference MMD, etc., so that the difference between the two conditional distributions and the edge distribution is minimized. However, for target domain data without tags, calculating the condition distribution of the target domain data cannot be achieved, so if a good target domain tag can be learned, a better projection matrix can be obtained, the difference between a source domain and a target domain can be reduced, a more excellent target tag can be obtained, the model identification precision is improved, and the reliability of emotion human-computer interaction is ensured.
However, the electroencephalogram signal is an unsteady signal, and different electroencephalogram signal characteristics tested for the same emotion are different if the cross-test migration recognition is simply performed, and the requirement of human-computer interaction on emotion recognition accuracy cannot be well met.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an electroencephalogram emotion recognition method combining feature migration and graph semi-supervised mark propagation, which is implemented by aligning a projection matrix W and a target domain emotion label F of source domain data and target domain data in a subspacetAnd performing joint iterative optimization on the undirected graph S, and sharing a subspace through continuous iterative optimization features to obtain a better migration effect so as to improve the accuracy of emotion recognition.
Step 1, collecting electroencephalogram data of a tested person in K different emotional states.
And 2, preprocessing and extracting features of the electroencephalogram data acquired in the step 1, wherein each sample matrix X consists of electroencephalogram features of a testee, and the label vector y is an emotion label corresponding to the electroencephalogram features in the sample matrix X. Selecting two different sample matrixes as source domain data X respectivelysAnd target data Xt。
And 3, constructing a learning model combining feature transfer learning and state estimation, wherein the learning model comprises a single mapping domain adaptation model and a semi-supervised label propagation model.
Step 3.1, establishing a single mapping domain adaptive model:
Figure BDA0003030687820000021
wherein the content of the first and second substances,
Figure BDA0003030687820000022
in order to map the source domain data and the target domain data to a mapping matrix in the same shared subspace, p represents a target dimension, and d represents an original dimension;
Figure BDA0003030687820000023
c is the category number;
Figure BDA0003030687820000024
to augment the target domain data tag, ns、ntAre respectively provided withThe number of samples of source domain data and target domain data; n is ns+ntRepresenting the total number of samples;
Figure BDA0003030687820000025
Figure BDA0003030687820000026
the matrix is a diagonal matrix, wherein the kth diagonal element in the matrix is the reciprocal of the number of data samples of the kth source domain or target domain, and k is 1, 2. Wherein
Figure BDA0003030687820000027
Is a central matrix, I is a unit matrix,
Figure BDA0003030687820000028
representing the calculation of the Frobenius norm; the superscript T denotes transpose.
Step 3.2, establishing a semi-supervised label propagation model based on an undirected graph:
Figure BDA0003030687820000029
wherein S isijRepresenting the elements of the ith row and the jth column of the undirected graph correlation matrix S, i.e., the degree of correlation between the ith sample and the jth sample (i 1, 2.., n; j 1, 2.., n); alpha and gamma are parameters;
Figure BDA00030306878200000210
is obtained by a correlation matrix S, L is S-D, D is an n-dimensional diagonal matrix, and the ith diagonal element
Figure BDA00030306878200000211
Figure BDA00030306878200000212
For label matrices for label propagation, Fs、FtLabels for the source domain and the target domain, respectively; 1 is a full 1 matrix; tr (-) represents a trace of a matrix；
Figure BDA00030306878200000213
Represents the calculation of a 2 norm; the first term in the formula (2) is semi-supervised label propagation, and the second term and the third term are correlation matrixes for solving an undirected graph so as to ensure that a good undirected graph is constructed.
Step 3.3, combining the two models constructed in the steps 3.1 and 3.2, integrating the domain adaptation and the undirected graph-based semi-supervised label propagation into a unified framework for joint optimization, wherein the optimization model is as follows:
Figure BDA0003030687820000031
λ, α, γ are parameters.
Step 4, according to the optimization model established in the step 3.3, mapping matrix W and target domain data label FtAnd performing joint iterative optimization on the undirected graph incidence matrix S.
Step 4.1, initialize the data label F of the target domaintHas a value of
Figure BDA0003030687820000032
And constructing an initial undirected graph using a neural network.
Preferably, the initial undirected graph is constructed from a KNN network.
Step 4.2, fixing the target domain data label FtAnd an undirected graph incidence matrix S, updating a mapping matrix W, wherein the objective function is as follows:
Figure BDA0003030687820000033
and solving the formula (4) to obtain an updated mapping matrix.
Step 4.3, fixing the mapping matrix W and the undirected graph correlation matrix S, and updating a target domain data label Ft, wherein the target function is as follows:
Figure BDA0003030687820000034
and solving the formula (5) to obtain the updated target domain data label.
Step 4.4, fix target domain data tag FtAnd a mapping matrix W, updating an undirected graph correlation matrix S, wherein the objective function is as follows:
Figure BDA0003030687820000035
and solving the formula (6) to obtain an updated undirected graph correlation matrix S.
Step 4.5, repeating steps 4.2, 4.3 and 4.4 for multiple times to complete mapping matrix W and target domain data label FtAnd joint iterative optimization of the undirected graph correlation matrix S.
And 5, inputting the sample matrix X obtained in the step 2 into the objective function subjected to iterative optimization in the step 4 to obtain a corresponding predicted value label, wherein the predicted value label is the emotional state of the testee corresponding to the sample at the acquisition moment.
The invention has the following beneficial effects:
1. the combined electroencephalogram feature migration and emotional state estimation method model provided by the invention provides an effective tool with higher accuracy for emotional man-machine interaction, and the target label is continuously iteratively optimized through the mathematical model, so that the emotional state of the testee can be accurately identified according to electroencephalogram data.
2. Aiming at the situation that the electroencephalogram research field is difficult to cross the tested situation, iteration optimization is carried out by combining the undirected graph construction, the semi-supervised label propagation and the domain adaptation, the undirected graph constructed based on sample data is continuously optimized, a better target label is obtained by the semi-supervised label propagation optimization, a more excellent mapping matrix is obtained, the migration effect is improved, the source domain data and the target domain data which are closer are obtained for composition, and the iteration and the optimization are continuously carried out, so that a more excellent emotion state recognition result is obtained, and the emotion recognition accuracy of the cross-tested migration is improved.
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FIG. 1 is a flow chart of the method.
Detailed Description
The invention is further explained below with reference to the drawings;
as shown in fig. 1, the electroencephalogram emotion recognition method combining feature migration and graph semi-supervised label propagation specifically comprises the following steps:
step 1, electroencephalogram data acquisition.
The human emotion does not appear very strong under daily conditions, therefore, in order to acquire strong emotion information, certain induction needs to be carried out on a human subject, 4 film segments with obvious emotion tendencies are selected to be respectively played to the human subject at different times for watching, and the 4 film segments are connected to corresponding brain areas through brain cap leads while watching a film to acquire brain electrical data of the human subject as an original emotion brain electrical data set.
And 2, preprocessing data.
Sampling the electroencephalogram data acquired in the step 1, wherein the sampling rate is 200Hz, then filtering noise and artifacts by a band-pass filter of 1 Hz-75 Hz, and respectively calculating Differential Entropy (DE) of 5 frequency bands (Delta (1-4Hz), Theta (4-8Hz), Alpha (8-14Hz), Beta (14-31Hz) and Gamma (31-50Hz)) as a sample matrix S:
Figure BDA0003030687820000041
wherein σ is a standard deviation of the probability density function; μ is the expectation of the probability density function.
It can be seen that the differential entropy signature is essentially a logarithmic form of the power spectral density signature, i.e.
Figure BDA0003030687820000042
The preprocessing of the electroencephalogram signals aims to improve the signal-to-noise ratio, so that the preprocessing effect of data is improved, and interference is reduced.
And the label vector y is an emotion label corresponding to the sample matrix X.
Step 3, establishing a combined electroencephalogram feature migration and emotional state estimation method model, integrating a single mapping domain adaptation model and a semi-supervised label propagation model based on an undirected graph into a unified framework, and obtaining a combined optimization objective function, wherein the specific steps are as follows:
step 3.1, establishing a single mapping domain adaptive model:
Figure BDA0003030687820000051
wherein the content of the first and second substances,
Figure BDA0003030687820000052
in order to map the source domain data and the target domain data to a mapping matrix in the same shared subspace, p represents a target dimension, and d represents an original dimension; xs、XtRespectively source domain data and target domain data;
Figure BDA0003030687820000053
c is the category number;
Figure BDA0003030687820000054
unknown in a single domain adaptation to augment target domain data tags, ns、ntThe sample numbers of the source domain data and the target domain data respectively;
Figure BDA0003030687820000055
the matrix is a diagonal matrix, and the kth diagonal element in the matrix is the reciprocal of the data sample number of the kth source domain or the target domain; wherein
Figure BDA0003030687820000056
Is a central matrix, and I is a unit matrix;
Figure BDA0003030687820000057
representing the calculation of the Frobenius norm; the superscript T denotes transpose.
Step 3.2, establishing a semi-supervised label propagation model based on an undirected graph:
Figure BDA0003030687820000058
wherein S isijRepresenting the elements of the ith row and the jth column of the undirected graph correlation matrix S, i.e., the degree of correlation between the ith sample and the jth sample (i 1, 2.., n; j 1, 2.., n); alpha and gamma are parameters;
Figure BDA0003030687820000059
is obtained by a correlation matrix S, L is S-D, D is an n-dimensional diagonal matrix, and the ith diagonal element
Figure BDA00030306878200000510
Figure BDA00030306878200000511
For label matrices for label propagation, Fs、FtLabels for the source domain and the target domain, respectively; 1 is a full 1 matrix; tr (-) denotes the trace of the matrix; Σ (-) is a summation formula;
Figure BDA00030306878200000512
represents the calculation of a 2 norm; the first term in the formula (9) is semi-supervised label propagation, and the second term and the third term are correlation matrixes for solving an undirected graph so as to ensure that a good undirected graph is constructed.
Step 3.3, combining the two models constructed in the steps 3.1 and 3.2, and integrating the two models into a unified framework to obtain a joint optimization objective function:
Figure BDA0003030687820000061
λ, α, γ are parameters.
Step 4, according to the optimization model established in the step 3.3, mapping matrix W and target domain data label FtAnd performing joint iterative optimization on the undirected graph incidence matrix S.
Step 4.1, initialize the data label F of the target domaintIs 0.25 and an initial undirected graph is constructed using a KNN network.
Step 4.2, fixing the target domain data label FtAnd an undirected graph incidence matrix S, updating a mapping matrix W, wherein the objective function is as follows:
Figure BDA0003030687820000062
the objective function is an equality constraint optimization problem, and is calculated by adopting a Lagrangian function to construct the Lagrangian function:
Figure BDA0003030687820000063
where phi is the lagrangian multiplier corresponding to the equality constraint,
Figure BDA0003030687820000064
the offset of W is obtained by equation (12):
Figure BDA0003030687820000065
thus, it is possible to provide
(CCT+2αXLXT)W＝ΦXHXTW (14)
Equation (14) is a generalized eigenvalue decomposition problem, let M ═ XHXT)-1(CCT+2αXLXT) And simultaneously taking W as a group of standard orthogonal basis vectors, performing eigenvalue decomposition on the matrix W, and taking the eigenvectors corresponding to the first p smallest eigenvalues to obtain an updated mapping matrix W. p is the shared subspace dimension.
Step 4.3, fixing the mapping matrix W and the undirected graph correlation matrix S, and updating the target domain data label FtThe objective function is:
Figure BDA0003030687820000066
order to
Figure BDA0003030687820000067
Equation (15) is reduced to:
Figure BDA0003030687820000071
converting equation (16) to the trace form:
Figure BDA0003030687820000072
in equation (17), the target variable exists in two forms, one is augmented and the other is normal, and the augmented form of the target variable is disassembled, i.e., the target variable is
Figure BDA0003030687820000073
In formula (17):
the first item:
Figure BDA0003030687820000074
the second term is:
Figure BDA0003030687820000075
wherein
Figure BDA0003030687820000076
Since the trace of the matrix is the sum of the major diagonal elements and the target variable is FtEquation (17) is thus converted to:
Figure BDA0003030687820000077
wherein the content of the first and second substances,
Figure BDA0003030687820000078
the formula (18) is simplified in a line-by-line solving mode, and is taken
Figure BDA0003030687820000079
Is composed of
Figure BDA00030306878200000710
IthtA column vector, i.e.
Figure BDA00030306878200000711
Is FtI th of (1)tLine row vector, it＝1，2，...，nt。
The first item:
Figure BDA00030306878200000712
the second term is:
Figure BDA00030306878200000713
the third item:
Figure BDA00030306878200000714
wherein
Figure BDA00030306878200000715
I th of MtA column vector; j is a function oft＝1，2，...，nt. At this time, the ithtThe objective function of the row is:
Figure BDA00030306878200000716
in the formula (19), the first term and the third term are both in the form of products, and only the second term is a calculation in the form of 2 norms, the second term is decomposed:
Figure BDA00030306878200000717
due to the fact that
Figure BDA00030306878200000718
Is a constant whose trace is itself, so equation (19) can be written as:
Figure BDA0003030687820000081
wherein the content of the first and second substances,
Figure BDA0003030687820000082
at the same time, get
Figure BDA0003030687820000083
The target formula can be simplified as follows:
Figure BDA0003030687820000084
solving the formula (22) to obtain the updated target domain data label Ft。
Step 4.4, fix target domain data tag FtAnd a mapping matrix W, updating an undirected graph correlation matrix S, wherein the objective function is as follows:
Figure BDA0003030687820000085
and similarly, adopting a line-by-line solving mode, firstly, disassembling a first item trace in the objective function:
Figure BDA0003030687820000086
since the calculation of the two-norm in equation (24) is independent of the target variable, let:
Figure BDA0003030687820000087
then equation (24) is solved in rows, and equation (26) is row i:
Figure BDA0003030687820000088
get
Figure BDA0003030687820000089
Then equation (26) can be written as:
Figure BDA00030306878200000810
order to
Figure BDA00030306878200000811
At this time, equation (27) can be converted into:
Figure BDA00030306878200000812
and solving the formula (28) to obtain the updated undirected graph correlation matrix S.
Step 4.5, repeating steps 4.2, 4.3 and 4.4 for multiple times to complete mapping matrix W and target domain data label FtAnd joint iterative optimization of the undirected graph correlation matrix S.
And 5, inputting the sample matrix X obtained in the step 2 into the objective function subjected to iterative optimization in the step 4 to obtain a corresponding predicted value label, wherein the predicted value label is the emotional state of the testee corresponding to the sample at the acquisition moment.
Figure BDA0003030687820000091
TABLE 1
Figure BDA0003030687820000092
Figure BDA0003030687820000101
TABLE 2
As can be seen from the data in the above two tables, the recognition accuracy of the result of this embodiment is higher than that of other migration methods.

Claims (8)

1. The electroencephalogram emotion recognition method combining feature migration and graph semi-supervised label propagation is characterized by comprising the following steps: the method specifically comprises the following steps:
step 1, acquiring electroencephalogram data of a testee in K different emotional states;
step 2, preprocessing and extracting characteristics of the electroencephalogram data acquired in the step 1, wherein each sample matrix X consists of electroencephalogram characteristics of a testee, and a label vector y is an emotion label corresponding to the electroencephalogram characteristics in the sample matrix X; selecting two different sample matrixes as source domain data X respectivelysAnd target domain data Xt；
Step 3, constructing a learning model for joint feature transfer learning and state estimation, and integrating a single mapping domain adaptation model and a semi-supervised label propagation model based on an undirected graph into a unified frame to obtain a joint optimization objective function;
step 4, firstly initializing a target domain data label FtConstructing and obtaining an initial undirected graph by using a neural network; then according to the target function of the combined optimization obtained in the step 3, a mapping matrix W and a target domain data label F are sequentially subjected to a method of fixing two variables and updating the other variabletOptimizing the undirected graph incidence matrix S, and repeating the optimization process for multiple times to realize joint iterative optimization;
and 5, inputting the sample matrix X obtained in the step 2 into the objective function subjected to iterative optimization in the step 4 to obtain a corresponding predicted value label, wherein the predicted value label is the emotional state of the testee corresponding to the sample at the acquisition moment.
2. The electroencephalogram emotion recognition method combining feature migration and map semi-supervised label propagation as recited in claim 1, wherein: sampling the acquired electroencephalogram data at the frequency of 200Hz, and then filtering noise and artifacts by passing the sampled data through a 1-75 Hz band-pass filter; and then dividing the sample matrix into five frequency bands of 1-4Hz, 4-8Hz, 8-14Hz, 14-31Hz and 31-50Hz, and respectively calculating the differential entropy under each frequency band as the electroencephalogram characteristic in the sample matrix X.
3. The electroencephalogram emotion recognition method combining feature migration and map semi-supervised label propagation as recited in claim 1, wherein: the step 3 specifically comprises the following steps:
step 3.1, establishing a single mapping domain adaptive model:
Figure FDA0003030687810000011
s.t.WTXHXTW＝I (1)
wherein the content of the first and second substances,
Figure FDA0003030687810000012
in order to map the source domain data and the target domain data to a mapping matrix in the same shared subspace, p represents a target dimension, and d represents an original dimension;
Figure FDA0003030687810000013
to augment the source domain data tag, YsIs a source domain label, and c is the number of categories;
Figure FDA0003030687810000014
to augment the target domain data tag, ns、ntThe sample numbers of the source domain data and the target domain data respectively; n is ns+ntRepresents the totalThe number of samples;
Figure FDA0003030687810000021
Figure FDA0003030687810000022
is a diagonal matrix, Ns、NtThe diagonal matrix is a diagonal matrix, wherein a kth diagonal element is an inverse number of data samples of a kth source domain or a kth target domain, and k is 1.2. Wherein
Figure FDA0003030687810000023
Is a central matrix, I is a unit matrix,
Figure FDA0003030687810000024
representing the calculation of the Frobenius norm; superscript T denotes transpose;
step 3.2, establishing a semi-supervised label propagation model based on an undirected graph:
Figure FDA0003030687810000025
Figure FDA0003030687810000026
wherein S isijRepresenting the element of the ith row and the jth column of the undirected graph correlation matrix S, namely the correlation degree between the ith sample and the jth sample; alpha and gamma are parameters;
Figure FDA0003030687810000027
is Laplace transform matrix, L is S-D, D is n dimensional diagonal matrix, the ith diagonal element
Figure FDA0003030687810000028
Figure FDA0003030687810000029
For label matrices for label propagation, Fs、FtLabels for the source domain and the target domain, respectively; 1 is a full 1 matrix; tr (-) denotes the trace of the matrix;
Figure FDA00030306878100000210
represents the calculation of a 2 norm;
step 3.3, combining the two models constructed in the steps 3.1 and 3.2, integrating the domain adaptation and the undirected graph-based semi-supervised label propagation into a unified framework for joint optimization, wherein the optimization model is as follows:
Figure FDA00030306878100000211
Figure FDA00030306878100000212
λ, α, γ are parameters.
4. The electroencephalogram emotion recognition method combining feature migration and map semi-supervised label propagation as recited in claim 1, wherein: step 4, labeling the target domain data FtIs initialized to 1/K.
5. The electroencephalogram emotion recognition method combining feature migration and map semi-supervised label propagation as recited in claim 1, wherein: the initial undirected graph is constructed from the KNN network.
6. The electroencephalogram emotion recognition method combining feature migration and map semi-supervised label propagation as recited in claim 1 or 3, wherein: the specific method of the joint iterative optimization in the step 4 comprises the following steps:
step 4.1, fix target domain data tag FtAnd an undirected graph incidence matrix S, updating a mapping matrix W, wherein the objective function is as follows:
Figure FDA0003030687810000031
Figure FDA0003030687810000032
solving a formula (4) to obtain an updated mapping matrix;
step 4.2, fixing the mapping matrix W and the undirected graph correlation matrix S, and updating the target domain data label FtThe objective function is:
Figure FDA0003030687810000033
s.t.F≥0，F1＝1 (5)
solving a formula (5) to obtain an updated target domain data label;
step 4.3, fix target domain data label FtAnd a mapping matrix W, updating an undirected graph correlation matrix S, wherein the objective function is as follows:
Figure FDA0003030687810000034
Figure FDA0003030687810000035
solving a formula (6) to obtain an updated undirected graph correlation matrix S;
step 4.4, repeating steps 4.1, 4.2 and 4.3 for multiple times to complete mapping matrix W and target domain data label FtAnd joint iterative optimization of the undirected graph correlation matrix S.
7. The electroencephalogram emotion recognition method combining feature migration and map semi-supervised label propagation as recited in claim 6, wherein: in step 4.1, the objective function of the optimized mapping matrix W is solved through a Lagrangian function.
8. The electroencephalogram emotion recognition method combining feature migration and map semi-supervised label propagation as recited in claim 6, wherein: simplifying and optimizing target domain data label F through line solving method in steps 4.2 and 4.3tAnd solving after the objective function of the undirected graph incidence matrix S.
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