Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles.
OBJECTIVE:
In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.
METHODS:
Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).
RESULTS:
Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.
CONCLUSION:
This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.
RemingerSL, et al. Age-invariance in the asymmetry of stimulus-evoked emotional facial muscle activity. Aging, Neuropsychology, and Cognition.2010; 7(3): 156-168.
2.
SmithDPHillmanCHDuleyAR. Influences of age on emotional reactivity during picture processing. The Journals of Gerontology: Series B.2005; 60(1): P49-P56.
3.
LeeKW, et al. Intraoperative facial EMG monitoring during decompression operation for hemifacial spasm. Journal of Korean Neurosurgical Society.2017; 26(9): 1265-1271.
4.
MøllerARJannettaPJ. Monitoring facial EMG responses during microvascular decompression operations for hemifacial spasm. J. Neurosurg.1987; 66(5): 681-5.
5.
TanJ, et al. Facial electromyography (fEMG) activities in response to affective visual stimulation. 2011 IEEE Workshop on Affective Computational Intelligence (WACI), Paris, 2011; pp. 1-5.
6.
SestitoM, et al. Facial reactions in response to dynamic emotional stimuli in different modalities in patients suffering from schizophrenia: a behavioral and EMG study. Front. Hum. Neurosci.2013; 7: 368.
7.
KüneckeJ, et al. Facial EMG responses to emotional expressions are related to emotion perception ability. PLoS One.2014; 9(1): e84053.
8.
HartBT, et al. Emotion in stories: facial EMG evidence for both mental simulation and moral evaluation. Front. Psychol.2018; 9: 613.
9.
TanJW, et al. Recognition of intensive valence and arousal affective states via facial electromyographic activity in young and senior adults. PLoS One.2016; 11(1): e0146691.
10.
FerozeRA, et al. Estimation of intraoperative stimulation threshold of the facial nerve in patients undergoing microvascular decompression. J. Neurol. Surg. B.2019; 80(6): 599-603.
11.
KorbS, et al. The perception and mimicry of facial movements predict judgments of smile authenticity. PLoS One.2014; 9(6): e99194.
12.
MozaffarileghaMNamaziHRAhadiMJafariS. Complexity-based analysis of the difference in speech-evoked Auditory Brainstem Responses (s-ABRs) between binaural and monaural listening conditions. Fractals.2018; 26(4): 1850052.
13.
Mujib KamalS, et al. Decoding of the relationship between human brain activity and walking paths. Technology and Health Care. 2019; 1-9. doi: 10.3233/THC-191965(2019).
14.
NamaziHSeifi AlaTAghasianE. Complexity-based classification of EEG signal in normal subjects and patients with epilepsy. Technology and Health Care. 2019; 1-10. doi: 10.3233/THC-181579.
15.
NamaziHAlaTS. Decoding of simple and compound limb motor imagery movements by fractal analysis of electroencephalogram (EEG) signal. Fractals.2019; 27(3): 1950041.
16.
AlipourHMenonANamaziHTowhidkhahFJafariS. Complexity-based analysis of the relation between fractal visual stimuli and fractal eye movements. Fluctuation and Noise Letters.2019; 18(3): 1950012.
17.
MozaffarileghaMNamaziHTahaeiAAJafariS. Complexity-based analysis of the difference between normal subjects and subjects with stuttering in speech evoked auditory brainstem response. J. Med. Biol. Eng. 2018. doi: 10.1007/s40846-018-0430-x.
18.
NamaziHR. Fractal-based analysis of the influence of music on human respiration. Fractals.2017; 25(6): 1750059.
19.
NamaziH. Fractal-based classification of electromyography (EMG) signal between fingers and hand’s basic movements, functional movements, and force patterns. Fractals.2019; 27(4): 1950050.
20.
NamaziHAghasian,ESeifi AlaT. Fractal-based classification of electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia. Technology and Health Care.2019; 27(3): 233-241.
21.
NamaziHJafariS. Estimating of brain development in newborns by fractal analysis of sleep electroencephalographic (EEG) Signal. Fractals.2019; 27(3): 1950021.
22.
NamaziH. Fractal based classification of electromyography (EMG) signal in response to basic movements of the fingers. Fractals.2018; 27(3): 1950037.
23.
AlipourHNamaziHAzarnoushHJafariS. Fractal-based analysis of the influence of color tonality on human eye movements. Fractals.2019; 27(3): 1950040.
24.
NamaziHJafariS. Decoding of simple hand movements by fractal analysis of electromyography (EMG) signal. Fractals.2019; 27(4): 1950042.
25.
AlipourHNamaziHAzarnoushHJafariS. Complexity-based analysis of the relation between moving visual stimuli and human eye movement. Fractals. 2018. doi: 10.1142/S0218348X19500245.
26.
NamaziH. Decoding of hand gestures by fractal analysis of electromyography (EMG) signal. Fractals.2018; 27(3): 1950022.
27.
AlipourJMKhosrowabadiRNamaziH. Fractal-based analysis of the influence of variations of rhythmic patterns of music on human brain response. Fractals.2018; 26(5): 1850080.
28.
NamaziHJafariS. Decoding of wrist movements’ direction by fractal analysis of magnetoencephalography (MEG) signal. Fractals.2019; 27(2): 1950001.
29.
NamaziHSeifi AlaTBakardjianH. Decoding of steady-state visual evoked potentials by fractal analysis of the electroencephalographic (EEG) signal. Fractals. 2018. doi: 10.1142/S0218348X18500925.
30.
AlipourHNamaziHAzarnoushHJafariS. Complexity-based analysis of the influence of visual stimulus color on human eye movement. Fractals.2019; 27(2): 1950002.
31.
NamaziHSeifi AlaTKulishV. Decoding of upper limb movement by fractal analysis of electroencephalogram (EEG) signal. Fractals.2018; 26(5): 1850081.
32.
NamaziHAkramiAHussainiJSilvaONWongAKulishVV. The fractal based analysis of human face and DNA variations during aging. Biosci. Trends.2016; 10(6): 477-481.
33.
NamaziHDaneshiAAzarnoushHJafariSTowhidkhahF. Fractal-based analysis of the influence of auditory stimuli on eye movements. Fractals.2018; 26(3): 1850040.
34.
NamaziHJafariS. Age-based variations of fractal structure of EEG signal in patients with epilepsy. Fractals.2018; 26(4): 1850051.
35.
NamaziHKhosrowabadiRHussainiJHabibiS, FaridAKulishVV. Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal. Oncotarget.2016; 7: 56120-56128.
36.
NamaziHKulishVVHussainiJHussainiJDelavizADelavizF, et al. A signal processing based analysis and prediction of seizure onset in patients with epilepsy. Oncotarget.2016; 7: 342-350.
37.
Ahmadi-PajouhMASeifi AlaaTZamanianFNamaziHJafariS. Fractal-based classification of human brain response to living and non-living visual stimuli. Fractals.2018; 26(5): 1850069.
38.
KamalSMSimSTeeRNathanVNamaziH. Complexity-based analysis of the relation between human muscle reaction and walking path. Fluctuation and Noise Letters. 2020. doi: 10.1142/S021947752050025X.
39.
ShafiulOBabiniMSimSTeeRNathanVNamaziH. Complexity-based decoding of brain-skin relation in response to olfactory stimuli. Computer Methods and Programs in Biomedicine.2020; 184: 105293.
40.
Ashfaq AhamedMRBabiniMNamaziH. Complexity-based decoding of the relation between human voice and brain activity. Technology and Health Care. 2020. doi: 10.3233/THC-192105.
41.
BabiniMHKulishVNamaziH. Complexity-based analysis of human physiological conditions in normal and virtual reality (VR) conditions. Journal of Medical Internet Research. 2020. doi: 10.2196/17945.
42.
ShiM, et al. Renyi distribution entropy analysis of short-term heart rate variability signals and its application in coronary artery disease detection. Front. Physiol.2019; 10: 809.
43.
AzamiHRostaghiMFernandezAEscuderoJ. Dispersion entropy for the analysis of resting-state MEG regularity in Alzheimer’s disease. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016; pp: 6417-6420.
44.
TungK, et al. Entropy-Assisted Multi-Modal Emotion Recognition Framework Based on Physiological Signals. 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Sarawak, Malaysia. 2018; pp. 22-26. doi: 10.1109/IECBES.2018.8626634.
45.
NamaziHDaneshiAShirzadianTAzarnoushHJafariS. Information-based analysis of the relation between visual stimuli and human eye movements. Fluctuation and Noise Letters.2019; 18(1): 1950010.
46.
KangHGDingwellJB. Differential changes with age in multiscale entropy of electromyography signals from leg muscles during treadmill walking. PLoS One.2016; 11(8): e0162034.
47.
VallejoM, et al. Neuromuscular disease detection by neural networks and fuzzy entropy on time-frequency analysis of electromyography signals. Expert Systems.2018; 35(1): e12274.
48.
TaoW, et al. Multi-scale complexity analysis of muscle coactivation during gait in children with cerebral palsy. Front. Hum. Neurosci.2015; 9: 367.
49.
ZhouPZhangX. A novel technique for muscle onset detection using surface EMG signals without removal of ECG artifacts. Physiol. Meas.2014; 35(1): 45-54.
50.
ZhangXZhouP. Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes. J. Electromyogr. Kinesiol.2012; 22(6): 901-907.
51.
IstenicR, et al. Multi-scale entropy-based approach to automated surface EMG classification of neuromuscular disorders. Med. Biol. Comput.2010; 48: 773-781.
52.
GaoYRenLLiRZhangY. Electroencephalogram-electromyography coupling analysis in stroke based on symbolic transfer entropy. Front. Neurol.2018; 8: 716.
53.
KamalSMBinti Mat DawiNSimSTeeRNathanVAghasianENamaziH. Information-based analysis of the relation between human muscle reaction and walking path. Technology and Health Care. 2020. doi: 10.3233/THC-192034.
54.
ParkJH, et al. Multiscale entropy analysis of EEG from patients under different pathological conditions. Fractals.2017; 15(4): 399-404.
55.
SharmilaAAman RajSShashankPMahalakshmiP. Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case study. J. Med. Eng. Technol.2018; 42(1): 1-8.
56.
ChaofengC, et al. Approximate entropy analysis on the electroencephalogram signal evoked by mental tasks. 2012 IEEE Symposium on Electrical & Electronics Engineering (EEESYM), Kuala Lumpur. 2012; pp. 52-54. doi: 10.1109/EEESym.2012.6258585.
57.
CaoZJLinCT. Inherent fuzzy entropy for the improvement of EEG complexity evaluation. IEEE Transactions on Fuzzy Systems.2017; 26(2): 1032-1035.
58.
ChengQ, et al. Increased sample entropy in EEGs during the functional rehabilitation of an injured brain. Entropy.2019; 21(7): 698.
59.
OlofsenESleighJWDahanA. Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect. Br. J. Anaesth.2008; 101(6): 810-21.
FalsaperlaR, et al. Congenital muscular dystrophy: from muscle to brain. Italian Journal of Pediatrics.2016; 42: 78.
62.
AngeliniCPinzanE. Advances in imaging of brain abnormalities in neuromuscular disease. Ther. Adv. Neurol. Disord.2019; 12: 1756286419845567.
63.
LeesA. An essay on the shaking palsy. Brain.2017; 140(3): 843-848.
64.
NamaziHKulishVV. Fractional diffusion based modelling and prediction of human brain response to external stimuli. Comput. Math. Methods Med.2015; 2015: 148534.
65.
SeetharamanKNamaziHKulsihVV. Phase lagging model of brain response to external stimuli – modeling of single action potential. Comput. Biol. Med.2012; 42: 857-862.
66.
NamaziHKulishVV. A mathematical based calculation of a myelinated segment in axons. Comput. Biol. Med.2013; 43(6): 693-8.
67.
NamaziHKulishVVWongANazeriS. Mathematical based calculation of drug penetration depth in solid tumors. Biomed. Res. Int.2016; 2016: 1-8.
68.
NamaziHKulishVV. Mathematical modeling of human brain neuronal activity in the absence of external stimuli. J. Med. Imaging Health Inform.2012; 2(4): 400-407.
69.
PourpanahFLimCPHaoQA. Reinforced fuzzy ARTMAP model for data classification. Int. J. Mach. Learn. & Cyber.2019; 10: 1643-1655.
70.
PourpanahF, et al. An improved fuzzy ARTMAP and Q-learning agent model for pattern classification. Neurocomputing.2019; 359: 139-152.
71.
PourpanahFZhangBMaRHaoQ. Non-Intrusive Human Motion Recognition Using Distributed Sparse Sensors and the Genetic Algorithm Based Neural Network, 2018 IEEE SENSORS, New Delhi. 2018; pp. 1-4. doi: 10.1109/ICSENS.2018.8589618.
