Music-induced Emotions and Musical Regulation and Emotion Improvement Based on EEG Technology

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1 Music-induced Emotions and Musical Regulation and Emotion Improvement Based on EEG Technology Xiaoling Wu 1*, Guodong Sun 2 ABSTRACT Musical stimulation can induce emotions as well as adjust and improve emotions, however, for scientific experimental researches on music-induced emotions and music regulation and emotion improvement, it s not enough just to give proof through scientific experiments, what is more important is further explanation and verification. Further, it is to study in-detail of the impact of music stimuli on the brain and neural mechanism, and how this impact effects the physiological function system of the brain, thus explaining the physiological function system of the human brain. Under the stimulating effect of music, the possible functional changes and the possible outcomes can be used to study the role of music in the clinical treatment and its possible function of musical neurological medical interventions. In this paper, the music fragments of four kinds of emotions "soothing", "happy", "sorrowful", and "nervous" are selected as stimuli, the EEG is recorded synchronously and analysis is conducted from aspects of brain regions, emotional intensity, and polarity etc. 379 Key Words: Cognitive Neuroscience, Emotion Improvement, Musical Stimulation, EEG DOI Number: /nq NeuroQuantology 2018; 16(6): Introduction Music and emotions have an ancient and close connection, but what is the brain mechanism of music affecting emotions? This issue has attracted more and more researchers' interest. In recent years, cognitive neuroscience research on emotions has developed rapidly (Zhao et al., 2016). In particular, after applying high spatial resolution, non-invasive brain imaging techniques such as functional magnetic resonance imaging (fmri) to the study of emotions, the researchers could directly observe the brain activity of the subjects when they are completing cognitive tasks without damaging the brain, which directly connects the cognitive process with the brain's activity process, greatly improving the intuitiveness and depth of the research results. The application of noninvasive brain functional imaging technology makes the study of brain mechanism become a very active field of emotional research in the past 10 years (Xun et al., 2009) The study of the relationship between music and emotion has an important theoretical significance. Music can induce many different emotions. For most people, the main motivation for listening to music is emotion induction, namely to use music to induce emotions, and it has many advantages. On the one hand, music can induce emotions with high intensity, and the emotions induced by music have a high degree of consistency with the subjects; on the other hand, music can not only induce unpleasant emotions, but also can induce pleasant emotions, which has Corresponding author: Xiaoling Wu Address: 1 Art Department, Handan University, Handan , China; 2 School of Earth Science and Engineering, Hebei University of Engineering, Handan , China wutong510@126.com Relevant conflicts of interest/financial disclosures: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Received: 3 March 2018; Accepted: 21 April 2018

2 more advantageous than using static images to induce emotions (Hou et al., 2010). As shown in Figure 1, cognitive neuroscience believes that the generation of human emotions originates from the intuitive processing of various internal and external stimuli. However, the specific brain mechanism of the entire process of emotion induction is not clear yet, therefore, using music to induce emotions provides a unique perspective to answer this question. Figure 1. The papes circuit theory of the functional neuroanatomy of emotion EEG signal is a comprehensive reflection of the activity of human brain neurons, which contains abundant physiological and pathological information. Music is the crystallization of human wisdom and has a major influence on people's mental state. This influence is carried out through the activity of neurons. Therefore, this effect can be further understood through the study of EEG signals (Yu et al., 2014). In general, the state of the brain can be reflected to some extent by the amplitude frequency of brain waves, therefore, at present, internationally, brain waves are divided according to frequency. The EEG study found that happy and cheerful music clips can more significantly activate the left frontal brain regions, while fear and sad music clips more strongly activate the right frontal brain regions. Sammler et al. found that, compared with unpleasant music, pleasant music can cause more θ waves in the forehead area. This effect reflects the close interaction between the emotional processing and the attentional functions (Lu et al., 2012). Related domestic research found that: the change of EEG α wave power is closely related to the polarity and intensity of emotion (Wang et al., 2010). In the treatment of positivepolarity emotions, the α power in the left frontal area is lower than the α power in the right frontal area, and when the negative-polarity emotion is processed, the α power in the left frontal area is greater than the α power in the right frontal area (Cheng et al., 2015). In addition, the EEG α power in the frontal area and the parietal-occipital area will increase as the emotion intensity of the music decreases. EEG records the brain's self-generating potential; its harmonic components are quite complex. Because of the complexity of musicinduced emotional processes, the use of EEG in this field is relatively less. At present, EEG research on musical emotions is still in early stages, and the results are dispersed. In this paper, music clips of four kinds of emotions soothing, happy, sorrowful, and nervous are chosen as stimuli, and using EEG to record and analyze. Methods Research objects Subjects: In the experiment, we randomly selected 50 company employees who have not received systematic professional music training and are right-handed, their characteristics are shown in Table 1. Table 1. Sample parameters Size of Gender Age Sample Male Female Record of formal schooling Secondary school, junior college, undergraduate. Music materials: The experiment selected online music clips with emotions of sorrowful, happy, nervous and soothing. Through music editing software, above music clips were processed and linked up and compiled into a music clip with a length of 10 minutes. Experimental Equipment: The EEG signal was recorded by an EGI 200 EEG recorder, which collect EEG signals by placing a 128-channel electrode cap on the subject's head. The electrode position was placed according to the GSN-128 standard, and the electrode impedance was adjusted to 40 ΚΩ or less. In the case of non invasive in vitro and no energy output to the human body, conduct EFG tests for music stimulation experiments to the subject groups, extract the power and relative power of 9 neurotransmitters in the 12 brain regions of the left and right hemispheres (left and right 380

3 frontalarea, area centralis, parietal region, occipital region, anterior temporal region, posterior temporal region), and then perform medical statistics analysis. Using Huawei Glory 9 Mobile Phones to connect with Hivi Audio M50W multimedia speakers to play the music selected by the experiment. In order to reduce the interference of music intensity factors on the detection of brain wave fluctuations, the intensity levels of the music were pre-adjusted to make the subjects feel comfortable and soft before the experiment (about 50db or so). Experimental process Data collection: Using five EFG devices, 50 subjects were divided into 10 groups of 5, and each group was tested for resting state and musical stimulation of EFG. Experiment a: Play the soothing music for 10 minutes, rest for 1 minute, then play the nervous music for 10 minutes, and let the subjects score the music they listened to during the test, the four score items respectively are: sorrowful (0-9), happy (0-9), nervous (0-9), soothing (0-9), after scoring, differential emotion scale (DES) is obtained, and the intensity of the music (0-9) was also scored during this process. EFG records: before the test, the subjects were forbidden to take excitatory drinks, medications or pain-killers within hours prior to testing. During the test, there were no high-power electrical appliances within 20 meters of the subjects, 16 detection electrodes were placed on the subjects scalp according to international brain electricity array system, 10 minutes of EEG signals and changes in brain wave power were recorded and processed by computers. Data analysis: Extract features from the EEG signals: Brainwave power spectrum mean value X : X 1 n Xi n i = 1 = (1) Brainwave power spectrum variance V: n 1 2 V = ( X X) i n i = 1 (2) Brainwave power range R: R = X X max Xi p( ) = F mi n Brainwave power ratio p(w): (3) (4) Where, F is the total band power value. The gravity frequency of the brain wave power spectrum GF is: GF = 2 = 1 2 = 1 p( ) p( ) (5) Where, w 1 and w 2 are the upper and lower limits of the total frequency band, respectively. Results and discussion DES measurement results of emotions From the emotion measurement results shown in Figure 2 and Figure 3, we can see that the music stimuli used in the experiment can effectively induce four kinds of basic emotions: nervous, soothing, happy and sorrowful. The distribution of the four music clips emotional polarity and intensity is appropriate, which meets the experimental requirements. In the design of the experiments, we also noticed that when the emotion was measured by the method of subjects self-reporting, because there may be emotional components other than the experimental purpose, therefore, if only the target emotions of the experiment were asked, it cannot fully prove that this emotion has effectively occurred. For this reason, in-depth EFG testing is necessary. Subject's brain wave mean value After multi-factor analysis of variance of repeated measures and the sphericity test, we can know that p < 0.05, so we use the Greenhouse-Geisser model. There is a significant difference in the gravity frequency of EEG between different music types, among which, the GF of soothing music is the highest, which is significantly higher than that of the resting state and other music types (as shown in Figure 4). 381

4 Table 2. EEG power spectrum of different types of s Resting state Nervous Soothing Happy Sorrowful α ± 10.82± 12.76± 13.12± 10.36± α ± 11.61± 12.83± 13.46± 11.22± θ 12.83± 11.13± 12.89± 12.94± 11.09± β 12.98± 11.35± 12.93± 13.01± 11.13± Figure 2. The DSE value analysis of different types of s Figure 3. The polarity analysis of different types of s Figure 4. The mean value of the gravity frequency of different types of s EEG power spectrum Since δ waves usually appear in normal adult slow-wave sleep, their physiological significance is not obvious, previous studies have shown that in the music-induced EEG experiment, the changes of δ waves were not obvious, so we only analyze the power spectrum of α 1 (α low band), α 2 (α high band), θ, and β waves, the results are shown in Table 2. The brainwave powers of α 1 wave of three kinds of music and resting state are significantly higher than that of the sorrowful music, followed by the nervous music, indicating that the α lowfrequency energy of negative music is lower than the other two kinds of music. The main effects of gender and brain regions were also significant: α 1 energy of females was higher than that of males; the temporal lobe α 1 wave PS is significantly lower than other brain regions; the difference between left and right hemispheres was not significant, but left hemisphere was lower than right hemisphere. The α 2 wave PS of three kinds of music and resting state are significantly higher than that of the sorrowful music, and happy music > soothing music > nervous music > sorrowful music. Gender differences were significant, with female α2 wave PS values higher than that of the male. There are similar laws for θ and β waves. Changes in EEG α wave power Frontal area: In the analysis process, emotional polarity (positive, negative), intensity (strong, weak), cerebral hemisphere (left, right) were used as within-group factors, and three-factor analysis of variance of repeated measures was performed on the frontal areas (F3, F4), and no significant difference was found. However, in the further analysis of the differences between the means of data, the following trends were found: in the positive-polarity and negative-polarity emotions, the α power of strong-intensity emotions are less than that of the weak-intensity emotions; in the strong-intensity and weak-intensity emotions, the α power of the positive-polarity music is relatively greater than that of the negative-polarity music. These trends indicate that the EEG α power of varies in the distribution according to the polarity and intensity of music emotions, the α power of positive-polarity emotions is relatively greater than that of negative-polarity emotions, and the α 382

5 power of strong-intensity emotions is relatively less than that of the weak-intensity emotions. In order to analyze the overall differences, the α powers of the right and left hemispheres of the frontal area and the parietal-occipital region were respectively added to re-construct the data table, obtaining the data shown in Figure 5, which indicates that in the frontal area, both the positive and negative polarity emotions have strongintensity α powers that are relatively weaker than that of the weak-intensity ones. emotions; in the analysis of the total α power of the frontal and parietal-occipital areas, there were nervous <happy, nervous<soothing and other significant differences. Compared with the data except for the nervous state, there is no significant difference between the other states. The positive stimulus tends to obtain a higher power value of α wave. Figure 5. The averatge value of Ln(Pα) of different types of s for frontal area (Pα means power of α) Parietal-occipital area: Using the same method as the frontal area, the three-factor analysis of variance of repeated measures was performed on the parietal-occipital area (PO3, PO4), the results showed that: the music emotion s polarity intensity has a significant interaction effect. As shown in Figure 6, the parietal-occipital area and the frontal area have similar trends in the α wave power value, but overall, the corresponding α wave power value in the parietal-occipital area is larger, indicating that the brain area has a significant influence on the distribution of α power, the α power (14.01 ± 0.34) in the parietal-occipital area was significantly greater than that in the frontal area (12.86 ± 0.41). This is the difference in distribution of the EEG α-rhythm in the occipital region and the parietal region during the nervous state; further multiple comparisons of the other three levels of emotion type showed that: under the four conditions other than resting state, the brain region has a significant effect on the distribution of α power. In the parietal-occipital area, the α power of positive-polarity emotions in the strong-intensity emotions was significantly greater than that of the negative-polarity Figure 6. The averatge value of Ln(Pα) of different types of s for occipital (Pα means power of α) Conclusions 1) The EEG α power in the frontal area and the parietal-occipital area both increase as the emotional intensity of the music decreases. The specific performance is: in the frontal area, for positive-polarity and negative-polarity emotions, the α power of strong-intensity emotions is relatively weaker than that of the weak-intensity emotions; in the parietal-occipital area, for the negative-polarity emotions, the α power of strong-intensity emotions is relatively less than that of the weak-intensity emotions; in the analysis of the total α power in the frontal and parietal-occipital area, there are conclusions as: nervous<happy, nervous<soothing, and nervous<sorrowful, especially as nervous is the strongest stimulus in the experiment, the total α power it ignites is smaller than the other three weaker stimuli. 2) EEG α power is related to emotional polarity and modulated by emotional intensity. The specific performance is: in the frontal area, for strong-intensity and weak-intensity emotions, the α power of positive-polarity emotions is relatively greater than that of the negativepolarity emotions; in the parietal-occipital area, the α power of positive-polarity emotions in the strong-intensity emotions is significantly greater than that of the negative-polarity emotions; in the 383

6 analysis of the total α power in the frontal and parietal-occipital area, there are conclusions as: happy > nervous, soothing > nervous and other significant differences. These results indicate that, in strong or weak emotions, the α power of the EEG signal in the positive-polarity emotions is greater than that of the negativepolarity emotions, and the relationship between the α power and the polarity of the emotion is modulated by the intensity of the music, namely the α power of strong-intensity negative-polarity emotions is the smallest, and the α power of weak-intensity positive-polarity emotions is the largest. References Cheng ZH, Shi SY. The principal component analysis studies during emotion transformation. Journal of Dalian Jiaotong University 2015; 36(5): Hou JC, Liu C. The research of brain waves about emotional activity induced by different musical conformations composing of mode and tempo: A combined representational character of musical training experience and gender. Psychological Exploration 2010; 30(6): Lu YJ, Dai LL, Wu HZ. Research on EEG responses induced by different types of music to ease grief. Psychological Exploration 2012; 32(4): Wang JM, Qian L, Lu YJ. Research on EEG mechanisms of music influence emotions. Chinese Medical Herald 2010; 7(32): Xun YN, Liu Y. The influence of music on emotion and its brain mechanism. Progress in Natural Science 2009; 19(1): Yu Y, Yuan J, Fang P. Measurement for music-induced emotions and its interacting factors. Studies of Psychology and Behavior 2014; 12(5): Zhao GL, Song JJ, Ge Y. Advances in emotion recognition based on physiological big data. Journal of Computer Research and Development 2016; 53(1):