Decoding of the Relationship between Brain and Facial

Muscle Activities in Response to Dynamic Visual Stimuli

Mirra Soundirarajan, Mohammad Hossein Babini, Sue Sim,Visvamba Nathan and Hamidreza Namazi*

School of Engineering

Monash University, Selangor, Malaysia*hamidreza.namazi@monash.edu

Received 19 January 2020

Accepted 23 March 2020Published 23 June 2020

Communicated by Robert Vajtai

In this research, for the ¯rst time, we analyze the relationship between facial muscles and brainactivities when human receives di®erent dynamic visual stimuli. We present di®erent moving

visual stimuli to the subjects and accordingly analyze the complex structure of electromyog-

raphy (EMG) signal versus the complex structure of electroencephalography (EEG) signal usingfractal theory. Based on the obtained results from analysis, presenting the stimulus with greater

complexity causes greater change in the complexity of EMG and EEG signals. Statistical

analysis also supported the results of analysis and showed that visual stimulus with greater

complexity has greater e®ect on the complexity of EEG and EMG signals. Therefore, we showedthe relationship between facial muscles and brain activities in this paper. The method of analysis

in this research can be further employed to investigate the relationship between other human

organs' activities and brain activity.

Keywords: Facial muscle; brain; electromyography signal; electroencephalography signal;

complexity; fractal dimension.

1. Introduction

Facial muscles play an important role in shaping of human face. These muscles react

to external changes around human. For instance, when we smell an odor, facial

muscles activity changes that results in changing our face. By referring to the lit-

erature, we can ¯nd plenty of works that focused on the analysis of facial muscles

activity. These works mainly analyzed EMG signal as the indicator of muscle ac-

tivity. The studies that worked on facial expression (happy, angry, and sad) [1] and

emotion (happy, sad, afraid, surprised, disgusted, and neutral) [2] recognition, an-

alyzed the e®ect of visual [3], emotional [4, 5] and mental [6] stimuli on EMG signal,

*Corresponding author.

Fluctuation and Noise Letters

(2020) 2050041 (13 pages)

#.c World Scienti¯c Publishing Company

DOI: 10.1142/S0219477520500418

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investigated the variations of EMG signal during decompression operation for

patients with hemifacial spasm [7], recognized intensive valence and arousal a®ective

[8], analyzed the activity of facial muscles during aging [9] and judged smile au-

thenticity by analysis of EMG signal [10] are noteworthy to be mentioned.

Since di®erent parts of human body are controlled by his brain, therefore, there

should be a relationship between facial muscle and brain activities in di®erent con-

ditions. Although many works conducted on analysis of EEG and EMG signals in

separate experiments, however, no reported study investigated the link between

these two signals in one experiment. In other words, no investigation has been

reported that investigated the relationship between facial muscles and brain activ-

ities by analysis of EMG and EEG signal. Therefore, in order to ¯ll this gap, in this

study, we analyze the relation between facial muscle and brain activities.

It is known that our brain processes di®erent stimuli that we receive. After that,

due to the connection between brain and di®erent organs, brain sends impulses about

the stimulus to them. Due to this connection, the reaction of brain to di®erent stimuli

is re°ected in the message that brain sends to di®erent organs. Considering this

interlink of activities between human brain and di®erent organs, in this research, we

analyze the relationship between facial muscle and brain activities by analyzing

EMG signal (as the feature of muscle activity) and EEG signal (as the feature of

brain activity).

Since both EEG and EMG signals have complex structures, in this research, we

employ complexity theory for our investigation. In fact, we analyze the recorded

EEG and EMG signals using fractal theory that indicates the complexity of system

(EEG and EMG signals in this research). Fractals are self-similar or self-a±ne

objects that have complex structures [11]. A self-similar object has the same scaling

exponent in di®erent directions. However, a self-a±ne object has di®erent scaling

exponents in di®erent directions [11]. The object with greater fractal dimension (as

indicator of complexity) has greater complexity [12].

During years, many works have been reported in the literature that analyzed the

variations of complexity of di®erent time series and patterns using fractal theory.

The investigations on fractal analysis of magnetoencephalography (MEG) signal,

human DNA and face [13, 14], eye movement [15, 16], speech-evoked auditory

brainstem responses (s-ABR) signal [17, 18], heart rate [19, 20], and galvanic skin

response (GSR) signal [21, 22] can be mentioned.

Similarly, some studies worked on the analysis of EEG signal using fractal theory.

Our recent investigations that evaluated the in°uence of auditory [23, 24], olfactory

[25] and visual [26, 27] stimuli, brain diseases [28], body movements [29, 30] and aging

[31] on variations of EEG signal are worthy to be mentioned.

In addition, there are some studies that focused on the application of fractal

theory in the analysis of EMG signal. Our recent reported investigations that

decoded ¯nger [32, 33], hand [33–35], functional movements [33], and force patterns

[33], and also analyzed the e®ect of complexity of walking path on complexity of leg

muscle reaction [36] can be mentioned.

M. Soundirarajan et al.

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Therefore, in this study, we employ fractal theory to make a link between facial

muscles and brain activities by analysis of EMG and EEG signals, respectively. In

the following sections, ¯rst, we bring our method of analysis based on fractal theory.

Then, the procedure of data collection and analysis will be presented. The obtained

results from analysis will be brought thereafter. In the last section of this paper, we

bring the discussion about the results of analysis that will be followed up with some

future works.

2. Method

In this research, we would like to analyze the relationship between the activities of

facial muscle and brain in the case of rest and visual stimulation. For this purpose, we

chose di®erent moving visual stimuli in which a bold dot moves on di®erent random

paths. Therefore, on one hand, we have complex paths of movements for di®erent

visual stimuli. On the other hand, we record EEG and EMG signals as complex time

series. Therefore, in order to make a relationship between complex EEG and EMG

signals and complex visual stimuli, we employ fractal theory.

Fractal theory considers fractal dimension as the indicator of complexity of

objects. As was mentioned before, greater fractal dimension indicates greater com-

plexity in the object. Di®erent mathematical techniques have been developed to

calculate the fractal dimension. In this research, we employ box counting method to

calculate fractal dimension. In this method, the object (EEG and EMG signals in this

research) is covered with di®erent boxes in di®erent steps, where in each step, boxes

have the same size of ". The algorithm counts the number of used boxes (N) in each

step and ¯nally calculates the fractal dimension from the slope of regression line that

is ¯tted to log–log plot of number of boxes versus scale [37]:

FD ¼ lim"!0

logNð"Þlog 1="

: ð1Þ

In general, the fractal dimension of order c is de¯ned as [37]:

FDc ¼ lim"!0

1

c� 1

logPN

j¼1 rcj

log "; ð2Þ

in which, probability of occurrence is shown by rj:

rj ¼ limT!1

tjT; ð3Þ

where tj stands for the total time of occurrence in the jth bin, and T represents the

total time span of the time series.

We designed three dynamic visual stimuli in order to stimulate subjects. These

paths were designed based on their complexity. In the case of each stimulus, a bold

dot moves on a random path. Figure 1 shows the paths of movement for di®erent

visual stimuli. Table 1 brings the fractal dimension of these paths. As can be seen in

this table, by moving from ¯rst to second and third stimulus (path of movement), the

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Table 1. Fractal dimension of di®erent visual

stimuli.

Stimulus Fractal dimension

First stimulus 1.1649

Second stimulus 1.4194

Third stimulus 1.6365

Fig. 1. Di®erent random paths as dynamic visual stimuli.

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fractal dimension of stimulus increases. In other words, the complexity of ¯rst to

second and third stimulus increases.

In fact, by selection of these stimuli with di®erent complexities, we will be able to

investigate the relationship between the complex structures of EMG and EEG sig-

nals in di®erent levels of brain activity.

Therefore, we stimulate subjects using di®erent visual stimuli in di®erent steps of

experiments and accordingly analyze how the variations of complexity of EMG signal

are linked to the variations of complexity of EEG signal and also visual stimuli.

3. Data Collection and Analysis

All steps of conducted study have been approved by Internal Review Board of

Monash University with approval number 18626. The experiment was conducted

based on the approved guidelines. Fourteen healthy students (18–22 years old) from

the Monash University Malaysia participated in this experiment. Initially, we

checked subjects' health conditions by asking several questions from them. None of

subjects had any history or current neurological disorder. It should be noted that

subjects who agreed to participate in the experiment were prohibited from drinking

beverages that contain alcohol/ca®eine for 48 h before the experiments. It should be

noted that signed informed consent form was obtained from subjects before starting

the experiment.

During the data collection, we isolated subjects from external stimuli by conducting the

experiments in a quiet room. The subjects sit on a chair comfortably during the experi-

ment. We instructed them to only look at the moving dot during stimulus presentation.

They were free to look anywhere in the white computer screen during rest periods.

We used Emotiv Epocþ 14 channel mobile EEG and Shimmer EMG devices,

respectively, in order to collect EEG and EMG signals from subjects. We collected

EEG and EMG signals with the sampling frequency of 128Hz and 512Hz, respec-

tively. The setup of experiment that also includes the placement of EEG and EMG

electrodes on subject's brain and face is shown in Fig. 2.

Here we should note that the reported study in this paper is a part of a work that

investigated the in°uence of static and dynamic visual stimuli on variations of EEG

and EMG signals. We started the collection of EEG and EMG signals from subjects

in the rest condition. During this period, subjects closed their eyes for 30 s and we did

not apply any external stimulus on them. After that, we presented the ¯rst stimulus

to subjects. As was mentioned before, by starting the stimulus, a bold dot appeared

on the screen of computer and then moved on the ¯rst random path for 30 s. During

this period, subjects' eyes followed the dot without looking at any other part of the

screen. The computer screen showed a white background after the bold dot reached

the last point of path, which means the second rest period started. During this period

subjects rest for 30 s, they could close their eyes or look anywhere on the screen

without any restriction. This procedure was continued in the case of second and third

moving visual stimuli in order to record subjects' EEG and EMG signals. It should be

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noted that presentation of each stimulus took 30 s which followed by 30-s rest period.

We repeated the data collection in the second session from each subject in order to

consider the repeatability of results.

In the case of data analysis, we pre-processed the recorded EEG and EMG data by

applying ¯lters on them. This task was done in order to remove unwanted noises

from signals. For this purpose, we did band-path ¯ltering using the Butterworth

¯lter in MATLAB (R2019a). The ¯ltration of EEG and EMG signals was done in the

frequency range of 1–40Hz and 25–180Hz, respectively. Accordingly, we calculated

the fractal dimension of de-noised EEG and EMG signals using the written code in

MATLAB. The computation of fractal dimension was based on box counting algo-

rithm using boxes with sizes (1=2; 1=4; 1=8; � � �) as scaling factor.

We ran statistical analysis of the calculated values of fractal dimension for EEG

and EMG signals. For this purpose, one-way repeated measures ANOVA test was

conducted in order to test the signi¯cance of variations of EEG and EMG signals in

rest and stimulations. We also ran the post-hoc Tukey test in order to analyze the

signi¯cance of variations of complexity of EEG and EMG signals between di®erent

pairs of conditions. In addition, e®ect size analysis was conducted to check the e®ect

of each stimulus on variations on complexity of EEG and EMG signals. The sig-

ni¯cance level of 95% was chosen in the case of all statistical analyses.

Fig. 2. Setup of the experiment.

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4. Results

This section presents the result of analysis. It should be noted that out of 112 sets of

data that were recorded from 14 subjects in the case of rest and di®erent visual

stimuli, the fractal dimension of seven sets of data did not fall within the proper

range, and therefore, we excluded them from further investigations.

The fractal dimension of EEG signal in the case of rest and di®erent stimuli, and

the fractal dimension of visual stimuli are shown in Figs. 3(a) and 3(b), respectively.

Based on the presented results in Fig. 3(a), EEG signal has the lowest fractal

dimension in the rest condition. As was mentioned before, fractal dimension indicates

the complexity of signal and therefore based on this result, EEG signal has the lowest

complexity in the rest condition. As it is known, brain has the lower activity during

the rest condition, compared to the stimulation condition. Therefore, fractal di-

mension of EEG signal in the rest condition is lower than stimulation condition. The

trend of variations of fractal dimension of EEG signal between di®erent conditions

shows that by moving from rest to ¯rst, second and third visual stimuli, the fractal

dimension of EEG signal increases. In order words, it can be said that the complexity

(a)

(b)

Fig. 3. Fractal dimension of EEG signal in the case of rest and di®erent visual stimuli (a) and fractal

dimension of di®erent visual stimuli (b).

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of EEG signal increases as we present ¯rst, second and third visual stimuli to sub-

jects. By looking at the variations of fractal dimension of visual stimuli in Fig. 3(b), it

can be understood that the increment of complexity of visual stimuli from ¯rst to

second, and third stimulus is re°ected in the increment of complexity of EEG signal.

Therefore, we can say that the complexity of EEG signal is related to the complexity

of visual stimuli.

The result of ANOVA test (P -value ¼ 0.6239) indicates that the e®ect of visual

stimulation on variations of fractal dimension of EEG signal was not signi¯cant.

Here, we should note that the variations of complexity of EEG signal are very

dependent on the complexity of visual stimuli and presenting the visual stimuli with

greater complexity could potentially cause signi¯cant change in the complexity of

EEG signal.

The result of comparison of fractal dimension of EEG signal between di®erent pairs

of conditions is brought in Table 2. As can be seen in this table, we cannot see any

signi¯cant variation in the fractal dimension of EEG signal between di®erent conditions.

Table 2 also brings the results of e®ect size analysis. Based on the results, third

visual stimulus with the greatest complexity had the greatest e®ect on variations of

complexity of EEG signal.

Figure 4 shows the fractal dimension of EMG signal in the case of rest and

di®erent visual stimuli.

Table 2. Comparison of fractal dimension of EEG signal between

di®erent conditions.

Condition P -value E®ect size (rÞRest versus ¯rst stimulus 0.8121 0.12

Rest versus second stimulus 0.7230 0.12

Rest versus third stimulus 0.6272 0.16

First stimulus versus second stimulus 0.9992 0.01First stimulus versus third stimulus 0.9911 0.05

Second stimulus versus third stimulus 0.9976 0.02

Fig. 4. Fractal dimension of EMG signal in the case of rest and di®erent visual stimuli.

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Based on the presented results in Fig. 4, EMG signal has the lowest fractal

dimension in the rest condition. Therefore, EMG signal has the lowest complexity in

the rest condition. As it is known [36], muscles have the lower activity during the rest

condition, compared to the stimulation condition. Therefore, fractal dimension of

EMG signal in the rest condition is lower than stimulation condition. The trend of

variations of fractal dimension of EMG signal between di®erent conditions shows

that by moving from rest to ¯rst, second and third visual stimuli, the fractal di-

mension of EMG signal increases. In other words, it can be said that the complexity

of EMG signal increases as we present ¯rst, second and third visual stimuli to sub-

jects. By looking at the variations of fractal dimension of visual stimuli in Fig. 3(b), it

can be understood that the increment of complexity of visual stimuli from ¯rst to

second, and third stimulus is re°ected in the increment of complexity of EMG signal.

Therefore, we can say that the complexity of EMG signal is related to the complexity

of visual stimuli.

The comparison of obtained results in Fig. 4 with the obtained results in Fig. 3(a)

indicates that the variations of complexity of EMG signal between rest and stimu-

lation conditions are greater than the variations of complexity of EEG signal between

rest and stimulation conditions. The reason for this behavior is due to the activity of

facial muscles compared to the brain. In the rest condition, the brain has high activity

even it does not receive external stimuli, whereas the activity of facial muscles is very low

during the rest condition. Therefore, presenting the stimuli to subjects causes greater

variations in the complexity of EMG signal compared to EEG signal.

The result of ANOVA test (P -value ¼ 0.0000) indicates that the e®ect of visual

stimulation on variations of fractal dimension of EMG signal was signi¯cant. The

result of comparison of fractal dimension of EMG signal between di®erent pairs of

conditions is brought in Table 3. As can be seen in this table, the variations of fractal

dimension of EMG signal between rest and di®erent visual stimuli were signi¯cant.

However, we cannot see any signi¯cant variation in the fractal dimension of EMG

signal between other conditions. The reason of this behavior is due to the variations

of the activity level of facial muscles between rest and stimulation conditions. Since

during the rest condition, subject does not look around; therefore, facial muscles have

lower activity compared to the stimulation conditions. Therefore, the variations of

complexity of EMG signal between rest and stimulations are signi¯cant, compared to

Table 3. Comparison of fractal dimension of EMG signal betweendi®erent conditions.

Condition P -value E®ect size (rÞRest versus ¯rst stimulus 0.0000 0.89Rest versus second stimulus 0.0000 0.88

Rest versus third stimulus 0.0000 0.86

First stimulus versus second stimulus 0.9988 0.02First stimulus versus third stimulus 0.8698 0.10

Second stimulus versus third stimulus 0.9165 0.08

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the variations of complexity of EMG signal between stimulation conditions.

Table 3 also brings the results of e®ect size analysis. Based on the results, in

pairwise comparisons between stimulations, third visual stimulus with the greatest

complexity had the greatest e®ect on variations of complexity of EMG signal.

Comparison of Fig. 4 with Fig. 3(a) shows that the variations of complexity of

EMG signal are related to the variations of complexity of EEG signal. In other words,

it can be said that a greater change in the complexity of visual stimulus causes a

greater change in the complexity of EEG signal and accordingly EMG signal.

Therefore, we can conclude that the activity of facial muscle is linked to the brain

activity.

5. Discussion

In this research, we analyzed the relationship between facial muscles and brain

activities in rest and stimulation conditions. For this purpose, we bene¯ted from

fractal theory in order to analyze the complexity EMG signal versus EEG signal

during rest and in the case of di®erent moving visual stimuli with di®erent

complexities.

Based on the results of analysis, EEG signal has the lowest complexity in the rest

condition. The value of complexity of EEG signal increased as we presented visual

stimuli with greater complexities. The analysis of the fractal dimension of EMG

signal showed the similar results with the variations of fractal dimension of EEG

signal between di®erent conditions. Based on the results, EMG signal has the lowest

complexity in the rest condition, and by presenting visual stimuli with greater

complexities, the complexity of EMG signal increased. The result of statistical

analysis also con¯rmed that increasing the complexity of visual stimuli causes greater

e®ect on the variations of complexity of EMG and EEG signals. Therefore, based on

the similar variations of complexity of EMG and EEG signals in di®erent conditions,

we can conclude that facial muscle activities are related to the brain activity.

In order to elaborate the observed behavior, we can refer to activity of nervous

system. As was mentioned before, facial muscles are controlled by the brain through

the nervous system. Therefore, when human receives a visual stimulus with greater

complexity that causes greater complexity in EEG signal, brain sends the message

about the stimulus to facial muscles, which leads to the greater complexity in EMG

signal. In fact, this investigation in novel as no evidence of research shows the

analysis of the relation between facial muscles and brain activities by evaluating

EMG and EEG signals.

The conducted investigation in this study can further analyze the relationship

between facial muscles and brain activities in the case of other types of stimuli such

as olfactory stimuli. For instance, we can analyze how EMG signal is related to EEG

signal when subject sni®s di®erent odors with di®erent complexities.

In this research, we examined the relationship between facial muscles and brain

activities in the case of healthy subjects. In further research, we can investigate the

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link between facial muscles and brain activities in the case of subjects with di®erent

brain or muscle disorders. Since brain controls facial muscle activities, the brain

disorder should a®ect the facial muscle activities. By decoding of the relation

between facial muscles and brain activities in the case of these patients, we can

understand the e®ect of brain disorder on the controlling role of brain on facial

muscles activities. The result of this investigation has great importance in rehabili-

tation science.

In another future study, we can work on the modeling of the relation between

EMG and EEG signals and also external stimuli. For this purpose, we can bene¯t

from di®erent mathematical models [38–40] or algorithms [41–43]. The developed

model will potentially enable us to predict EMG response based on EEG response

and the complexity of applied external stimulus. In overall, all these investigations

have great importance in decoding of the relationship between brain and muscles

activities that has great impact on rehabilitation.

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