CN111184509A - Emotion-induced electroencephalogram signal classification method based on transfer entropy - Google Patents
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Abstract
The invention discloses an emotion-induced electroencephalogram signal classification method based on transfer entropy. And then selecting 10 channels of electroencephalograms such as Fp1, Fp2, P3, P4, C3, C4, O1, O2, F3 and F4, constructing a transfer entropy relation matrix diagram among the channels by using a correlation algorithm of transfer entropy, and performing feature extraction on the generated transfer entropy matrix relation diagram through a direction gradient histogram. And finally, training and classifying the extracted features by using a support vector machine with a kernel function as a radial basis function, and analyzing and classifying the electroencephalogram signals according to different emotional states. The method can comprehensively, accurately and quickly reflect information interaction among different channels during emotional stimulation, becomes the basis of subsequent classification and analysis, reduces characteristic redundancy, improves classification accuracy and reduces analysis time.
Description
Technical Field
The invention belongs to the field of biological signal processing, and relates to a method for classifying electroencephalogram signals under different emotional stimuli based on transfer entropy.
Technical Field
Electroencephalograms (EEG) are signals generated by spontaneous or rhythmic activity of a brain nerve group recorded through electrodes, and reflect potential changes during activity of a brain functional region nerve cell group. Studies in cognitive psychology and brain neurology indicate that mood development and changes are directly related to the central nervous system of the brain. Emotion recognition is mainly recognized by analyzing the speech, facial expressions, EEG signals and other physiological electrical signals of participants, and EEG signals have the characteristics of good time resolution, non-invasiveness, rapidness and low cost, so that EEG signals become a main method for studying emotion change.
EEG signal features can be generally divided into time domain features, frequency domain features, time-frequency domain features and space domain features, wherein the time domain features mainly comprise some statistics of signals, event-related potentials, power, energy, high-order zero-crossing analysis, fractal dimension and the like, the frequency domain features mainly decompose original EEG signals into frequency bands such as theta (4-8Hz), α (8-13Hz), β (13-30Hz), gamma (30-45Hz) and the like, and then high-order spectra, event-related synchronization, event-related desynchronization, power spectral density and the like are respectively calculated from the frequency bands.
Because the brain is a quite complex nonlinear dynamical system, and the Transfer entropy is an asymmetric measurement method based on transition probability definition, which contains directional and dynamic information, has the characteristics of independence on established models and nonlinear quantitative analysis, and can be used for estimating the functional coupling strength and information Transfer between brain regions, the invention uses the Transfer Entropy (TE) in the nonlinear method as an EEG feature extraction method, constructs a Transfer entropy relation matrix according to the relation between channels, and explores the EEG signal change under different emotional states.
Disclosure of Invention
In order to objectively and effectively analyze and classify EEG signals under different emotional stimuli, the invention provides an emotion induction EEG signal classification method based on transfer entropy by utilizing the EEG signals. Firstly, EEG signals of participants under different emotional stimuli are collected, the collected signals are preprocessed, then a transfer entropy relation matrix is constructed for a selected channel by using transfer entropy, a picture is generated, then feature extraction is carried out on the generated picture by using a Histogram of Oriented Gradients (HOG), and finally, a support vector machine with a kernel function as a radial basis function is used for classifying features. The invention can effectively analyze the change of EEG signals when different emotions are stimulated, and provides an idea for the wide application of subsequent EEG emotion classification and man-machine interaction.
The method mainly comprises the following steps:
step one, different emotions are induced by using different types of visual and auditory stimuli, and multichannel EEG signals of the participants in the period are collected.
Step two, preprocessing the collected different emotion-induced EEG signals as follows:
1) removing baseline drift, ocular artifacts and 50Hz power frequency signals by using EEGLAB, retaining 4-45Hz EEG signals by using a band-pass filter, and down-sampling the collected EEG signals from 512Hz to 128Hz and storing;
2) decomposing each sample into data of 4 frequency bands by using wavelet packet transformation, wherein the data are respectively a theta frequency band (4-8Hz), an α frequency band (8-13Hz), a β frequency band (13-30Hz) and a gamma frequency band (30-45Hz), performing 6-layer decomposition by using a 'db 5' wavelet base, finding out wavelet packet tree nodes respectively corresponding to the 4 frequency bands, and reconstructing wavelet packet coefficients of each frequency band to obtain EEG signal data of the 4 frequency bands;
3) and respectively carrying out dual-density dual-tree complex wavelet transform on the data of the 4 frequency bands to obtain cleaner EEG signals.
Step three, selecting EEG signals of 10 channels including Fp1, Fp2, P3, P4, C3, C4, O1, O2, F3 and F4 from the preprocessed data, constructing a 10 x 10 transfer entropy relation matrix by using transfer entropy, and generating a matrix relation graph after normalizing the two-dimensional relation matrix.
The calculation method of the transfer entropy comprises the following steps: suppose that given two time series X ═ X1,x2,…,xTY ═ Y1,y2,…,yTWhere T is the length of the time series, x1、y1Are respectively the first observed value, x2、y2Respectively, the second observed value and so on; we can get the transfer entropy of Y to X (TE)Y→X) And X to Y Transfer Entropy (TE)X→Y) The formula (1) and (2) are shown as follows:
where n is a discrete time index, τ is the predicted time, and p (·) represents the probability distribution.
And step four, extracting the characteristics of the transfer entropy matrix relation graph generated in the step three by using a Histogram of Oriented Gradients (HOG).
And step five, classifying the features in the step four by using a Support Vector Machine (SVM) with the kernel function as the radial basis function, obtaining optimal parameters by using cross validation and grid search, and training a training set to obtain a classification model with better effect.
Compared with the existing methods for classifying various emotional stimulus EEG signals, the method has the following characteristics:
firstly, in the selection of EEG signal sources, Fp1 and Fp2 channels of frontal lobe, P3 and P4 of apical lobe, C3 and C4 channels of occipital lobe, O1 and O2 channels and F3 and F4 channels of temporal lobe are selected, so that information interaction among different channels during emotional stimulation can be comprehensively, accurately and quickly reflected, and the basis of later classification and analysis is formed.
Secondly, during feature extraction, firstly, features among the selected EEG signal channels are extracted by using transfer entropy, a relation matrix among the channels is constructed, an MATLAB is used for generating a picture after normalization processing is carried out, and then, feature extraction is carried out on the picture by using HOG.
Drawings
FIG. 1 shows a flow chart of EEG signal classification at emotional stimulation;
FIG. 2 shows an EEG signal channel selection diagram;
FIG. 3 shows a schematic wavelet frequency division;
FIG. 4 is a diagram showing (a) a matrix of transfer entropy relationship in a calm state, and FIG. 4(b) a matrix of transfer entropy relationship in a pressure state;
FIG. 5 shows a flow chart for feature extraction using HOG;
FIG. 6 shows the relationship between Cell and Block in HOG feature extraction;
FIG. 7 shows the classification accuracy results of different methods.
Detailed Description
In order to efficiently extract and classify features of an EEG, the present invention primarily improves on EEG feature extraction. The embodiments of the present invention will be described in detail below with reference to the accompanying drawings: the embodiment is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are given.
The overall flow of the emotion-induced EEG signal classification method based on transfer entropy is shown in FIG. 1, and the specific implementation method comprises the following steps:
step one, EEG signals at different emotional stimuli are collected. 32 volunteers were selected for 40 visual and auditory stimulation experiments, and EEG signals of 10 channels, Fp1, Fp2 located in frontal lobe, P3, P4, C3, C4 located in parietal lobe, O1, O2 located in occipital lobe, F3 located in temporal lobe, and F4 located in temporal lobe were collected at the same time, as shown in fig. 2. After the experiment is finished, each volunteer needs to score the 40 visual and auditory stimuli respectively from 1 to 9 in two dimensions of Valence (active-passive) and Arousal (awakened-not-awakened) according to own experience and a Valence-awakening degree two-dimensional emotion model.
Step two, preprocessing the collected different emotion-induced EEG signals as follows:
(1) removing baseline drift, ocular artifacts and 50Hz power frequency signals by using EEGLAB, only retaining 4-45Hz EEG signals by using a band-pass filter, and down-sampling the collected EEG signals from 512Hz to 128Hz and storing;
(2) according to the theory related to psychology and brain science, four nodal rate wave frequency bands of an EEG signal are closely related to the physiological and psychological activities of a person, so that each sample is decomposed into data of 4 frequency bands by wavelet packet transformation, wherein the data are a theta frequency band (4-8Hz), an α frequency band (8-13Hz), a β frequency band (13-30Hz) and a gamma frequency band (30-45Hz), 6 layers of decomposition is carried out by using a 'db 5' wavelet base, wavelet packet tree nodes corresponding to the 4 frequency bands are found out, and the EEG signal data of the 4 frequency bands are obtained after the wavelet packet coefficients of the frequency bands are reconstructed.
(3) The data of these 4 bands are separately subjected to dual-density dual-tree complex wavelet transform to obtain cleaner EEG signals, as shown in FIG. 3. according to the previous research, the bands contain a large number of features related to emotion, so the invention uses the collected EEG signals of β bands to perform analysis research.
And step three, constructing a 10 multiplied by 10 transfer entropy relation matrix for the processed data by using the transfer entropy, and generating a matrix relation diagram after normalizing the two-dimensional relation matrix, as shown in fig. 4.
Step four, using the HOG to perform feature extraction on the transfer entropy matrix relation diagram generated in step three, wherein the technical process is shown in fig. 5, and the specific steps are as follows:
(1) the Gamma space and the color space are normalized. The contrast of the image is adjusted to suppress noise interference.
(2) The image gradients are calculated. The image is processed in the horizontal and vertical directions using a one-dimensional discrete difference template.
(3) A direction histogram is created. The image is divided into several cells (cells), each pixel in a Cell is weighted and projected into a histogram with gradient directions (mapped to a fixed angular range), and then the gradient direction histogram of this Cell can be obtained.
(4) The cells are grouped into larger spatial blocks (blocks) and then each Block is normalized separately. Fig. 6 shows the relationship between blocks and cells. And by analogy, combining the feature vectors of all the units in the block into the HOG feature of the space block.
(5) HOG features were collected. All overlapping blocks in the detection window are collected to obtain HOG features and merged into a final feature vector for classification.
And step five, classifying the features in the step four by using a Support Vector Machine (SVM) with the kernel function as the radial basis function, obtaining optimal parameters by using cross validation and grid search, and training a training set to obtain a classification model with better effect.
This example analyzed EEG signals of 32 participants at audiovisual stimuli satisfying both calm and stress emotions, resulting in a total of 125 sets of matrices of transitive entropy relations at calm and 127 sets of matrices of transitive entropy relations at stress. As shown in fig. 7, comparing the accuracy rates of different methods, it can be seen that after the transfer entropy relationship matrix is obtained, the feature extraction of the transfer entropy relationship matrix map by using the HOG can effectively improve the classification accuracy rate and reveal the relationship between EEG signals of different channels during emotional stimulation.
Claims (1)
1. A sentiment-induced electroencephalogram signal classification method based on transfer entropy specifically comprises the following steps:
step one, inducing different emotions by using different types of visual and auditory stimuli, and collecting multichannel scalp electroencephalogram signals of participants in the period;
step two, preprocessing the collected EEG signals induced by different emotions as follows:
1) removing baseline drift, ocular artifacts and 50Hz power frequency signals by using EEGLAB, retaining 4-45Hz EEG signals by using a band-pass filter, and down-sampling the collected EEG signals from 512Hz to 128Hz and storing;
2) decomposing each sample into data of 4 frequency bands by using wavelet packet transformation, wherein the data are respectively a theta frequency band (4-8Hz), an α frequency band (8-13Hz), a β frequency band (13-30Hz) and a gamma frequency band (30-45Hz), performing 6-layer decomposition by using a 'db 5' wavelet base, finding out wavelet packet tree nodes respectively corresponding to the 4 frequency bands, and reconstructing wavelet packet coefficients of each frequency band to obtain EEG signal data of the 4 frequency bands;
3) respectively carrying out dual-density dual-tree complex wavelet transform on the data of the 4 frequency bands, and carrying out noise elimination on an EEG signal;
selecting EEG signals of 10 channels including Fp1, Fp2, P3, P4, C3, C4, 01, 02, F3 and F4 from the preprocessed data, constructing a 10 x 10 transfer entropy relation matrix by using transfer entropy, and normalizing the two-dimensional relation matrix to generate a matrix relation graph;
the calculation method of the transfer entropy comprises the following steps: suppose that given two time series X ═ X1，x2，…，xTY ═ Y1，y2，…，yTWhere T is the length of the time series, x1、y1Are respectively the first observed value, x2、y2Respectively, the second observed value and so on; obtaining the transfer entropy TE from Y to Xy→XAnd X to Y transfer entropy TEX→YThe formula (1) and (2):
wherein n is a discrete time index, τ is a prediction time, and p (·) represents probability distribution;
step four, extracting the characteristics of the transfer entropy matrix relation graph generated in the step three by using the direction gradient histogram;
and step five, classifying the features in the step four by using a support vector machine with the kernel function as the radial basis function, obtaining the optimal parameters by using cross validation and grid search, and training the training set to obtain a classification model with better effect.
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| CN112244880A (en) * | 2020-09-24 | 2021-01-22 | 杭州电子科技大学 | Emotion-induced electroencephalogram signal analysis method based on variable-scale symbol compensation transfer entropy |
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Application publication date: 20200522 |