Evaluation of the correlation between facial muscle and brain activities in auditory stimulation

February 2, 2021 15:57 0218-348X 2150100 OPEN ACCESS Fractals, Vol. 29, No. 1 (2021) 2150100 (9 pages) c The Author(s) DOI: 10.1142/S0218348X21501000 EVALUATION OF THE CORRELATION BETWEEN FACIAL MUSCLE AND BRAIN ACTIVITIES IN AUDITORY STIMULATION MIRRA SOUNDIRARAJAN,∗MARTIN AUGUSTYNEK,† ONDREJ KREJCAR‡and HAMIDREZA NAMAZI‡,§,¶ ∗ School of Engineering,Monash University,Selangor,Malaysia † Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB – Technical University of Ostrava,Ostrava,Czech Republic ‡ Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove,Hradec Kralove,Czechia § College of Engineering and Science Victoria University,Melbourne,Australia ¶ hamidreza.nam[email protected]du.au Received 16 November 2020 Accepted 29 December 2020 Published February 4, 2021 Abstract Evaluation of the correlation of the activities of various organs is an important area of research in physiology. In this paper, we evaluated the correlation among the brain and facial muscles’ reactions to various auditory stimuli. We played three different music (relaxing, pop, and rock music) to 13 subjects and accordingly analyzed the changes in complexities of EEG and EMG ¶Corresponding author. This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND) License which permits use, distribution and reproduction, provided that the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. 2150100-1 Fractals 2021.29. Downloaded from www.worldscientific.com by 158.196.184.115 on 05/11/22. Re-use and distribution is strictly not permitted, except for Open Access articles. February 2, 2021 15:57 0218-348X 2150100 M. Soundirarajan et al. signals by calculating their fractal exponent and sample entropy. Based on the results, EEG and EMG signals experienced more significant changes by presenting relaxing, pop, and rock music, respectively. A strong correlation was observed among the alterations of the complexities of EMG and EEG signals, which indicates the coupling of the activities of facial muscles and brain. This method could be further applied to investigate the coupling of the activities of the brain and other organs of the human body. Keywords: Facial Muscle; EMG Signals; Brain; EEG Signals; Fractal Dimension; Sample Entropy; Complexity. 1. INTRODUCTION It is known that the human body is covered with different muscles that play an important role in its functioning. Like other organs, human muscles are controlled by the brain. For instance, when we are scared of something, our facial muscles react, and we will have a new gesture on our face. Many researchers have evaluated the activities of human muscles in various conditions. Among all the reported studies, a great number of works belong to investigating human facial muscle response to external stimuli by mathematical analysis of electromyography (EMG) signals. The reported works that analyzed facial EMG signals due to auditory,1 visual,2and emotional3stimuli, and during food consumption,4evaluated the influence of aging on facial muscle activity,5analyzed EMG responses for patients with hemifacial spasm during microvascular decompression operations,6and monitored EMG signals during decompression operation7can be mentioned. It is known that human muscles are controlled by the brain through the nervous system. Although the nature of this control which is performed through a high degree of complexity is not fully understood,8however, an important category of work is to quantify the coupling among the reactions of facial muscle and brain in different conditions. Referring to the literature, some studies analyzed the coupling among the alterations of EMG and EEG signals to decode the coupling of facial muscle and brain activities. The reported works that evaluated the coupling between EEG and EMG signals for post-stroke patients and healthy subjects,9to decode sleep-disordered breathing events,10 during seizure,11 for patients with Parkinson’s disease,12 during fatigue,13 due to electrical stimulation in individuals with stroke,14 while watching a video,15 and in response to different visual stimuli16–18 are worthy of being mentioned. Since different music affects facial muscle reactions,19 in this study, for the first time, we analyzed the coupling among the activities of the brain and facial muscles while listening to different types of music. As is known, the brain and muscle activities can be quantified using EEG and EMG signals, which have complex structures. Therefore, this study utilized the fractal theory to evaluate the coupling of the brain and facial muscle reactions to auditory stimuli. Fractals are objects (1D, 2D, or 3D) that show self-similarity or self-affinity. We can observe how different segments of a self-similar fractal object have a geometrical relationship to each other. However, self-affine fractals behave differently, and we cannot see any relationship among their different parts due to their different scaling exponents in various directions.20 Therefore, EEG and EMG signals are categorized as self-affine fractals. Fractal dimension is the principal exponent to quantify the complexity of fractals. Many works have investigated complex structures of various biosignals using fractal theory.21–24 Similarly, many works have analyzed EEG signals using fractal theory. The studies that evaluated the effect of auditory25 and electrical26 stimuli, aging,27 brain disorders,28 and imaginary movements29 on EEG signals can be stated. Similarly, the fractal theory also widely has been utilized to evaluate the complexity of EMG signals in different experiments.30–32 In this work, we also chose sample entropy to evaluate the complexity of recorded signals. In fact, we verified the result of the fractal analysis by computing the sample entropy of EEG and EMG signals. Sample entropy also has been utilized widely to analyze the complexity of various kinds of physiological signals.33–37 Specifically, we can mention the extensive application of sample entropy in the analysis of EEG38–40 and EMG41,42 signals. 2150100-2 Fractals 2021.29. Downloaded from www.worldscientific.com by 158.196.184.115 on 05/11/22. Re-use and distribution is strictly not permitted, except for Open Access articles. February 2, 2021 15:57 0218-348X 2150100 Evaluation of the Correlation Between Facial Muscle and Brain Activities in Auditory Stimulation As previously mentioned, no reported study has focused on the decoding of the correlation between facial muscle and brain reactions in response to auditory stimuli. Therefore, in this paper, we applied fractal theory and sample entropy to EEG and EMG signals to evaluate how the changes of these complex signals are coupled with the changes in the auditory stimuli. In the following, first, the method of analysis will be discussed. After that, we explain the steps taken for data collection and analysis. Then, the result of the analysis will be presented. Finally, we bring the discussion section that also includes potential future works. 2. METHOD We evaluated the correlation between the reactions of the brain and facial muscles in auditory stimulation using the fractal theory. Fractal exponent reflects the complexity; an object with larger complexity has a larger fractal exponent.43 We utilized the box-counting fractal dimension in this research. The box-counting algorithm covers the object with same-size (ε) boxes and accordingly counts their number (N). This process is repeated in further levels by changing the box size at each level. The fractal dimension is computed as follows: F= lim ε→0 log N(ε) log 1/ε .(1) Equation (1) in the general form is formulated as follows: FDh= lim ε→0 1 h−1 log N j=1 zh j log ε,(2) when his the order and zjindicates the probability: zj= lim T→0 tj T,(3) when Trepresents the total period of the signal. We also utilized sample entropy to quantify the complexity of signals in different auditory stimulations. Sample entropy is similar to the approximate entropy, but it is independent of the length of data. If we consider a signal in the form of {y(1),y(2),...,y(n)}with a constant interval of α, a template vector of length kcan be formulated as of Yk(i)={yi,y i+1,y i+2,...,y i+k−1},andthedistance function d[Yk(i),Y k(j)](i=j)istobeChebyshev distance. Then, the sample entropy (SamEn) is formulated as follows34: SamEn = −log A C.(4) Considering εas the tolerance (0.2×standard deviation of data), Ademonstrates for the number of template vector pairs that: d[Yk+1(i),Y k+1(j)] <ε. (5) Besides, Cdemonstrates for the number of template vector pairs that d[Yk(i),Y k(j)] <ε. (6) We chose three music as auditory stimuli in this study. These auditory stimuli include relaxing music, pop music, and rock music. In fact, choosing different types of music enabled us to evaluate the coupling among the complexities of EEG and EMG signals. We played each music for subjects and then investigated the coupling between EEG and EMG signals by calculating their fractal exponent and sample entropy. 2.1. Data Collection and Analysis This research was approved by Monash University and Victoria University (# 19050). The experiment was run on 13 healthy participants (3 F and 10 M, 18–22 years old) after they gave their consent. Participants did not drink alcoholic/caffeine beverages within 48 h before sitting for the experiment. We ran the experiment in an isolated room. We asked the participants to concentrate on the listening of music, without performing any other activity (e.g. body movements, opening of their eyes). We have recorded EEG signals using EMOTIV EPOC+ 14 Channel EEG Mobile device (Emotiv, USA) at 128 Hz, and EMG signals using Shimmer3 EMG Unit (Shimmer, Ireland) at 512 Hz. In the case of EEG recording, the EEG headset, which contains 14 recording electrodes and two reference electrodes (placed backside of ears), has been put on the subject’s scalp (based on the 10–20 system). In the case of EMG recording, five electrodes have been put on the subject’s face. It should be noted that the participants washed and dried their faces before data collection, and none of them had hair on their faces. It is worthy of mentioning that the used EMG electrodes (Covidien Kendall Disposable Surface EMG Electrodes 1”) had their own conductive gel, and we did not apply the extra gel on the skin of participants. 2150100-3 Fractals 2021.29. Downloaded from www.worldscientific.com by 158.196.184.115 on 05/11/22. Re-use and distribution is strictly not permitted, except for Open Access articles. February 2, 2021 15:57 0218-348X 2150100 M. Soundirarajan et al. (a) (b) Fig. 1 (a) The experiment set up and (b) the placement of EMG electrodes. The placements of EEG and EMG electrodes are shown in Fig. 1. Music files were played for subjects using the speaker of a computer at 50 dB volume. First, we recorded EEG and EMG signals during rest for one minute. Then, we played “Zen”, “Happy”, and “My life” music (downloaded from YouTube), respectively, as relaxing, pop, and rock music(eachmusicforoneminute)tothesubjects and recorded their EEG and EMG signals. Oneminute rest was given to participants between different stimulations. To consider the repeatability of results, the experiment has been repeated in another session for all participants. We should note that the EEG device had some poor connection (or disconnected) during the data collection in five periods and therefore, we removed those recorded data from the analysis. After removing the DC offset, we filtered EEG signals using Butterworth band-pass filter at 1–40 Hz. We also denoised EMG signals using Butterworth band-pass filter at 25–180 Hz. We checked the filtered versus raw signals visually to ensure the quality of filtering. After that, we calculated the fractal dimension and sample entropy of filtered signals. We used boxes with the sizes of 1 2, 1 4,1 8,1 16 ,... in running box-counting algorithm. All steps of data analysis were conducted in MATLAB R2019a (MathWorks, USA). We compared the complexity of EEG signals (and also EMG signals) among various conditions by conducting the Tukey test (α=0.05). Besides, the effect sizes (Cohen’s d) were computed to analyze the effect of different music on the alterations of the complexity of EEG and EMG signals. We examined the coupling among the alterations of complexity EEG and EMG signals by calculating the Pearson correlation coefficient. 3. RESULTS The presented results are based on the average of recorded data from all channels for all participants in both sessions of the recording. The error bars in case of each figure indicate the standard deviation. The changes in the fractal dimension of EEG signals are shown in Fig. 2. As Fig. 2 demonstrates, the EEG signals’ fractal exponent decreased due to the application of first music to the subjects. As can be seen, by moving from relaxing to pop and rock music, the fractal exponent of EEG signals decreased. Therefore, Fig. 2 The fractal exponent of EEG signals. 2150100-4 Fractals 2021.29. Downloaded from www.worldscientific.com by 158.196.184.115 on 05/11/22. Re-use and distribution is strictly not permitted, except for Open Access articles. February 2, 2021 15:57 0218-348X 2150100 Evaluation of the Correlation Between Facial Muscle and Brain Activities in Auditory Stimulation we can state that the complexity of EEG signals decreased. Considering the rest condition as a reference, we can state that by moving from relaxing to pop and rock music, the complexity of EEG signals experiences greater changes. Comparing the fractal exponents among various conditions (Table 1) demonstrates that in general, rock music caused a more significant alteration in the EEG signals’ complexity compared to pop music, which itself caused a more significant change in the EEG signals’ complexity compared to relaxing music. Here we should note that in this study, we look for the correlation between the alterations of EEG and EMG signals, not the significance of their alterations due to stimulation. Besides, the calculated values of Cohen’s din this table indicate that by moving from relaxing to pop and rock music, the impact of the music on the alterations to the EEG signals’ complexity increased. The alterations of the fractal exponent for EMG signals are shown in Figure 3. As can be observed, the EMG signals’ fractal exponents increased due to the application of relaxing music, and then, this increase continued as we moved to pop and rock music. Considering the rest condition as a reference, we can state that EMG signals experienced greater alterations Table 1 Pair-Wise Comparison of Fractal Exponents of EEG Signals. Pairwise Comparison p-Value Cohen’s d Rest versus relaxing music 0.0353 0.7923 Rest versus pop music 0.0149 0.9346 Rest versus rock music 0.0000 2.0669 Relaxing versus pop music 0.9866 0.0819 Relaxing versus rock music 0.0113 0.8510 Pop versus rock music 0.0307 0.7926 Fig. 3 The fractal exponent of EMG signals. in their complexity as we move toward rock music, similar to the changing of the complexity of EEG signals in Fig. 2. Besides, the calculated Pearson correlation coefficient of −0.8350 reflects a strong coupling between the alterations in the EEG and EMG signals’ complexities in various conditions. Multiple comparisons of the fractal exponents in Table 2 demonstrate that by moving from relaxing to pop and rock music, the alterations in the complexities become more significant. Besides, the effect sizes in this table demonstrate that rock music had the largest effect on the fractal exponents. Qualitatively comparing the effect sizes between Tables 1 and 2 indicates the greater influence of music on changing the complexity of EMG signals than EEG signals from the rest condition. This finding can be referred to the activity of the brain versus facial muscles during rest. It is known the facial muscles are less active than the brain during rest (the brain is engaged with different processing even in rest condition) and therefore, listening to music increased their activity greater than the brain. Therefore, stimulation of subjects with music caused greater alterations in the activity of facial muscles than the brain. Figure 4 shows the alterations of the sample entropy of EEG signals in the case of various music. Table 2 Pair-Wise Comparison of Fractal Exponents of EMG Signals. Comparison p-Value Cohen’s d Rest versus relaxing music 0.0000 −2.2854 Rest versus pop music 0.0000 −2.1971 Rest versus rock music 0.0000 −2.6940 Relaxing versus pop music 0.9968 −0.0525 Relaxing versus rock music 0.9388 −0.1722 Pop versus rock music 0.9822 −0.1053 Fig. 4 The sample entropy of EEG signals. 2150100-5 Fractals 2021.29. Downloaded from www.worldscientific.com by 158.196.184.115 on 05/11/22. Re-use and distribution is strictly not permitted, except for Open Access articles. February 2, 2021 15:57 0218-348X 2150100 M. Soundirarajan et al. As Fig. 4 demonstrates, the EEG signals’ sample entropy decreased due to the application of first music to the subjects. After that, by moving from relaxing to pop and rock music, the sample entropy of EEG signals continued to decrease. Therefore, the complexity of EEG signals experienced greater changes as we moved toward rock music. Comparing this figure with Fig. 2 indicates that the results of sample entropy verify the findings of fractal analysis. Multiple comparisons from the Tukey test in Table 3 demonstrate that by moving from relaxing to pop and rock music, the changes of the sample entropy of EEG signals become more significant. As was mentioned previously, the in-significant alterations of the complexity of EEG signals among various conditions are not considered in this research since we are looking for the correlation between the alterations of EEG and EMG signals, not the significance of their alterations due to stimulation. Besides, the values of Cohen’s din this table indicate that rock and relaxing music, respectively, had the largest and smallest effect on the alterations of the complexity of EEG signals. Figure 5 shows the sample entropy of EMG signals in the case of various music. Table 3 Pair-Wise Comparison of Sample Entropy of EEG Signals. Comparison p-Value Cohen’s d Rest versus relaxing music 0.9092 0.1847 Rest versus pop music 0.7115 0.2834 Rest versus rock music 0.5157 0.4112 Relaxing versus pop music 0.9775 0.1108 Relaxing versus rock music 0.8807 0.2284 Pop versus rock music 0.9857 0.1017 Fig. 5 ThesampleentropyofEMGsignals. Table 4 Pair-Wise Comparison of Sample Entropy of EMG Signals. Comparison p-Value Cohen’s d Rest versus relaxing music 0.6707 −0.3644 Rest versus pop music 0.3985 −0.5013 Rest versus rock music 0.0029 −1.0196 Relaxing versus pop music 0.9690 −0.1263 Relaxing versus rock music 0.0658 −0.6427 Pop versus rock music 0.1783 −0.5240 This figure presents that the sample entropy of EMG signals increased due to the application of relaxing music and then continued to increase as we moved to pop and rock music. In fact, this result is similar to the result of fractal analysis in Fig. 3. On the other hand, based on Figs. 4 and 5, EEG and EMG signals’ sample entropy experiences greater changes as we move toward rock music. Besides, the calculated Pearson correlation coefficient of −0.9337 reflects a strong coupling between the alterations in the EEG and EMG signals’ complexity when subjects listened to different music. Therefore, the analysis of sample entropy verified the result of fractal analysis. Multiple comparisons of the sample entropy of EMG signals in Table 4 demonstrate that by moving from relaxing to pop and rock music, the alterations in the complexities become more significant. Besides, the effect sizes tell us that rock music had the largest effect on the sample entropy of the EMG signals. Therefore, according to the findings, the alterations in the EMG and EEG signals’ complexities are coupled; as we shift between different stimuli, the fractal exponent (and sample entropy) of EEG and EMG signals change together, which indicates the coupling of the brain and facial muscles’ activities. 4. DISCUSSION AND CONCLUSION For the first time, we evaluated the coupling between the reactions of the brain and facial muscles in auditory stimulation, using the complexity theory. We evaluated the changes in the EEG and EMG signals’ complexities among various auditory stimulations by calculating their fractal exponent and sample entropy. The findings of the fractal analysis demonstrated that EEG signals experienced more significant changes by presenting relaxing, pop, and rock 2150100-6 Fractals 2021.29. Downloaded from www.worldscientific.com by 158.196.184.115 on 05/11/22. Re-use and distribution is strictly not permitted, except for Open Access articles. February 2, 2021 15:57 0218-348X 2150100 Evaluation of the Correlation Between Facial Muscle and Brain Activities in Auditory Stimulation music, respectively. Similar findings were obtained for the fractal exponent of EMG signals. The result of statistical analysis showed that EEG and EMG signals experience more significant changes by moving from relaxing to pop and rock music. Besides, a strong correlation among the changes in the complexity of EEG and EMG signals in response to different music was observed, which indicates a strong coupling among the brain and facial muscle reactions to stimuli. The result of the analysis of sample entropy of signals was similar to the findings of fractal analysis and indicated a strong coupling between the alterations of the complexity of EEG and EMG signals. Therefore, we conclude that the alterations of the activities of the brain and facial muscles are coupled. The conducted investigation in this research is novel since, for the first time, we analyzed the coupling among the alterations of EEG and EMG signals in auditory stimulation. We elaborate on the results by referring to the brain-muscle connection. Due to the controlling role of the brain on facial muscles’ activities through the nervous system,44 the brain sends an impulse to facial muscles about the stimulus that we receive (music in this research). Therefore, depending on the type of music that we listen to, different messages are sent to facial muscles by the brain, which causes different reactions of facial muscles. In fact, the obtained results for the reaction of facial muscles to music are valid based on the obtained results in Ref. 45, which state the changes in EMG signals due to listening to different music. In future studies, we can expand this analysis to examine the coupling of the brain and facial muscles’ reactions to other types of stimuli (e.g. olfactory stimuli). We can also investigate the coupling among other organs of humans. For instance, we can evaluate the coupling among skin and brain reactions to different stimuli by evaluating GSR and EEG signals. Due to the interaction of different regions of the brain together,46 we can also focus on evaluating the association of facial muscles’ reaction with the changes in the complexity of EEG signals recorded from different areas of the brain. Besides, due to the interaction of different organs of the body within the network physiology,47 the simultaneous analysis of their reactions to external stimuli can be examined. It should be noted that all these analyses also can be conducted for patients with different brain disorders to investigate how a damaged brain can control different organs within the physiological network. All these analyses have great importance in understanding human physiology. ACKNOWLEDGMENT This work was supported in part by the project of the Grant Agency of Excellence 2021, the University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic. REFERENCES 1. S. Vieillard, C. Pinabiaux and E. Bigand, Positive and detached reappraisal of threatening music in younger and older adults, Front. Hum. Neurosci. 14 (2020) 216. 2. P. A. G. Forbes, S. Korb, A. Radloff and C. Lamm, The effects of self-relevance versus reward value on facial mimicry, Acta Psychol. 212 (2021) 103193. 3. S. Tamm et al., A combined fMRI and EMG study of emotional contagion following partial sleep deprivation in young and older humans, Sci. Rep. 10 (2020) 17944. 4. W. Sato et al., Facial EMG correlates of subjective hedonic responses during food consumption, Nutrients 12(4) (2020) 11. 5. S. L. Reminger et al., Age-invariance in the asymmetry of stimulus-evoked emotional facial muscle activity, Aging,Neuropsychol. Cogn.7(3) (2010) 156–168. 6. A. R. Møller and P. J. Jannetta, Monitoring facial EMG responses during microvascular decompression operations for hemifacial spasm, J. Neurosurg. 66(5) (1987) 681–685. 7. K. W. Lee et al., Intraoperative facial EMG monitoring during decompression operation for hemifacial spasm, J. Korean Neurosurg. Soc. 26(9) (1997) 1265–1271. 8. A. Bashan et al., Network physiology reveals relations between network topology and physiological function, Nat. Commun.3(2012) 702. 9. Y. Gao et al., Electroencephalogram-electromyography coupling analysis in stroke based on symbolic transfer entropy, Front. Neurol.8(2018) 716. 10. M.R.Azim,S.A.Haque,M.S.AminandT.Latif, Analysis of EEG and EMG signals for detection of sleep disordered breathing events, Int. Conf. Electrical and Computer Engineering (ICECE 2010), Dhaka, 2010, pp. 646–649. 11. A. A. Arbune et al., Ictal quantitative surface electromyography correlates with postictal EEG suppression, Neurology 94(24) (2020). 12. M. Gunther et al., Coupling between leg muscle activation and EEG during normal walking, intentional stops, and freezing of Gait in Parkinson’s disease, Front. Physiol.10 (2019) 870. 2150100-7 Fractals 2021.29. Downloaded from www.worldscientific.com by 158.196.184.115 on 05/11/22. Re-use and distribution is strictly not permitted, except for Open Access articles. February 2, 2021 15:57 0218-348X 2150100 M. Soundirarajan et al. 13. Q. Qiu et al., Muscle a extremely low frequency magnetic stimulation eliminates the effect of fatigue on EEG-EMG coherence during the lateral raise task: A pilot quantitative investigation, Biomed. Res. Int. 2018 (2018) 1–8. 14. L. H. Pan et al., Effects of 8-week sensory electrical stimulation combined with motor training on EEGEMG coherence and motor function in individuals with stroke, Sci. Rep.8(2018) 9217. 15. A. Achaibou, G. Pourtois, S. Schwartz and P. Vuilleumier, Simultaneous recording of EEG and facial muscle reactions during spontaneous emotional mimicry, Neuropsychologia 46(4) (2008) 1104–1113. 16. M. Soundirarajan et al., Decoding of the relationship between brain and facial muscle activities in response to dynamic visual stimuli, Fluct. Noise Lett., doi:10.1142/ S0219477520500418. 17. M. Soundirarajan et al., Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli, Technol. Health Care, doi:10.3233/THC-192085. 18. M. Soundirarajan et al., Analysis of brainfacial muscle connection in the static fractal visual stimulation, Int. J. Imaging Syst. Technol., doi:10.1002/ima. 22480. 19. L. O. Lundqvist, F. Carlsson, P. Hilmersson and P. Juslin, Emotional responses to music: Experience, expression, and physiology, Psychol. Music 37(1) (2008) 61–90. 20. H. Babini, V. V. Kulish and H. Namazi, Physiological state and learning ability of students in normal and virtual reality conditions: Complexitybased analysis, J. Med. Internet Res. 22(6) (2020) e17945. 21. H. Namazi, O. Krejcar and A. Subasi, Complexity and information-based analysis of the variations of the SARS-CoV-2 genome in the United States of America (USA), Fractals 28(7) (2020) 2150023. 22. M. R. Ashfaq Ahamed et al.,Complexitybased analysis of brains’ synchronization in humanhuman interaction, Fractals, doi: 10.1142/ S0218348X20501029. 23. S. Omam et al., Complexity-based decoding of brain-skin relation in response to olfactory stimuli, Comput. Methods Programs Biomed.184 (2020) 1–6, doi:10.1016/j.cmpb. 2019.105293. 24. S. Mujib Kamal et al., Decoding of the relationship between human brain activity and walking paths, Technol. Health Care 28(4) (2020) 381–390. 25. A. F. Teixeira Borges et al., Scaling behavior in music and cortical dynamics interplay to mediate music listening pleasure, Sci. Rep.9(2019) 17700. 26. K. Tripanpitak et al., Classification of pain event related potential for evaluation of pain perception induced by electrical stimulation, Sensors (Basel) 20(5) (2020) 1491. 27. A. P. Anokhin et al., Age increases brain complexity, Electroencephalogr. Clin. Neurophysiol. 99 (1996) 63–68. 28. L. A. Moctezuma and M. Molinas, Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD, J. Biomed. Res. 34(3) (2020) 180–190. 29. V. V. Grubov, A. A. Badarin, N. Frolov and E. Pitsik, Analysis of real and imaginary motor activity with combined EEG and fNIRS, Saratov Fall Meeting 2019: Computations and Data Analysis:From Nanoscale Tools to Brain Functions, doi:10.1117/12.2564387. 30. H. Namazi and T. Seifi Ala, Decoding of simple and compound limb motor imagery movements by fractal analysis of Electroencephalogram (EEG) signal, Fractals 27(3) (2019) 1950041. 31. A. Phinyomark, P. Phukpattaranont, C. Limsakul and M. Phothisonothai, Electromyography (EMG) signal classification based on detrended fluctuation analysis, Fluctuat. Noise Lett.10 (2011) 281–301. 32. A. Santuz et al., Lower complexity of motor primitives ensures robust control of high-speed human locomotion, Heliyon 6(10) (2020) e05377. 33. S. Mujib Kamal et al., Complexity-based decoding of the coupling among heart rate variability (HRV) and walking path, Front. Physiol.11 (2020) 602027, doi:10.3389/fphys.2020.602027. 34. H. Namazi, E. Herrera-Viedma and O. Krejcar, Complexity-based detection of similarity between animal coronaviruses and SARS-CoV-2 in humans, Fractals 28(7) (2020) 2150031. 35. H. Namazi and O. Krejcar, Analysis of pregnancy development by complexity and information-based analysis of fetal phonocardiography (PCG) signals, Fluct. Noise Lett.20(1) (2021) 2150028. 36. H. Namazi, Complexity-based classification of the coronavirus genome versus genomes of the human immunodeficiency virus (HIV) and dengue virus, Fractals, doi:10.1142/S0218348X20501297. 37. Q. D. Nam Nguyen, A. B. Liu and C. W. Lin, Development of a neurodegenerative disease gait classification algorithm using multiscale sample entropy and machine learning classifiers, Entropy 22 (2020) 1340. 38. Y. Lu et al., Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals, Measurement 150 (2020) 107003. 39. J. Prasanna et al., Detection of focal and non-focal electroencephalogram signals using fast 2150100-8 Fractals 2021.29. Downloaded from www.worldscientific.com by 158.196.184.115 on 05/11/22. Re-use and distribution is strictly not permitted, except for Open Access articles. February 2, 2021 15:57 0218-348X 2150100 Evaluation of the Correlation Between Facial Muscle and Brain Activities in Auditory Stimulation walsh-hadamard transform and artificial neural network, Sensors 20(17) (2020) 4952. 40. F. Li et al., Multi-feature fusion method based on EEG signal and its application in stroke classification, J. Med. Syst.44 (2020) 39. 41. E. Ferrari, G. Cooper, N. D. Reeves and E. F. Hodson-Tolea, Intrinsic foot muscles act to stabilise the foot when greater fluctuations in centre of pressure movement result from increased postural balance challenge, Gait Posture 79 (2020) 229–233. 42. Y. Lu et al., Entropy-based pattern learning based on singular spectrum analysis components for assessment of physiological signals, Complexity 2020 (2020) 4625218, doi:10.1155/2020/ 4625218. 43. H. Namazi and V. V. Kulish, Complexity-based classification of the coronavirus disease (COVID-19), Fractals 28(5) (2020) 2050114. 44. K. M. Gothard, The amygdalo-motor pathways and the control of facial expressions, Front. Neurosci.8 (2014) 43. 45. L. Lundqvist, F. Carlsson, P. Hilmersson and P. Juslin, Emotional responses to music: Experience, expression, and physiology, Psychol. Music 37(1) (2008) 61–90. 46. K. K. L. Liu et al., Plasticity of brain wave network interactions and evolution across physiologic states, Front. Neural Circuits 9(2015) 62. 47. R. P. Bartsch, K. K. L. Liu, A. Bashan and P. C. Ivanov, Network physiology: How organ systems dynamically interact, PLoS One 10(11) (2015) e0142143. 2150100-9 Fractals 2021.29. Downloaded from www.worldscientific.com by 158.196.184.115 on 05/11/22. Re-use and distribution is strictly not permitted, except for Open Access articles.