Feature screening system for evaluating interference effect of pulse sound on emotion

Abstract

The invention discloses a feature screening system for evaluating an interference effect of pulse sound on emotions, relates to the technical field of cognitive neuroscience, and solves the problem that in the prior art, effective screening of pulse sound features influencing emotions is lacked. The system comprises a sound generation module which is used for performing cross design on independent variables of pulse sound to form various sound stimuli and performing experiments on a plurality of subjects by using the various sound stimuli; the data acquisition module is used for acquiring experimental data in an experimental process in the tested body; the experimental data comprises a physiological signal, an emotion titer score and a wake-up degree score of each subject under each sound stimulation; the data processing module is used for processing experimental data in the experimental process in the subject to obtain a dependent variable of the pulse sound under each sound stimulation; and the data analysis module is used for determining a feature screening result of the pulse sound inducing the emotion by analyzing an association relationship between an independent variable and a dependent variable of the pulse sound under sound stimulation.

Description

Technical Field

[0001] The present invention relates to the technical field of cognitive neuroscience, and particularly to a feature screening system for evaluating the interference effect of pulsed sounds on emotions. Background Art

[0002] Noise or unwanted sounds are important sources of annoyance and pain. With the continuous advancement of urbanization and industrialization, noise has become increasingly common in living and working environments, such as factory noise, traffic noise, automobile noise, air-conditioning noise, etc. In particular, many machines commonly used in the working environment (such as fans, blowers, compressors, etc.) generate low-frequency noise with a frequency below 100 Hz. It is worth noting that a lot of noise exists in the form of pulsed sounds. Previous studies have shown that emotions are vulnerable to the influence of noise, and the higher the noise annoyance level, the worse the emotional state. A poor emotional state will lead to a negative attitude towards problems and lower job performance; if the emotional state is good, the way of looking at problems will be more optimistic, and it is easier to accept the surrounding environment compared to people in other states, and the job performance will also be higher.

[0003] Some studies have shown that the two dimensions of valence and arousal are suitable for subjectively describing the emotional response to sounds. In addition to subjective evaluation, emotions can be expressed through various physiological information, and various information features can be analyzed to evaluate the emotional state of participants. Most studies focus on the analysis of facial expressions or speech, but these types of signals are (more or less) easily forged. In addition, the emotions caused by sounds can also be evaluated through physiological signals of the peripheral nervous system (such as heart rate, electromyogram, skin resistance) and physiological signals of the central nervous system (such as electroencephalogram). Since the subjective indicators for evaluating emotions can distinguish different emotional types, their drawback is that emotions tend to dissipate during introspection and the intensity of emotions will decrease during recall. Therefore, evaluating emotions solely based on subjective experience is not highly accurate; the emotional evaluation based on physiological indicators is not affected by the subjective influence of the measured person, and there will be no problem of the subject intentionally or unintentionally not reporting emotions, but its drawback is that it is not easy to distinguish various different emotions. Therefore, it is more reliable to evaluate emotions through a method combining subjective evaluation and physiological indicators.

[0004] Currently, there are many studies on the influence of sounds on emotions, but there are few studies on the influence of pulsed sounds on emotions, and there is a lack of research on what characteristics of pulsed sounds have a decisive influence on emotions. Therefore, how to effectively screen the characteristics of pulsed sounds that affect emotions, so as to better use pulsed sounds to regulate emotions, is a technical problem that urgently needs to be solved at present. Summary of the Invention

[0005] In view of the above analysis, the embodiments of the present invention aim to provide a feature screening system for evaluating the interference effect of pulsed sounds on emotions, so as to solve the problem in the prior art of lacking effective screening of the characteristics of pulsed sounds that affect emotions.

[0006] The present invention discloses a feature screening system for evaluating the interference effect of pulsed sound on emotions, and the system includes:

[0007] A sound generation module, configured to perform a crossed design on the independent variables of the pulsed sound to form a variety of sound stimuli, and use the variety of sound stimuli to conduct experiments on multiple subjects respectively; the independent variables include sound frequency, repetition frequency, and relative loudness;

[0008] A data acquisition module, configured to acquire experimental data during the within-subject experiment; the experimental data includes physiological signals, emotional valence scores, and arousal scores of each subject under each sound stimulus;

[0009] A data processing module, configured to process the experimental data during the within-subject experiment to obtain the dependent variables of the pulsed sound under each sound stimulus; the dependent variables include average physiological statistical features, average emotional valence scores, and average arousal scores;

[0010] A data analysis module, by analyzing the correlation relationship between the independent variables and the dependent variables of the pulsed sound under the sound stimulus, determines the feature screening result of the pulsed sound that induces emotions.

[0011] On the basis of the above solution, the present invention also makes the following improvements:

[0012] Further, the data acquisition module includes a polysomnograph, a digital keyboard, and a data memory; wherein,

[0013] The polysomnograph is configured to acquire the physiological signals of each subject under each sound stimulus;

[0014] The digital keyboard is configured to acquire the emotional valence scores and arousal scores of each subject under each sound stimulus;

[0015] The data memory is connected to the polysomnograph and the digital keyboard, and is configured to store the physiological signals, emotional valence scores, and arousal scores of each subject under each sound stimulus in combination.

[0016] Further, the data processing module includes a data preprocessing unit and a feature extraction unit; wherein,

[0017] The data preprocessing unit is configured to perform preprocessing on the physiological signals of each subject under each sound stimulus in turn;

[0018] The feature extraction unit is configured to perform feature extraction on the preprocessed physiological signals to obtain the physiological statistical features of the corresponding subject under the corresponding sound stimulus.

[0019] Further, the physiological signals include electrocardiogram signals and corrugator electromyogram signals; among them, in the data preprocessing unit:

[0020] The preprocessing of electrocardiogram signals successively includes: removing baseline drift, removing power frequency noise, and removing irrelevant electromyographic noise.

[0021] The preprocessing of corrugator electromyogram signals successively includes: filtering, removing power frequency noise, rectification, smoothing, and normalization.

[0022] The rectification is used to change all negative half-waves of the corrugator electromyogram signal after removing power frequency noise into positive half-waves, and the amplitude remains unchanged.

[0023] Further, during the experiments on multiple subjects respectively, each sound stimulus is set with N trials, and each trial is used as a condition, where N is a positive integer; all conditions are presented randomly.

[0024] During the playback of the sound stimulus in each trial, the physiological signals of the subject in the corresponding trial are collected.

[0025] After the playback of the sound stimulus in each trial ends, the emotional valence score and arousal score of the corresponding trial are collected.

[0026] Further, the physiological statistical features include heart rate features, heart rate variability features, and corrugator electromyogram features; the average physiological statistical features include average heart rate features, average heart rate variability features, and average corrugator electromyogram features.

[0027] In the feature extraction unit, the following operations are performed:

[0028] Heart rate and heart rate variability are extracted from the preprocessed electrocardiogram signals; the heart rate and heart rate variability of each subject under each sound stimulus in all trials are averaged respectively to obtain the heart rate features and heart rate variability features of the corresponding subject under the corresponding sound stimulus.

[0029] The average value of the corrugator electromyogram amplitude is extracted from the preprocessed corrugator electromyogram signals; the average values of the corrugator electromyogram amplitudes of each subject under each corresponding sound stimulus in all trials are averaged to obtain the corrugator electromyogram features of the corresponding subject under the corresponding sound stimulus.

[0030] The heart rate features, heart rate variability features, and corrugator electromyogram features of all subjects under each sound stimulus are averaged respectively to correspondingly obtain the average heart rate features, average heart rate variability features, and average corrugator electromyogram features under the corresponding sound stimulus.

[0031] Further, the data processing module further includes a scoring processing unit.

[0032] A scoring processing unit is used to average the emotional valence scores and arousal scores of all subjects under each sound stimulus respectively, so as to obtain the average emotional valence score and the average arousal score under the corresponding sound stimulus.

[0033] Furthermore, in the data analysis module, a repeated measures analysis of variance method, or a combination of a multiple linear regression analysis method and a Durbin-Watson test method is used to determine the characteristic screening result of the pulsed sound that induces emotions.

[0034] Furthermore, when the data analysis module uses the repeated measures analysis of variance method to determine the characteristic screening result of the pulsed sound that induces emotions, the following steps are executed:

[0035] Perform a repeated measures analysis of variance on the relationship between the independent variable and the dependent variable of the pulsed sound under various sound stimuli;

[0036] Use a sphericity test method to check whether the repeated measures analysis of variance passes. If the sphericity test fails, correct the degrees of freedom and statistical values in the repeated measures analysis of variance result to update the repeated measures analysis of variance result;

[0037] According to the repeated measures analysis of variance result, evaluate the correlation between the independent variable and the dependent variable of the pulsed sound, and select the independent variable with the greatest impact on emotions in the pulsed signal as the characteristic screening result of the pulsed sound that induces emotions.

[0038] Furthermore, when the data analysis module uses a combination of a multiple linear regression analysis method and a Durbin-Watson test method to determine the characteristic screening result of the pulsed sound that induces emotions, the following steps are executed:

[0039] Use the multiple linear regression analysis method to establish a hierarchical regression model, gradually include the independent variables of the pulsed sound one by one, and examine the relationship between the independent variables and the dependent variables of the pulsed sound under various sound stimuli;

[0040] Taking the R 2 index as a reference, examine the explanatory rate of the independent variable of the pulsed sound under different sound stimuli to the corresponding dependent variable of the pulsed sound; combined with the Durbin-Watson test method, select the independent variable with the greatest impact on emotions in the pulsed signal as the characteristic screening result of the pulsed sound that induces emotions.

[0041] Compared with the prior art, the present invention can at least achieve one of the following beneficial effects:

[0042] The characteristic screening system for evaluating the emotional interference effect of pulsed sounds provided by the present invention can effectively screen the main pulsed sound characteristics that affect emotions, so as to better use pulsed sounds to regulate emotions, and well solve the problem of the lack of effective screening of pulsed sound characteristics that affect emotions in the prior art.

[0043] In the present invention, the above technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent specification. Moreover, some advantages can be made obvious from the specification or understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained from the content specifically pointed out in the specification and the accompanying drawings. Description of the Drawings

[0044] The drawings are only for the purpose of showing specific embodiments and are not considered as a limitation to the present invention. Throughout the drawings, the same reference signs denote the same components.

[0045] Figure 1 It is a schematic structural diagram of a feature screening system for evaluating the emotional interference effect of pulsed sounds provided by an embodiment of the present invention.

[0046] Figure 2 It is a flow chart of an indoor experiment provided by an embodiment of the present invention.

[0047] Figure 3 It is a flow chart of an experimental trial provided by an embodiment of the present invention.

[0048] Figure 4 It is a schematic diagram of the valence comparison of a subject under different sound stimuli provided by an embodiment of the present invention.

[0049] Figure 5 It is a schematic diagram of the arousal comparison of a subject under different sound stimuli provided by an embodiment of the present invention.

[0050] Figure 6 It is a schematic diagram of the comparison of the root mean square (RMS) of the corrugator electromyogram amplitude of a subject under different sound stimuli provided by an embodiment of the present invention.

[0051] Figure 7 It is a schematic diagram of the comparison of the heart rate (BMP) of a subject under different sound stimuli provided by an embodiment of the present invention. Detailed Embodiments

[0052] The following will specifically describe the preferred embodiments of the present invention in conjunction with the accompanying drawings. The accompanying drawings form a part of this application and are used together with the embodiments of the present invention to explain the principles of the present invention, rather than to limit the scope of the present invention.

[0053] A specific embodiment of the present invention discloses a feature screening system for evaluating the emotional interference effect of pulsed sounds. The schematic structure is as Figure 1 shown. The system includes:

[0054] A sound generation module, configured to perform a crossed design on the independent variables of pulsed sounds to form various sound stimuli, and use the various sound stimuli to conduct experiments on multiple subjects respectively; the independent variables include sound frequency, repetition frequency, and relative loudness;

[0055] A data acquisition module, configured to acquire experimental data during the within-subject experiment; during the within-subject experiment, perform a crossed design on the independent variables of pulsed sounds to form various sound stimuli, and use the various sound stimuli to conduct experiments on multiple subjects respectively; the independent variables include sound frequency, repetition frequency, and relative loudness; the experimental data includes the physiological signals, emotional valence scores, and arousal scores of each subject under each sound stimulus;

[0056] A data processing module, configured to process the experimental data during the within-subject experiment to obtain the dependent variables of pulsed sounds under each sound stimulus; the dependent variables include average physiological statistical characteristics, average emotional valence scores, and average arousal scores;

[0057] An analysis module, configured to determine the characteristic screening results of pulsed sounds that induce emotions by analyzing the correlation between the independent variables and the dependent variables of pulsed sounds under sound stimuli.

[0058] It should be noted that in this embodiment, under the within-subject experimental conditions, multiple pulsed sounds are set according to the emotion-inducing scenarios; a crossed design is performed on different sound frequencies, repetition frequencies, and relative loudness to form various sound stimuli. During the experiments on multiple subjects respectively, each sound stimulus is set with N trials, and each trial is used as a condition, where N is a positive integer; all conditions are presented randomly; during the playback of the sound stimulus in each trial, the physiological signals of the subject in the corresponding trial are acquired; after the playback of the sound stimulus in each trial, the emotional valence scores and arousal scores of the corresponding trial are acquired.

[0059] Next, the following specific descriptions are made for each module in this embodiment:

[0060] (1) Sound generation module

[0061] In the specific implementation process, the sound generation module can be implemented by using an audio device, and by adjusting the sound frequency, repetition frequency, and relative loudness of the audio device, the corresponding sound stimuli are formed.

[0062] (2) Data acquisition module

[0063] The data acquisition module includes a polysomnograph, a numeric keypad, and a data memory; where,

[0064] The polysomnograph is configured to acquire the physiological signals of each subject under each sound stimulus;

[0065] A numeric keypad for collecting the emotional valence score and arousal score of each subject under each sound stimulus;

[0066] A data memory, connected to the polygraph and the numeric keypad, for storing the physiological signals, emotional valence scores, and arousal scores of each subject under each sound stimulus in combination.

[0067] It should be noted that considering that the experimental process of each subject under each sound stimulus also includes multiple trials, each component in the data acquisition module can ultimately collect the above information with the trial as the smallest object. That is: the polygraph is used to collect the physiological signals of each trial of each subject under each sound stimulus; the numeric keypad is used to collect the emotional valence score and arousal score of each trial of each subject under each sound stimulus; the data memory, connected to the polygraph and the numeric keypad, is used to store the physiological signals, emotional valence scores, and arousal scores of each trial of each subject under each sound stimulus in combination.

[0068] (3) Data processing module

[0069] The data processing module includes a data preprocessing unit and a feature extraction unit; among them,

[0070] The data preprocessing unit is used to preprocess the physiological signals of each subject under each sound stimulus in turn. The physiological signals include electrocardiogram signals and corrugator electromyogram signals. Among them, in the data preprocessing unit: the preprocessing of electrocardiogram signals includes, in turn: removing baseline drift, removing power frequency noise, and removing irrelevant electromyogram noise; the preprocessing of corrugator electromyogram signals includes, in turn: filtering, removing power frequency noise, rectification, smoothing, and normalization; the rectification is used to change all the negative half-waves of the corrugator electromyogram signal after removing power frequency noise into positive half-waves, and the amplitude remains unchanged.

[0071] The feature extraction unit is used to extract features from the preprocessed physiological signals to obtain the physiological statistical features of the corresponding subject under the corresponding sound stimulus. The physiological statistical features include heart rate features, heart rate variability features, and corrugator electromyogram features; the average physiological statistical features include average heart rate features, average heart rate variability features, and average corrugator electromyogram features. In the feature extraction unit, execute:

[0072] Extract the heart rate and heart rate variability from the preprocessed electrocardiogram signals; average the heart rate and heart rate variability of each subject under each sound stimulus in all trials respectively to obtain the heart rate features and heart rate variability features of the corresponding subject under the corresponding sound stimulus;

[0073] Extract the average value of the electromyogram amplitude of the corrugator supercilii muscle from the preprocessed electromyogram signals of the corrugator supercilii muscle; average the average values of the electromyogram amplitudes of the corrugator supercilii muscle of each subject under each corresponding sound stimulus in all trials to obtain the electromyogram characteristics of the corresponding subject under the corresponding sound stimulus.

[0074] Average the heart rate characteristics, heart rate variability characteristics, and electromyogram characteristics of the corrugator supercilii muscle of all subjects under each sound stimulus respectively, and correspondingly obtain the average heart rate characteristics, average heart rate variability characteristics, and average electromyogram characteristics of the corrugator supercilii muscle under the corresponding sound stimulus.

[0075] In addition, the data processing module further includes a scoring processing unit; the scoring processing unit is used to average the emotional valence scores and arousal scores of all subjects under each sound stimulus respectively to obtain the average emotional valence score and average arousal score under the corresponding sound stimulus.

[0076] (4) Data analysis module

[0077] In the data analysis module, use the repeated measures analysis of variance method, or a combination of the multiple linear regression analysis method and the Durbin-Watson test method to determine the characteristic screening results of the pulsed sound that induces emotions.

[0078] Preferably, when the data analysis module uses the repeated measures analysis of variance method to determine the characteristic screening results of the pulsed sound that induces emotions, perform:

[0079] Conduct a repeated measures analysis of variance on the relationship between the independent variable and the dependent variable of the pulsed sound under various sound stimuli;

[0080] Use the sphericity test method to test whether the repeated measures analysis of variance passes. If the sphericity test fails, correct the degrees of freedom and statistical values in the repeated measures analysis of variance results to update the repeated measures analysis of variance results;

[0081] According to the results of the repeated measures analysis of variance, evaluate the correlation between the independent variable and the dependent variable of the pulsed sound, and select the independent variable that has the greatest impact on emotions in the pulsed signal as the characteristic screening results of the pulsed sound that induces emotions.

[0082] Preferably, when the data analysis module uses a combination of the multiple linear regression analysis method and the Durbin-Watson test method to determine the characteristic screening results of the pulsed sound that induces emotions, perform:

[0083] Use the multiple linear regression analysis method to establish a hierarchical regression model, gradually incorporate the independent variables of the pulsed sound respectively, and examine the relationship between the independent variables and the dependent variables of the pulsed sound under various sound stimuli;

[0084] With R 2Using the index as a reference, examine the explanatory rate of the independent variable of the pulsed sound under different sound stimuli on the dependent variable of the corresponding pulsed sound (the higher the explanatory rate, the more important the independent variable); combined with the Durbin-Watson test method, screen out the independent variable that has the greatest impact on emotion in the pulse signal as the characteristic screening result of the pulsed sound that induces emotion

[0085] To facilitate those skilled in the art to better implement this solution, the present invention also gives the specific implementation process of realizing the screening of pulsed sound characteristics by using the characteristic screening system for evaluating the emotion interference effect of pulsed sound. The flowchart is as Figure 2 shown and is described as follows

[0086] (1) Experimental task design

[0087] This experiment adopts a within-subject experimental design, which refers to a true experimental design in which each or each group of subjects receives experimental treatments at all levels of the independent variable, also known as repeated measures design and within-group design. In this embodiment, the following settings are made according to the scenarios that induce emotions: the sound frequency is set to 5000Hz, 10000Hz, 15000Hz, the repetition frequency is set to 1Hz, 100Hz, 400Hz, and the relative loudness is set to -12db and -24db. The sound pulse width is controlled at 100μs, and a crossed design is carried out for the sound frequency, repetition frequency, and relative loudness, totaling 18 kinds of sound stimuli. The dependent variables of the pulsed noise are the physiological signals of the subjects hearing the sound stimuli, as well as the emotion valence score and arousal score (that is, the scores of the subjects rating the emotion valence and arousal). In the specific implementation process, the emotion valence score and arousal score can be quantified by using a 9-point rating scale. Exemplarily: the valence is a 9-point rating scale, from -4 to 4, where -4 represents very unpleasant and 4 represents very pleasant; the arousal is a 9-point rating scale, from -4 to 4, where -4 represents very calm and 4 represents very excited

[0088] (2) Experimental procedure

[0089] ① First, present the experimental instructions to the subjects, explaining the experimental content and experimental response methods

[0090] ② Second, require the subjects to conduct practice experiments, a total of 10 trials. The process of each trial in the practice experiment is as follows: a fixation point is presented in the center of the screen for 1000ms, and then a blank screen is presented for 1000ms. This form is usually used in psychological and cognitive science research. Presenting a fixation point on the screen is to let the subjects concentrate their attention, and presenting a blank screen is to reduce the interference of other factors on attention and facilitate better listening to the subsequent sound stimuli. Then the sound stimulus is played for 5s. After the sound stimulus is played, the subjects are required to move the mouse to give a 9-point rating to the valence and arousal of the emotion displayed on the screen according to the emotion they feel when hearing the sound stimulus, and then enter the next trial

[0091] ③Finally, after each subject was familiar with the requirements of the practice experiment, they entered the formal experiment. The procedure was the same as that of the practice trials, with 20 trials for each condition. There were a total of 360 trials in the experiment. Each condition was presented randomly. There was a break in the middle of the formal experiment, about 3 - 5 minutes.

[0092] The flow chart of the experimental trials is as Figure 3 shown.

[0093] (3) Data acquisition

[0094] This process corresponds to the data acquisition module. The MP150 multi-channel physiological recorder of the physiological signal BIOPAC collected the physiological data of the subjects at a sampling rate of 1000 Hz, with a high-pass of 0.5 Hz and a low-pass of 35 Hz. The physiological signals of the subjects were recorded during the playback of the sound stimuli, covering the electromyogram of the corrugator supercilii, electrocardiogram signals (heart rate and heart rate variability obtained through post-processing), etc. The data collected by the multi-channel physiological recorder did not include the data during the evaluation stage (i.e., when the subjects were evaluating their emotional feelings towards the sounds they heard) to avoid confusion.

[0095] (4) Data processing

[0096] This process corresponds to the data processing module.

[0097] 1) Preprocessing of electrocardiogram signals:

[0098] ①Removing baseline drift: First, design two median filters. That is, pass the electrocardiogram signal through a 200-ms median filter to remove the QRS complex and P waves; then pass it through a 600-ms median filter to remove the T wave and obtain the drifting baseline; finally, subtract the drifting baseline from the electrocardiogram signal to obtain the signal with baseline drift removed.

[0099] ②Removing power frequency noise: Power frequency interference has a fixed frequency. Use a 50-Hz notch filter to perform notch processing on the signal with baseline drift removed to filter out power frequency interference.

[0100] ③Removing irrelevant electromyogram noise: Irrelevant electromyogram noise (such as some electromyogram noise generated by movements, blinking, or sweating) interference belongs to high-frequency interference, while the frequency of electrocardiogram signals is mainly concentrated in 5 - 20 Hz. Therefore, filter the electrocardiogram signal with power frequency interference removed through a 5 - 20 Hz band-pass filter to reduce the influence of irrelevant electromyogram noise.

[0101] 2) Preprocessing of corrugator supercilii electromyogram signals:

[0102] ①Filtering: Since the spectrum of the corrugator supercilii electromyogram signal is mainly distributed between 28 - 500 Hz, use a filter to perform 28 - 500 Hz band-pass filtering on the corrugator supercilii electromyogram signal;

[0103] ②Removing power frequency noise: A notch filter with a frequency of 50 Hz is used to notch filter the filtered corrugator electromyogram signal to remove power frequency interference;

[0104] ③Rectification: All negative half-waves in the signal are changed to positive half-waves, and the amplitude remains unchanged (i.e., its amplitude is retained).

[0105] ④Smoothing: The moving average method is used to smooth the rectified signal to remove high-frequency noise.

[0106] ⑤Normalization: The smoothed signal is normalized so that its amplitude range is between 0 and 1.

[0107] Subsequently, through the peak detection algorithm, the heart rate and heart rate variability can be extracted from the preprocessed electrocardiogram signal (refer to the existing method, which will not be elaborated here), and the average value of the corrugator electromyogram amplitude can be extracted from the preprocessed corrugator electromyogram signal (that is, all corrugator electromyogram amplitudes in the preprocessed corrugator electromyogram signal are averaged to obtain the average value of the corrugator electromyogram amplitude). By averaging the heart rate and heart rate variability of each subject under each sound stimulus in all trials respectively, the heart rate characteristics and heart rate variability characteristics of the corresponding subject under the corresponding sound stimulus are obtained. By averaging the average values of the corrugator electromyogram amplitudes of each subject under each sound stimulus in all trials, the corrugator electromyogram characteristics of the corresponding subject under the corresponding sound stimulus are obtained. Then, the heart rate characteristics, heart rate variability characteristics, and corrugator electromyogram characteristics of all subjects under each sound stimulus are averaged respectively to obtain the average heart rate characteristics, average heart rate variability characteristics, and average corrugator electromyogram characteristics under the corresponding sound stimulus. In addition, by directly averaging the emotional valence scores and arousal scores of all subjects under each sound stimulus respectively, the average emotional valence score and average arousal score under the corresponding sound stimulus can be obtained.

[0108] (5) Data statistical analysis

[0109] This process corresponds to the data analysis module. Repeated measures analysis of variance is performed on the changes in emotional scores (average emotional valence score, average arousal score) and physiological indicators (average heart rate characteristics, average heart rate variability characteristics, average corrugator electromyogram characteristics) induced by different sound stimuli. When the sphericity test fails, the greenhouse-geisser method is used to correct the degrees of freedom F and the statistical value p. The sphericity test is used to test whether the variances of the differences between different measurements are equal. If the p-value of the sphericity test is less than 0.05, it fails. If the sphericity test fails, the p-value needs to be corrected.

[0110] Secondly, a hierarchical regression model can also be established using the multiple linear regression analysis algorithm. The independent variables of the pulsed sound are gradually incorporated respectively to examine the relationships between different sound stimuli, physiological indicators, and emotion scores (i.e., the relationships between the independent and dependent variables of the pulsed sound under various sound stimuli). At the same time, taking the R 2 index as a reference, the explanatory rates of each sound stimulus on physiological indicators and emotion scores are examined. The Durbin-Watson test method is used to test whether there is an autocorrelation phenomenon in the residual terms. At the same time, collinearity diagnosis is selected to judge whether multiple variables are related to each other and the degree of association. After testing, the data of this study meet all the assumptions of linear regression.

[0111] (6) Research results

[0112] ① Analysis results of emotional valence

[0113] First, a repeated measures analysis of variance of 3 (frequency: 5000hz, 10000hz, 15000hz) × 3 (repetition frequency: 1hz, 100hz, 400hz) × 2 (loudness: -12db, -24db) is performed on the emotional valence data of all subjects.

[0114] The results show that the main effect of frequency (i.e., sound frequency) is significant, with degrees of freedom F(2,38) = 30.16, statistical value p < 0.001, and significant value effect η 2 p = 0.61. After comparing the emotional valence of the subjects under the three frequency conditions, it is found that when the 5000hz sound is presented, the emotional valence level of the subjects is the lowest, and when the 15000hz sound is presented, the emotional valence level of the subjects is the highest. That is, the lower the presented sound frequency, the more negative emotions the subjects show. The main effect of repetition frequency (i.e., the frequency of repetition) is significant, F(2,38) = 53.70, p < 0.001, η 2 p = 0.74. After pairwise comparison of the three repetition frequency conditions, it is found that when the repetition frequency of the pulsed sound is 1hz, the subjects will have positive emotions, but when the repetition frequency of the pulsed sound is 100hz and 400hz, the subjects will have negative emotions, and at 100hz, the emotional valence level is the lowest. The main effect of loudness (i.e., relative loudness) is significant, F(1,39) = 94.29, p < 0.001, η 2 p = 0.71. After comparing the emotional valence of the subjects under the two relative loudness conditions, it is found that when the sound with a loudness of -12db is presented, the subjects will have more negative emotions. The results of the main effects of each variable are shown in Figure 4 as follows.

[0115] The results of multiple regression analysis showed that, after modeling with the loudness index, the degree of explaining the variation of emotional valence was 6%. After incorporating the frequency variable into the model, the degree of explaining the variation of emotional valence was still 6%. After incorporating the sound variable of repetition frequency into the model, the degree of explaining the variation of emotional valence was 44%, and the prediction of emotional valence increased by 38%. It can be seen that the influence of repetition frequency on emotional valence is more significant.

[0116] ② Analysis results of emotional arousal

[0117] The results of repeated measures ANOVA showed that the main effect of frequency was significant, F(2, 38) = 18.32, p < 0.001, η 2 p = 0.49. After further pairwise comparisons of the three conditions, it was found that the arousal level induced by the 5000 hz sound was greater than that of 10000 hz, and the arousal level induced by 10000 hz was greater than that of 5000 hz. The main effect of repetition frequency was significant, F(2, 38) = 35.82, p < 0.001, η 2 p = 0.65. The significant difference in repetition frequency was that when the repetition frequency of the sound was 1 hz, the arousal level of the subjects was the lowest, and when the repetition frequency of the sound was 100 hz, the arousal level of the subjects was the highest. The main effect of loudness was significant, F(1, 39) = 63.37, p < 0.001, η 2 p = 0.62. The specific performance of the subjects under different loudness conditions was that when the sound of -12 db loudness was presented, the arousal degree of the subjects was greater than that when the sound of -24 db loudness was presented. For the results of the main effects of each variable, see Figure 5 。

[0118] The results of multiple regression analysis showed that, after modeling with the loudness index, the degree of explaining the variation of emotional arousal was 4%. After incorporating the frequency variable into the model, the degree of explaining the variation of emotional arousal was still 4%. After further incorporating the repetition frequency variable into the model, the degree of explaining the variation of emotional arousal was 29%, and the prediction degree increased by 25%. Thus, it can be seen that emotional arousal is most affected by the repetition frequency variable.

[0119] ③ Analysis results of the amplitude of the corrugator EMG

[0120] The results of repeated measures ANOVA showed that the main effect of repetition frequency was significant, F(2, 34) = 4.41, p < 0.05, η 2 p = 0.21. After pairwise comparisons, it was found that the amplitude of the corrugator EMG of the subjects under the condition of the repetition frequency sound of 400 hz was greater than that of 1 hz, and when the repetition frequency sound was 1 hz, the EMG amplitude of the subjects was the smallest. The main effect of loudness was also significant, F(1, 35) = 7.06, p < 0.05, η2 p = 0.17. After analyzing the EMG data under different loudness conditions, it was found that when presenting a sound with a loudness of -24 dB, the amplitude of the corrugator supercilii EMG of the subjects was greater than that under a loudness of -12 dB. The main effect of frequency was not significant, F(2,34) = 1.43, p = 0.25, indicating that the differences in the corrugator supercilii muscle changes of the subjects for sounds with different frequencies were not obvious. This shows that the repetition frequency and loudness of the sound can independently affect the changes of the corrugator supercilii muscle, but the effect of frequency is not very obvious. The results of the main effects of each variable are shown in Figure 6 .

[0121] The results of multiple regression analysis showed that loudness could explain 1% of the changes in the corrugator supercilii muscle. After incorporating the frequency variable into the model, the degree of explaining the variation of the corrugator supercilii muscle was still 1%. After adding the repetition frequency variable, it was found that the degree of predicting the changes in the corrugator supercilii muscle was 2%, an increase of 1%. Therefore, loudness and repetition frequency can be used as influencing factors for predicting the changes in the corrugator supercilii muscle.

[0122] ④Analysis results of heart rate (BPM)

[0123] The results of repeated measures analysis of variance showed that the main effect of repetition frequency was significant, F(2,31) = 16.26, p < 0.001, η 2 p = 0.51. Further analysis found that under the condition of 100 hz, the heart rate of the subjects was the fastest, and under the condition of 1 hz, the heart rate of the subjects was the slowest. The main effect of frequency was marginally significant, F(2,31) = 16.26, p = 0.09, η 2 p = 0.15. When the subjects heard a sound of 15000 hz, their heart rate was faster than when they heard 5000 hz, and when they heard 5000 hz, their heart rate was faster than when they heard 10000 hz. However, the main effect of loudness was not significant, F(1,32) = 2.13, p = 0.16, indicating that there were no obvious differences in the heart rate of the subjects when presenting sounds with different loudness. The results of BPM under different experimental conditions are shown in Figure 7 .

[0124] The model established using multiple regression analysis showed that the three variables of loudness, frequency, and repetition frequency were not obvious in independently explaining the changes in heart rate.

[0125] Based on the results of the comprehensive emotional subjective evaluation scale and objective physiological indicators, although multiple characteristics of the pulsed sound can affect emotions, the repetition frequency has the most obvious effect on emotions. Therefore, in the future, the repetition frequency can be mainly selected as the main research factor to regulate emotions.

[0126] In summary, the feature screening system for evaluating the emotional interference effect of pulsed sounds proposed in this embodiment can effectively screen the pulsed sound features that affect emotions, so as to better use pulsed sounds to regulate emotions.

[0127] Those skilled in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium is a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0128] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A feature screening system for evaluating the emotional interference effect of pulsed sounds, characterized in that, The system includes: A sound generation module, which is used to perform a cross-design on the independent variables of the pulsed sound to form a variety of sound stimuli, and use the variety of sound stimuli to conduct experiments on multiple subjects respectively; the independent variables include sound frequency, repetition frequency, and relative loudness; A data acquisition module, which is used to acquire the experimental data during the within-subject experiment; the experimental data includes the physiological signals, emotional valence scores, and arousal scores of each subject under each sound stimulus; A data processing module, which is used to process the experimental data during the within-subject experiment to obtain the dependent variables of the pulsed sound under each sound stimulus; the dependent variables include average physiological statistical characteristics, average emotional valence scores, and average arousal scores; A data analysis module, which determines the characteristic screening results of the pulsed sound that induces emotions by analyzing the correlation between the independent variables and the dependent variables of the pulsed sound under the sound stimulus.

2. The feature screening system for evaluating the emotional interference effect of pulsed sounds according to claim 1, characterized in that, The data acquisition module includes a polygraph, a digital keyboard, and a data memory; wherein, The polygraph is used to acquire the physiological signals of each subject under each sound stimulus; The digital keyboard is used to acquire the emotional valence scores and arousal scores of each subject under each sound stimulus; The data memory is connected to the polygraph and the digital keyboard, and is used to store the physiological signals, emotional valence scores, and arousal scores of each subject under each sound stimulus in combination.

3. The feature screening system for evaluating the emotional interference effect of pulsed sounds according to claim 1 or 2, characterized in that, The data processing module includes a data preprocessing unit and a feature extraction unit; wherein, The data preprocessing unit is used to preprocess the physiological signals of each subject under each sound stimulus in turn; The feature extraction unit is used to extract features from the preprocessed physiological signals to obtain the physiological statistical characteristics of the corresponding subject under the corresponding sound stimulus.

4. The feature screening system for evaluating the emotional interference effect of pulsed sounds according to claim 3, characterized in that, The physiological signals include electrocardiogram signals and corrugator electromyogram signals; wherein, in the data preprocessing unit: The preprocessing of the electrocardiogram signals includes, in turn: removing baseline drift, removing power frequency noise, and removing irrelevant electromyographic noise; The preprocessing of the corrugator electromyogram signals includes, in turn: filtering, removing power frequency noise, rectifying, smoothing, and normalizing; The rectifying is used to change all the negative half-waves of the corrugator electromyogram signals after removing the power frequency noise into positive half-waves, and the amplitude remains unchanged.

5. The feature screening system for evaluating the emotional interference effect of pulsed sounds according to claim 4, characterized in that, During the process of conducting experiments on multiple subjects respectively, each sound stimulus is set with N trials, each trial is used as a condition, and N is a positive integer; all conditions are presented randomly; During the playback of the sound stimulus in each trial, the physiological signals of the subject in the corresponding trial are acquired; After the playback of the sound stimulus in each trial ends, the emotional valence score and arousal score of the corresponding trial are acquired.

6. The feature screening system for evaluating the emotional interference effect of pulsed sounds according to claim 5, characterized in that, The physiological statistical characteristics include heart rate characteristics, heart rate variability characteristics, and corrugator electromyogram characteristics; The average physiological statistical characteristics include average heart rate characteristics, average heart rate variability characteristics, and average corrugator electromyogram characteristics; In the feature extraction unit, execute: Extract the heart rate and heart rate variability from the preprocessed electrocardiogram signals; average the heart rate and heart rate variability of each subject under each sound stimulus in all trials respectively to obtain the heart rate characteristics and heart rate variability characteristics of the corresponding subject under the corresponding sound stimulus; Extract the average value of the corrugator muscle EMG amplitude from the preprocessed corrugator muscle EMG signals; average the average values of the corrugator muscle EMG amplitudes of each subject under each corresponding sound stimulus in all trials to obtain the corrugator muscle EMG characteristics of the corresponding subject under the corresponding sound stimulus. Average the heart rate characteristics, heart rate variability characteristics, and corrugator muscle EMG characteristics of all subjects under each sound stimulus respectively to correspondingly obtain the average heart rate characteristics, average heart rate variability characteristics, and average corrugator muscle EMG characteristics under the corresponding sound stimulus.

7. The feature screening system for evaluating the emotional interference effect of pulsed sounds according to claim 6, characterized in that, The data processing module further includes a scoring processing unit. The scoring processing unit is used to average the emotion valence scores and arousal scores of all subjects under each sound stimulus respectively to obtain the average emotion valence score and the average arousal score under the corresponding sound stimulus.

8. The characteristic screening system for evaluating the emotional interference effect of pulsed sounds according to any one of claims 1-7, characterized in that, In the data analysis module, use the repeated measures ANOVA method, or a combination of the multiple linear regression analysis method and the Durbin-Watson test method to determine the characteristic screening results of the pulsed sound that induces emotions.

9. The characteristic screening system for evaluating the emotional interference effect of pulsed sounds according to claim 8, characterized in that, When the data analysis module uses the repeated measures ANOVA method to determine the characteristic screening results of the pulsed sound that induces emotions, execute: Conduct a repeated measures ANOVA on the relationship between the independent variable and the dependent variable of the pulsed sound under various sound stimuli. Use the sphericity test method to test whether the repeated measures ANOVA passes. If the sphericity test fails, correct the degrees of freedom and statistical values in the repeated measures ANOVA results to update the repeated measures ANOVA results. According to the repeated measures ANOVA results, evaluate the correlation between the independent variable and the dependent variable of the pulsed sound, and select the independent variable that has the greatest impact on emotions in the pulsed signal as the characteristic screening results of the pulsed sound that induces emotions.

10. The characteristic screening system for evaluating the emotional interference effect of pulsed sounds according to claim 8, characterized in that, When the data analysis module uses a combination of the multiple linear regression analysis method and the Durbin-Watson test method to determine the characteristic screening results of the pulsed sound that induces emotions, execute: Use the multiple linear regression analysis method to establish a hierarchical regression model, gradually include the independent variables of the pulsed sound one by one, and examine the relationship between the independent variables and the dependent variables of the pulsed sound under various sound stimuli. Taking R 2 as a reference, the explanatory rate of the independent variable of the pulse sound under different sound stimuli to the dependent variable of the corresponding pulse sound was investigated; combined with the Durbin-Watson test method, the independent variable with the greatest impact on emotion in the pulse signal was selected as the characteristic screening result of the pulse sound that induces emotion.

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