阅读说明：本技术 基于脑电信号的视听觉美学评价方法及系统 (Visual and auditory aesthetic evaluation method and system based on electroencephalogram signals ) 是由 李卫东 陈志堂 王立卉 杨翔宇 张旭 匡奕方 曾苏华 于 2021-09-07 设计创作，主要内容包括：本发明提供一种基于脑电信号的视听觉美学评价方法及系统,涉及脑认知神经功能技术领域,包括：采集被试进行审美活动时的原始脑电信号,得到被试的EEG原始数据；对获取到的所述EEG原始数据进行预处理,得到不同条件下同等时长纯净的EEG信号；利用短时傅里叶变换分别计算δ、θ、α、β和γ五个不同频段的能量密度谱,得到不同频段的能量密度谱特征矩阵；利用基于支持向量机的递归特征筛选最优特征进行分类器训练,对参与者的审美主观判断进行分类识别,得到参与者的主观判断结果并通过声音和画面提醒反馈给参与者。本发明能够实现参与者审美判断的高精度识别,实现视觉、听觉审美的客观评价,为审美测评的公平性开拓思路并奠定技术基础。(The invention provides a visual and auditory aesthetic evaluation method and system based on electroencephalogram signals, which relate to the technical field of brain cognitive nerve functions and comprise the following steps: acquiring original EEG signals when the tested person carries out aesthetic activities to obtain original EEG data of the tested person; preprocessing the acquired EEG original data to obtain EEG signals with the same time length and purity under different conditions; respectively calculating energy density spectrums of the five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform to obtain energy density spectrum characteristic matrixes of the different frequency bands; and screening optimal characteristics by using the recursive characteristics based on the support vector machine to train a classifier, classifying and identifying the aesthetic subjective judgment of the participants, obtaining the subjective judgment result of the participants and feeding back the result to the participants through sound and picture reminding. The invention can realize high-precision identification of aesthetic judgment of participants, realize objective evaluation of visual and auditory aesthetics, and lay a technical foundation for developing ideas and laying a foundation for fairness of aesthetic evaluation.)

1. A visual and auditory aesthetic evaluation method based on electroencephalogram signals is characterized by comprising the following steps:

step S1: acquiring original EEG signals when the tested person carries out aesthetic activities to obtain original EEG data of the tested person;

step S2: preprocessing the acquired EEG original data to obtain EEG signals with the same time length and purity under different conditions;

step S3: respectively calculating energy density spectrums of the five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform to obtain energy density spectrum characteristic matrixes of the different frequency bands;

step S4: and screening optimal characteristics by using the recursive characteristics based on the support vector machine to train a classifier, classifying and identifying the aesthetic subjective judgment of the participants, obtaining the subjective judgment result of the participants and feeding back the result to the participants through sound and picture reminding.

2. The visual-auditory aesthetic evaluation method based on electroencephalogram signals according to claim 1, wherein the step S1 includes:

step S1.1: carrying out EEG data acquisition by a NeuSen W332 system by using a 32-lead silver/silver chloride alloy electrode cap placed on the surface of the scalp to obtain an EEG signal of 32 channels when a subject carries out aesthetic subjective evaluation, wherein EEG frequency band filtering is 0.5-100Hz, sampling frequency is 1000Hz, and electrode impedance is kept below 5 kilo-ohm;

step S1.2: in the experimental process, the person is tried to sit on a seat which is about 1m away from the screen, the comfortable state is kept, and the body is prevented from obviously moving.

3. The visual-auditory aesthetic evaluation method based on electroencephalogram signals according to claim 1, wherein the step S2 includes:

step S2.1: reducing the collected original electroencephalogram signals to 200Hz through down-sampling, and removing power frequency interference by adopting a 50Hz wave trap;

step S2.2: removing low-frequency components by adopting a common average reference, intercepting data from the first 0.5s to the first 1.5s after presentation of the stimulation, and analyzing the data in 2 s;

step S2.3: acquiring multi-channel independent components through a fast-ICA algorithm, detecting artifact interference through an Adjust plug-in, setting the artifact interference to zero, and further performing inverse conversion to return time domain signals to remove the artifact interference.

4. The visual-auditory aesthetic evaluation method based on electroencephalogram signals according to claim 1, wherein the step S3 includes:

step S3.1: respectively calculating energy density spectrums of five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform;

step S3.2: and summing the energy density spectrums in the five frequency bands of delta, theta, alpha, beta and gamma respectively to obtain the frequency spectrum characteristics under different aesthetic judgments.

5. The visual-auditory aesthetic evaluation method based on electroencephalogram signals according to claim 1, wherein the step S4 includes:

step S4.1: inputting different aesthetic characteristics of the electroencephalogram into recursive characteristic screening based on a support vector machine, and screening an optimal lead combination to obtain a classification result;

wherein, the cost function of the support vector machine is as follows:

wherein H ═ y_iy_jK(x_i,x_j) The element matrix representing the ith and jth samples, K is the measurement sample x_iAnd x_jA kernel function of similarity, y a class label, λ a lagrange coefficient,represents the transpose of the lambda conjugate;

when the change condition of the cost function after a certain characteristic dimension is removed is measured, keeping lambda unchanged, and obtaining the sequential index of the characteristic as follows:

wherein H (-i) is H after the ith feature is removed; j. the design is a square_iA sequential index, i.e., a degree of importance, representing the jth feature; all the characteristics of one lead are regarded as a whole, namely the weight of the lead is sequenced, the characteristics of the first 1 lead, the first 2 lead, the … lead and the first N lead are respectively taken as the input of a classifier according to the lead sequencing obtained by the process, and the recognition result under the corresponding lead combination is obtained, wherein the lead combination with the highest recognition rate is the optimal combination;

step S4.2: and feeding back the recognized result of the aesthetic subjective judgment classification to the participants through sound and pictures.

6. A visual and auditory aesthetic evaluation system based on electroencephalogram signals is characterized by comprising:

module M1: acquiring original EEG signals when the tested person carries out aesthetic activities to obtain original EEG data of the tested person;

module M2: preprocessing the acquired EEG original data to obtain EEG signals with the same time length and purity under different conditions;

module M3: respectively calculating energy density spectrums of the five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform to obtain energy density spectrum characteristic matrixes of the different frequency bands;

module M4: and screening optimal characteristics by using the recursive characteristics based on the support vector machine to train a classifier, classifying and identifying the aesthetic subjective judgment of the participants, obtaining the subjective judgment result of the participants and feeding back the result to the participants through sound and picture reminding.

7. The visual-auditory aesthetic evaluation system based on electroencephalogram signals according to claim 6, wherein the module M1 comprises:

module M1.1: carrying out EEG data acquisition by a NeuSen W332 system by using a 32-lead silver/silver chloride alloy electrode cap placed on the surface of the scalp to obtain an EEG signal of 32 channels when a subject carries out aesthetic subjective evaluation, wherein EEG frequency band filtering is 0.5-100Hz, sampling frequency is 1000Hz, and electrode impedance is kept below 5 kilo-ohm;

module M1.2: in the experimental process, the person is tried to sit on a seat which is about 1m away from the screen, the comfortable state is kept, and the body is prevented from obviously moving.

8. The visual-auditory aesthetic evaluation system based on electroencephalogram signals according to claim 6, wherein the module M2 comprises:

module M2.1: reducing the collected original electroencephalogram signals to 200Hz through down-sampling, and removing power frequency interference by adopting a 50Hz wave trap;

module M2.2: removing low-frequency components by adopting a common average reference, intercepting data from the first 0.5s to the first 1.5s after presentation of the stimulation, and analyzing the data in 2 s;

module M2.3: acquiring multi-channel independent components through a fast-ICA algorithm, detecting artifact interference through an Adjust plug-in, setting the artifact interference to zero, and further performing inverse conversion to return time domain signals to remove the artifact interference.

9. The visual-auditory aesthetic evaluation system based on electroencephalogram signals according to claim 6, wherein the module M3 comprises:

module M3.1: respectively calculating energy density spectrums of five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform;

module M3.2: and summing the energy density spectrums in the five frequency bands of delta, theta, alpha, beta and gamma respectively to obtain the frequency spectrum characteristics under different aesthetic judgments.

10. The visual-auditory aesthetic evaluation system based on electroencephalogram signals according to claim 6, wherein the module M4 comprises:

module M4.1: inputting different aesthetic characteristics of the electroencephalogram into recursive characteristic screening based on a support vector machine, and screening an optimal lead combination to obtain a classification result;

wherein, the cost function of the support vector machine is as follows:

wherein H ═ y_iy_jK(x_i,x_j) The element matrix representing the ith and jth samples, K is the measurement sample x_iAnd x_jA kernel function of similarity, y a class label, λ a lagrange coefficient,represents the transpose of the lambda conjugate;

when the change condition of the cost function after a certain characteristic dimension is removed is measured, keeping lambda unchanged, and obtaining the sequential index of the characteristic as follows:

wherein H (-i) is H after the ith feature is removed; j. the design is a square_iA sequential index, i.e., a degree of importance, representing the jth feature; all the characteristics of one lead are regarded as a whole, namely the weight of the lead is sequenced, the characteristics of the first 1 lead, the first 2 lead, the … lead and the first N lead are respectively taken as the input of a classifier according to the lead sequencing obtained by the process, and the recognition result under the corresponding lead combination is obtained, wherein the lead combination with the highest recognition rate is the optimal combination;

module M4.2: and feeding back the recognized result of the aesthetic subjective judgment classification to the participants through sound and pictures.

Technical Field

The invention relates to the technical field of brain cognitive nerve functions, in particular to a visual and auditory aesthetic evaluation method and system based on electroencephalogram signals.

Background

The beauty has been a curious topic of people since ancient times. Aesthetic quality assessments have a wide impact on human social activities. The development of the electroencephalogram technology provides a technical means for aesthetic and objective evaluation.

Electroencephalogram (EEG) data is more focused on time-series brain changes and more conforms to aesthetic activities related to thinking or actions, so that characteristics related to aesthetic feelings can be extracted through the EEG data, and objective evaluation of different aesthetic feelings is realized. At present, electroencephalogram technologies are mainly divided into evoked electroencephalogram technologies and spontaneous electroencephalogram technologies. The evoked brain electrical technology is regular brain potential changes generated under the stimulation of external task conditions such as vision, hearing, touch and the like, and comprises event-related potentials (ERP), Steady-state visual evoked potentials (SSVEP) and the like; the spontaneous electroencephalogram technology refers to spontaneous potential changes generated by brain activities under the condition of not applying external stimulation, and comprises resting electroencephalogram, polysomnography and the like. In induced brain electricity, ERP is a cognitive potential related to psychological processes such as perception, thinking, attention, memory, intelligence and the like, is an electrical activity generated by the processes of further processing and processing received information, identifying, distinguishing, expecting and making a judgment on received stimulation and the like by the brain, can be used as an objective index for reflecting the higher nerves of the brain, and is widely applied to research of cognitive functions.

The invention patent with publication number CN110338760A discloses a method for classifying schizophrenia into three categories based on electroencephalogram frequency domain data, which is to obtain an electroencephalogram with LESs external interference as a data source for aided diagnosis of schizophrenia without induction, convert electroencephalogram time domain data into frequency domain data after initializing data processing, perform frequency band division on the electroencephalogram frequency domain data, perform matrix processing on the segmented data respectively to obtain controllable LES features, obtain frequency domain weights with the best classification effect by using a frequency band weight distribution algorithm based on quadratic programming, and classify the first stage, health stage and critical high risk syndrome stage of schizophrenia based on the electroencephalogram frequency domain data by using a support vector machine classification algorithm. By collecting the tested electroencephalogram signal and extracting the frequency domain characteristics, the classification system design of schizophrenia is realized, and the electroencephalogram technology can be used for objective characterization of neural activity.

From the actual process of aesthetic activity, the anatomical structure of the cerebral nervous system and its activity laws are an inseparable whole with the processes and mechanisms of aesthetic cognition. Works with different aesthetic degrees can cause brain-specific nerve responses, however, the existing aesthetic evaluation technology is mostly based on a cognitive behavior questionnaire mode and lacks the objectivity of evaluation criteria, so that the need of providing an aesthetic objective evaluation system is high.

Disclosure of Invention

Aiming at the defects in the prior art, the invention provides a visual and auditory aesthetic evaluation method and system based on electroencephalogram signals.

According to the visual and auditory aesthetics evaluation method and system based on the electroencephalogram signals, the scheme is as follows:

in a first aspect, a visual and auditory aesthetic evaluation method based on electroencephalogram signals is provided, and the method comprises the following steps:

step S1: acquiring original EEG signals when the tested person carries out aesthetic activities to obtain original EEG data of the tested person;

step S2: preprocessing the acquired EEG original data to obtain EEG signals with the same time length and purity under different conditions;

step S3: respectively calculating energy density spectrums of the five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform to obtain energy density spectrum characteristic matrixes of the different frequency bands;

step S4: and screening optimal characteristics by using the recursive characteristics based on the support vector machine to train a classifier, classifying and identifying the aesthetic subjective judgment of the participants, obtaining the subjective judgment result of the participants and feeding back the result to the participants through sound and picture reminding.

Preferably, the step S1 includes:

step S1.1: carrying out EEG data acquisition by a NeuSen W332 system by using a 32-lead silver/silver chloride alloy electrode cap placed on the surface of the scalp to obtain an EEG signal of 32 channels when a subject carries out aesthetic subjective evaluation, wherein EEG frequency band filtering is 0.5-100Hz, sampling frequency is 1000Hz, and electrode impedance is kept below 5 kilo-ohm;

step S1.2: in the experimental process, the person is tried to sit on a seat which is about 1m away from the screen, the comfortable state is kept, and the body is prevented from obviously moving.

Preferably, the step S2 includes:

step S2.1: reducing the collected original electroencephalogram signals to 200Hz through down-sampling, and removing power frequency interference by adopting a 50Hz wave trap;

step S2.2: removing low-frequency components by adopting a common average reference, intercepting data from the first 0.5s to the first 1.5s after presentation of the stimulation, and analyzing the data in 2 s;

step S2.3: acquiring multi-channel independent components through a fast-ICA algorithm, detecting artifact interference through an Adjust plug-in, setting the artifact interference to zero, and further performing inverse conversion to return time domain signals to remove the artifact interference.

Preferably, the step S3 includes:

step S3.1: respectively calculating energy density spectrums of five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform;

step S3.2: and summing the energy density spectrums in the five frequency bands of delta, theta, alpha, beta and gamma respectively to obtain the frequency spectrum characteristics under different aesthetic judgments.

Preferably, the step S4 includes:

step S4.1: inputting different aesthetic characteristics of the electroencephalogram into recursive characteristic screening based on a support vector machine, and screening an optimal lead combination to obtain a classification result;

wherein, the cost function of the support vector machine is as follows:

wherein, Hy_iy_jK(x_i,x_j) The element matrix representing the ith and jth samples, K is the measurement sample x_iAnd x_jA kernel function of similarity, y a class label, λ a lagrange coefficient,representing the transpose of the lambda conjugate.

When the change condition of the cost function after a certain characteristic dimension is removed is measured, keeping lambda unchanged, and obtaining the sequential index of the characteristic as follows:

wherein H (-i) is H after the ith feature is removed; j. the design is a square_iA sequential index, i.e., a degree of importance, representing the jth feature; all features of a lead are considered as a whole, i.e. the weights of the leads are ordered. And according to the lead sequencing obtained by the process, respectively taking the characteristics of the first 1 lead, the first 2 lead, … and the first N lead as the input of a classifier to obtain the recognition result under the corresponding lead combination, wherein the lead combination with the highest recognition rate is the optimal combination.

Step S4.2: and feeding back the recognized result of the aesthetic subjective judgment classification to the participants through sound and pictures.

In a second aspect, there is provided a visual-auditory aesthetic evaluation system based on electroencephalogram signals, the system comprising:

module M1: acquiring original EEG signals when the tested person carries out aesthetic activities to obtain original EEG data of the tested person;

module M2: preprocessing the acquired EEG original data to obtain EEG signals with the same time length and purity under different conditions;

module M3: respectively calculating energy density spectrums of the five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform to obtain energy density spectrum characteristic matrixes of the different frequency bands;

module M4: and screening optimal characteristics by using the recursive characteristics based on the support vector machine to train a classifier, classifying and identifying the aesthetic subjective judgment of the participants, obtaining the subjective judgment result of the participants and feeding back the result to the participants through sound and picture reminding.

Preferably, the module M1 includes:

module M1.1: carrying out EEG data acquisition by a NeuSen W332 system by using a 32-lead silver/silver chloride alloy electrode cap placed on the surface of the scalp to obtain an EEG signal of 32 channels when a subject carries out aesthetic subjective evaluation, wherein EEG frequency band filtering is 0.5-100Hz, sampling frequency is 1000Hz, and electrode impedance is kept below 5 kilo-ohm;

module M1.2: in the experimental process, the person is tried to sit on a seat which is about 1m away from the screen, the comfortable state is kept, and the body is prevented from obviously moving.

Preferably, the module M2 includes:

module M2.1: reducing the collected original electroencephalogram signals to 200Hz through down-sampling, and removing power frequency interference by adopting a 50Hz wave trap;

module M2.2: removing low-frequency components by adopting a common average reference, intercepting data from the first 0.5s to the first 1.5s after presentation of the stimulation, and analyzing the data in 2 s;

module M2.3: acquiring multi-channel independent components through a fast-ICA algorithm, detecting artifact interference through an Adjust plug-in, setting the artifact interference to zero, and further performing inverse conversion to return time domain signals to remove the artifact interference.

Preferably, the module M3 includes:

module M3.1: respectively calculating energy density spectrums of five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform;

module M3.2: and summing the energy density spectrums in the five frequency bands of delta, theta, alpha, beta and gamma respectively to obtain the frequency spectrum characteristics under different aesthetic judgments.

Preferably, the module M4 includes:

module M4.1: inputting different aesthetic characteristics of the electroencephalogram into recursive characteristic screening based on a support vector machine, and screening an optimal lead combination to obtain a classification result;

wherein, the cost function of the support vector machine is as follows:

wherein, Hy_iy_jK(x_i,x_j) The element matrix representing the ith and jth samples, K is the measurement sample x_iAnd x_jA kernel function of similarity, y a class label, λ a lagrange coefficient,representing the transpose of the lambda conjugate.

When the change condition of the cost function after a certain characteristic dimension is removed is measured, keeping lambda unchanged, and obtaining the sequential index of the characteristic as follows:

wherein H (-i) is H after the ith feature is removed; j. the design is a square_iA sequential index, i.e., a degree of importance, representing the jth feature; all features of a lead are considered as a whole, i.e. the weights of the leads are ordered. According to the lead sorting obtained by the above process, the characteristics of the first 1 lead, the first 2 lead, … and the first N lead are respectively taken as the input of the classifier to obtain the recognition result under the corresponding lead combination,wherein the lead combination with the highest recognition rate is the optimal combination.

Module M4.2: and feeding back the recognized result of the aesthetic subjective judgment classification to the participants through sound and pictures.

Compared with the prior art, the invention has the following beneficial effects:

1. according to the method, a classifier is constructed by extracting specific nerve response characteristics caused by visual aesthetics and auditory aesthetics, and different tested aesthetic responses are classified and identified and feedback results are output;

2. aiming at the problems of feature redundancy, deficiency and the like, the invention adopts a recursive feature screening algorithm based on a support vector machine to optimize feature combinations, divides aesthetic judgment of participants into aesthetic, general and non-aesthetic and corresponding brain wave signals according to prior experimental data to train a classifier, and realizes high-precision identification of the aesthetic judgment of the participants;

3. the invention can realize the objective evaluation of visual and auditory aesthetics, is expected to develop the idea of fairness of aesthetic evaluation and lay a technical foundation;

4. at the same time, the present invention is also expected to enrich the treatment of psychotherapy in psychiatric patients based on aesthetics.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:

FIG. 1 is a schematic diagram of the overall structure of a visual and auditory aesthetic evaluation system based on electroencephalogram signals;

FIG. 2 is a flow chart of an experimental paradigm in accordance with the present invention.

Detailed Description

The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.

The embodiment of the invention provides a visual and auditory aesthetic evaluation method based on electroencephalogram signals, which is mainly used for preprocessing electroencephalogram data and identifying aesthetic judgment of participants, extracting specific neural response frequency domain characteristics related to aesthetic by collecting electroencephalogram signals when a tested person observes works or listens music, training a classifier, carrying out classification and identification on the aesthetic judgment of the tested person, outputting subjective judgment of the tested person through voice feedback and picture prompt, and realizing objective evaluation of the aesthetic judgment of the tested person. The method comprises a signal acquisition module, a signal processing module, a mode identification module and an output feedback module, and as shown in fig. 1, the method specifically comprises the following steps:

step S1: acquiring original EEG signals when the tested person carries out aesthetic activities to obtain original EEG data of the tested person;

the step S1 includes: EEG data acquisition was performed by the NeuSen W332 system using a 32-lead silver/silver chloride (Ag/AgC1) alloy electrode cap placed on the scalp surface to acquire 32-channel EEG signals of a subject under aesthetic subjective evaluation, EEG band filtering was 0.5-100Hz, sampling frequency was 1000Hz, and all electrode impedances were kept below 5 kilo-ohms with reference to the tip of the nose.

In the experimental process, a person is tried to sit on a seat which is about 1m away from a screen, the comfortable state is kept, and the body is prevented from obviously moving as far as possible. The experimental flow of a single trial is shown in fig. 2, and comprises a total of 4 stages with random durations of 6.75-7.25 s. Firstly, the participants have a rest time of 5s and keep a good state; the first stage is a preparation period, wherein a plus sign appears in the center of the screen, and the screen lasts for 0.25-0.5s to remind the tested sample round of formal start of the experiment; the next 2s time is a prompt period, and an appreciated picture or played music appears in the middle of the screen; then, in a judgment period lasting for 3s, the subject is subjected to subjective judgment according to the picture observed in the previous stage or the music listened to; and finally, a rest period lasts for 1.5-1.75s, and the rest state of the test is adjusted to prepare the next experiment. The whole experiment is completed in a quiet and non-interfering environment.

The experiment totally collected 6 groups of data (3 groups of pictures and music each), each group of experiments contained 60 single round tasks, and three aesthetic degree tasks appeared 20 times at random. The first 2 sets of experimental data were used to train the classifier, and the last 1 set of experiments were used to test the performance of the system on-line. The whole experiment lasts for 60 minutes, and electroencephalogram signals under 6 x 60-360 experimental tasks, namely 360 electroencephalogram data samples, are collected.

Step S2: preprocessing the acquired EEG raw data, including data down-sampling, denoising, intercepting, filtering and the like, to obtain EEG signals with the same time length and purity under different conditions.

Step S2 specifically includes: reducing the collected original electroencephalogram signals to 200Hz through down-sampling, and removing power frequency interference by adopting a 50Hz wave trap; low frequency components were removed using a Common Average Reference (CAR), and data were captured for 2s from the first 0.5s to the first 1.5s after presentation of the stimulus.

Acquiring multi-channel independent components through a fast-ICA algorithm, detecting artifact interference through an Adjust plug-in, setting the artifact interference to zero, and further performing inverse conversion to return time domain signals to remove the artifact interference. Adjust is a fully automated algorithm for identifying and cleaning EEG data.

Step S3: and respectively calculating energy density spectrums of five different frequency bands of delta (0.5-4Hz), theta (4-8Hz), alpha (8-13Hz), beta (14-30Hz) and gamma (30-50Hz) by utilizing short-time Fourier transform to obtain energy density spectrum characteristic matrixes of different frequency bands.

Step S3 specifically includes:

respectively calculating energy density spectrums of five different frequency bands of delta, theta, alpha, beta and gamma by using short-time Fourier transform;

the short-time fourier transform is obtained by first observing the signal x (t) using a finite-width observation window function w (t), and then fourier transforming the windowed signal:

wherein t, τ represents a time point; w is the angular frequency; x (τ) represents the signal at time τ; j represents an imaginary number; w^*(τ -t) is the complex conjugation function of W (τ -t)And (4) counting. When the observation window with limited value length is translated along the time axis, the information of the frequency spectrum distribution of the signals changing along with the time can be obtained on a two-dimensional time-frequency plane, and thus, the two-dimensional time-frequency map of the electroencephalogram signals can be obtained.

The energy density spectrum Spectra (SPEC) is calculated as follows:

SPEC(t,w)＝|STFT(t,w)|² (2)

the window function w (t) is generally a symmetrical real function, and the commonly used window functions include a rectangular window, a hanning window, a gaussian window, etc., in this embodiment, the gaussian window is selected as the time window function, and the window length is 2 s.

And summing the energy density spectrums in the five frequency bands of delta, theta, alpha, beta and gamma respectively to obtain the frequency spectrum characteristics under different aesthetic judgments.

Step S4: and screening optimal characteristics by using the recursive characteristics based on the support vector machine to train a classifier, classifying and identifying the aesthetic subjective judgment of the participants, obtaining the subjective judgment result of the participants and feeding back the result to the participants through sound and picture reminding.

Step S4 specifically includes:

inputting different aesthetic characteristics of the electroencephalogram into recursive characteristic screening based on a support vector machine, and screening an optimal lead combination to obtain the most reliable classification result;

recursive feature screening based on a support vector machine is a commonly used feature optimization algorithm, and the basic idea is as follows: the order of the features to the classifier weights (called sequential index of the features) is measured according to the variation of the classifier cost function after removing a certain one-dimensional feature, and the larger the variation is caused, the larger the proportion of the feature in the contribution of all the features is, and vice versa.

Wherein, the cost function of the support vector machine is as follows:

wherein, Hy_iy_jK(x_i,x_j) The element matrix representing the ith and jth samples, KIs a measure of the sample x_iAnd x_jA kernel function of similarity, y a class label, λ a lagrange coefficient,representing the transpose of the lambda conjugate.

When the change condition of the cost function after a certain characteristic dimension is removed is measured, keeping lambda unchanged, and obtaining the sequential index of the characteristic as follows:

wherein H (-i) is H after the ith feature is removed; j. the design is a square_iA sequential index, i.e., a degree of importance, representing the jth feature;

all features of a lead are considered as a whole, i.e. the weights of the leads are ordered. And according to the lead sequencing obtained by the process, respectively taking the characteristics of the first 1 lead, the first 2 lead, … and the first N lead as the input of a classifier to obtain the recognition result under the corresponding lead combination, wherein the lead combination with the highest recognition rate is the optimal combination.

And feeding back the recognized result of the aesthetic subjective judgment classification to the participants through sound and pictures.

The embodiment provides a visual and auditory aesthetic evaluation method and system based on electroencephalogram signals. And a recursive feature screening algorithm based on a support vector machine is adopted, feature combinations are optimized, aesthetic judgment of participants is divided into aesthetic, general and non-aesthetic and corresponding brain wave signals according to prior experimental data, a classifier is trained, and high-precision identification of the aesthetic judgment of the participants is realized. The invention can realize the objective evaluation of visual and auditory aesthetics, is expected to develop the idea of fairness of aesthetic evaluation and lays a technical foundation. At the same time, the invention is expected to enrich the treatment of psychotherapy in psychiatric patients based on aesthetics.

Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.

The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.