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Complexity-based decoding of brain-skin relation in response to olfactory stimuli
Computer Methods and Programs in Biomedicine 184 (2020) 105293
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Computer Methods and Programs in Biomedicine journal homepage: www.elsevier.com/locate/cmpb
Complexity-based decoding of brain-skin relation in response to olfactory stimuli Shafiul Omam a, Mohammad Hossein Babini a, Sue Sim a, Rui Tee b, Visvamba Nathan a, Hamidreza Namazi a,c,∗ a b c
School of Engineering, Monash University, Selangor, Malaysia School of Pharmacy, Monash University, Selangor, Malaysia Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
a r t i c l e
i n f o
Article history: Received 17 September 2019 Revised 12 December 2019 Accepted 20 December 2019
Keywords: Skin Brain Galvanic skin response (GSR) Electroencephalography (EEG) signals Fractal theory Complexity
a b s t r a c t Background and Objective: Human body is covered with skin in different parts. In fact, skin reacts to different changes around human. For instance, when the surrounding temperature changes, human skin will react differently. It is known that the activity of skin is regulated by human brain. In this research, for the first time we investigate the relation between the activities of human skin and brain by mathematical analysis of Galvanic Skin Response (GSR) and Electroencephalography (EEG) signals. Method: For this purpose, we employ fractal theory and analyze the variations of fractal dimension of GSR and EEG signals when subjects are exposed to different olfactory stimuli in the form of pleasant odors. Results: Based on the obtained results, the complexity of GSR signal changes with the complexity of EEG signal in case of different stimuli, where by increasing the molecular complexity of olfactory stimuli, the complexity of EEG and GSR signals increases. The results of statistical analysis showed the significant effect of stimulation on variations of complexity of GSR signal. In addition, based on effect size analysis, fourth odor with greatest molecular complexity had the greatest effect on variations of complexity of EEG and GSR signals. Conclusion: Therefore, it can be said that human skin reaction changes with the variations in the activity of human brain. The result of analysis in this research can be further used to make a model between the activities of human skin and brain that will enable us to predict skin reaction to different stimuli. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Human body is covered with skin in different parts. Human skin is reactive to different conditions around human. For instance, we can sense temperature or stress with our skin. Brain as the main controller of human body adjust all reactions of skin. In fact, brain does this task by sending impulses through the nervous system. During years, several researchers have worked on analysis of human skin reaction in different conditions. The major part of works belongs to the analysis of Galvanic Skin Response (GSR) signal as the indicator of skin reaction. The works that analyzed GSR signal to investigate the effect of stimulus set size on the ∗ Corresponding author at: School of Engineering, Monash University, Selangor, Malaysia. E-mail address: [email protected] (H. Namazi).
https://doi.org/10.1016/j.cmpb.2019.105293 0169-2607/© 2019 Elsevier B.V. All rights reserved.
efficiency of detection of information [1], to discriminate real from fake smiles [2], investigated how classical Turkish music can affect galvanic skin response and skin temperature of schizophrenic patients [3], did GSR signal analysis for detection of stress at a particular time [4], investigated the effect of visual, auditory and haptic stimuli on skin conductance response [5], examined facilitatory and inhibitory effects of instructions on the GSR index of the orienting reflex [6], analyzed how skin conductance changes due to a loud white noise and emotional pictures [7], and developed an algorithm for model-based analysis of evoked skin conductance responses [8], are noteworthy to be mentioned. Besides all the works done on analysis of skin reaction by investigating about GSR signal, no work has been reported yet that studied the relation between the activities of skin and brain. Since brain controls skin’s activity, there should be a relation between brain and skin reactions. For this purpose, in this research we analyze how the activity of the skin is related to the activity of brain.
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Complexity is the concept that can be used to define the structure of EEG and GSR signals. In fact, complexity characterises the behaviour of a system that has many parts, which interact with each other in highly variable way [9]. During years, scientists employed different techniques and methods [10] to analyze, model and sometimes to forecast the behaviour of different complex systems. These systems have wide variety in the form of trajectories / time series to spatial patterns, such as animal groups [11], biological data [12], and physiological signal [13]. Since EEG and GSR signals have complex structures, in this research we employ fractal theory for our analysis. The term “fractal” that was first introduced by mathematician Benoit Mandelbrot is used to define self-similar or self-affine objects with repeating patterns [14]. Self-similar objects can be identified easily from their repeating patterns. However, self-affine objects have similarity that is hidden in their complex structures and cannot be identified easily and therefore needs mathematical analysis. In fact, self-affine fractals have different scaling factors in different levels, whereas self-similar objects follow same scaling factor. Physiological signals such as EEG and GSR signals are considered as self-affine fractals. Fractal dimension is the main tool to define the complexity of fractal objects, where its greater values indicate greater complexity [15]. Many researchers have applied fractal analysis to investigate about the complex structure of different physiological signals and patterns. The works that employed fractal analysis to investigate about the complexity of Electromyogram (EMG) signal [1618], heart rate [19-20], human gait [21-22], Magnetoencephalography (MEG) signal [23-24], respiration signal [25-26], eye movement [27-29], speech-evoked Auditory Brainstem Responses (sABRs) [30], human face and DNA variations [31-32], are noteworthy to be mentioned. Besides that, fractal-based modelling of different physiological processes also has aroused the attention of some researchers. The reported studies that worked on the modelling of gait variability [33], EMG signal [34], cardiac electrical propagation [35], and EEG signal [36] using fractal theory can be mentioned. Similarly, there have been many works that employed fractal theory for analysis of EEG signal. The reported works that analyzed the effect of visual [37] and auditory stimuli [38], aging [39], brain diseases [40] and body movements [41] on variations of EEG signal using fractal theory, are noteworthy to be mentioned. On the other hand, by looking at literature, we can only find one reported work that analyzed skin reaction signal using fractal theory. This work identified distraction of drivers during a driving experiment on-the-road by fractal analysis of GSR signal [42]. Therefore, since there has not been any work that made a relation between skin and brain activities by analysis of GSR and EEG signals, in this research for the first time we employ fractal analysis to investigate how the reaction of skin is linked to the reaction of brain. In the rest of this paper, firstly we will bring our method of analysis that is based on fractal theory. After that, we discuss about data collection and analysis with details. The obtained results from analysis will be provided thereafter. In the last section of this paper, we bring conclusion and discussion about the obtained results and draw some future works. 2. Method In this paper, we aim to analyze the coupling between human brain and skin activities in response to different olfactory stimuli. In order to do this job, we employ complexity concept to relate the complexity of skin signal to the complexity of brain signal and olfactory stimuli. In case of EEG signal (brain reaction) and GSR signal (skin reaction), we consider fractal dimension as indicator
Table 1 The chemical formula and molecular complexity of different odors (stimulus). Odor
Chemical formula
Molecular complexity
Pineapple flavour Banana flavour Vanilla flavour Lemon flavour
C6H12O2 C7H14O2 C8H8O3 C10H16
68.9 86.9 135 163
of complexity. It is known that greater value of fractal dimension stands for greater level of complexity. There are several methods that have been used for computation of fractal dimension. These methods are mainly based on entropy concept. In this research, we use box counting method for our analysis. In this method, the object (signal or image) is covered with number of boxes that have the same size of ε . Then, the algorithm calculates the number of used boxes (N). This process will be repeated several times and the box size will be changed in each step. Finally, the fractal dimension of object is calculated from the slope of regression line that is fitted to log-log plot of number of boxes versus scale [43]:
F D = lim
ε →0
log N (ε ) log 1/ε
(1)
As can be seen in (1), fractals show power-law distribution that is dependent on the number of boxes (N) and scaling factor (ε ) [44]. In general, fractal dimension in Eq. (1) is a special case of a continuous spectrum of generalized dimensions of order c [45]:
N c 1 log j=1 rj FDc = lim ε →0 c − 1 log ε
(2)
where rj as the probability of occurrence is defined as:
rj = lim
T→∞
tj T
(3)
where tj and T represents the total time of occurrence in the jth bin and the total time span of the time series respectively. On the other hand, in order to quantify the complexity of olfactory stimuli, we consider molecular complexity. Four odors (as olfactory stimuli) were selected based on their respective molecular complexities that were calculated using Bertz Formula. Bertz Formula is composed of two terms. The first term (Cn ) measures the skeletal complexity as a function of bond connectivity (ƞ), and the second term (Ce ) is a function of diversity of atoms in the molecule [46]. In general, smaller and/or higher symmetrical molecules that have fewer distinct elements have lower complexity [47].
C = Cn + Ce
(4)
For our experiment, we chose four pleasant odors as olfactory stimuli. As can be seen in Fig. 1, first to fourth odors include pineapple, banana, vanilla and lemon flavour that have different molecular complexities. In fact, as we move from pineapple to lemon, complexity of odors increases. The reason of choosing these stimuli was due to their complexities that enable us to investigate the relation between the complexity of EEG, GSR and odors. The molecular complexities of these odors are brought in Table 1. Therefore, in case of different odors, we analyze how the variations of complexity of EEG and GSR signals are coupled. We stimulate subjects using different odors in different steps of experiment and then investigate how the variations of fractal dimension of EEG and GSR signals are related. The result of the variations in the fractal dimension for brain and skin signals will be discussed in relation with the variations of molecular complexity of odors.
S. Omam, M.H. Babini and S. Sim et al. / Computer Methods and Programs in Biomedicine 184 (2020) 105293
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Fig. 1. The molecular structure of different odors (stimulus).
2.1. Data collection and analysis All procedures of recruiting subjects and conducting the experiment were approved by Monash University Human Research Ethics Committee (MUHREC) with ethical number 19,851. The study was carried out in accordance with the approved guidelines. We have conducted the experiment on eight healthy students (18–22 years old) from Monash University Malaysia. After explaining the experiment to subjects, we asked them several questions about their health conditions. The subjects that were found suitable for experiments signed the consent form and we started the experiment on them. In order to isolate subjects from unwanted external stimuli that can affect their EEG and GSR signals, we conducted the experiment in an quiet room. Subjects were sitting on a chair comfortably during the experiment and instructed to focus on sniffing the odors without doing any other job. We have non-invasively recorded EEG and GSR signals from subjects using Muse headband and Shimmer GSR devices with the sampling frequency of 256 Hz and 51.2 Hz respectively. The EEG device has been put on subject’s head, and two electrodes of GSR device were connected to subject’s right-hand fingers. We put 0.01 ml of each fruit flavour (Fig. 2), as olfactory stimulus, on a small piece of paper and kept near the nose of subjects (in front of their lips) and asked them to sniff it. Initially, we recorded EEG and GSR signals of subjects for one minute while they were at rest (closed-eyes condition), without
receiving any external stimulus. After that, we presented the first odor to subjects for one minute and they sniffed it while we recorded their EEG and GSR signals. Then, subjects had one minute of rest again while they sniffed coffee. In this way, brain forgot the presented odor and got ready for next olfactory stimulation. We continued this procedure to collect EEG and GSR signals in case of second, third and fourth odors with considering one minute of rest between stimulations. We repeated the data collection in the second session for each subject in order to consider the repeatability of results. It should be noted that although we recorded EEG signal from four channels, however for our data analysis we only analysed the data from AF7 and AF8 channels as they have closest positions to the olfactory bulb of the brain. Before fractal analysis, we did preprocessing on recorded EEG and GSR data in order to filter unwanted noises. For this purpose, we wrote a set of codes in MATLAB based on Butterworth filter. The frequency band of 0.5–30 Hz and cut-off frequency of 20 Hz were chosen for filtering of EEG and GSR signals respectively. We processed the filtered EEG and GSR signals by computing their fractal dimension in MATLAB using box counting algorithm. The computation of fractal dimension was based on box counting algorithm using boxes with sizes ( 12 , 14 , 18 . . .) as scaling factor. It should be noted that beside one minute of data collection duration in case of rest and each odor stimulation, since EEG and GSR devices sometimes did not have consistent sampling frequency, we had few seconds difference in the duration of collected data in case of different subjects, and therefore we considered 54 s of data for our processing in case of all subjects. We also did statistical analysis on the obtained results. We run one-way ANOVA test to compare the mean values of fractal dimension of EEG and GSR signals between different conditions. In order to check the effect of rest and different stimulation on fractal dimension of EEG and GSR signals, we run effect size analysis. In addition, we compared the significance of difference in fractal dimension of EEG and GSR signals between different pairs of conditions using Post-hoc Tukey test. The significance of level of 95% has been considered in case of all statistical analysis. 3. Results
Fig. 2. Fruits flavours as olfactory stimuli.
In this section, we report the result of our analysis. It should be noted that out of eight subjects that participated in this study and gave us total of eighty sets of data in case of rest and stimulation, five sets of data did not fall within the proper range of values and therefore we excluded those data from our analysis. Fig. 3 shows
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Fig. 4. The fractal dimension of GSR signal in case of different conditions.
Table 3 p-value and effect size (r) in case of comparison of fractal dimension of GSR signal between different conditions.
Fig. 3. The fractal dimension of EEG signal in different conditions (a) and the molecular complexity of odors (b). Table 2 p-value and effect size (r) in case of comparison of fractal dimension of EEG signal between different conditions. Comparison
p-value
Effect size (r)
Rest vs. first odor Rest vs. second odor Rest vs. third odor Rest vs. fourth odor First odor vs. second odor First odor vs. third odor First odor vs. fourth odor Second odor vs. third odor Second odor vs. fourth odor Third odor vs. fourth odor
1.0000 1.0000 0.9907 0.9608 1.0000 0.9960 0.9791 0.9965 0.9790 0.9995
0.0116 0.0167 0.0709 0.1261 0.0069 0.0677 0.1331 0.0571 0.1118 0.0373
the variations of fractal dimension of EEG signal (a) and the molecular complexity of odors (b) As it is clear in this figure, the fractal dimension of EEG signal increases as we move from rest to first, second, third and fourth stimulus. In fact, the increment in the molecular complexity of odors (Fig. 3(b)) causes the increment in the fractal dimension of EEG signal. Since fractal dimension shows the complexity of signal, therefore it can be said that presenting of an odor with greater molecular complexity causes greater complexity in subjects’ EEG signals. The result of ANOVA test shows that the effect of olfactory stimuli on fractal dimension of EEG signal was not significant (pvalue=0.9544). In fact, the sensitivity of variations of complexity of EEG signal also depends on the intensity and duration of stimulation, and therefore changing of these parameters can cause the significant effect on variations of fractal dimension of EEG signal. P-values for comparison of fractal dimension between different pairs of conditions are brought in Table 2. As can be seen in this
Comparison
p-value
Effect size (r)
Rest vs. first odor Rest vs. second odor Rest vs. third odor Rest vs. fourth odor First odor vs. second odor First odor vs. third odor First odor vs. fourth odor Second odor vs. third odor Second odor vs. fourth odor Third odor vs. fourth odor
0.1010 0.0592 0.0393 0.0056 0.9963 0.9998 0.9278 1.0000 0.9337 0.9595
0.5886 0.4988 0.5046 0.5861 0.0125 0.0323 0.1484 0.0177 0.1208 0.1020
table, the variations of fractal dimension between each pair of conditions was in-significant. By referring to the results of effect size analysis in this table, we can see that fourth odor had the greatest effect on variations of fractal dimension of EEG signal. Fig. 4 shows the variations of fractal dimension of GSR signal. Similar with Fig. 3(a), as can be seen in Fig. 4, by moving from rest to first, second, third and fourth stimulus, the fractal dimension of GSR signal increases. In fact, the increment in the molecular complexity of odors (Fig. 3(b)) causes the increment in the fractal dimension of GSR signal. Therefore, it can be said that presenting of an odor with greater molecular complexity causes greater complexity in subjects’ GSR signals. The result of ANOVA test shows the significant effect (p-value=0.0075, F-value=3.7947) of olfactory stimuli on fractal dimension of GSR signal. In comparison of fractal dimension of GSR signal with the fractal dimension of EEG signal, as it is clear in Fig. 4 and Fig. 3(a), the fractal dimension of GSR changes greater as we move from rest to fourth stimulus compared to the fractal dimension of EEG signal. Therefore, it can be said that olfactory stimuli had greater effect on GSR signal compared to EEG signal, or in other words, skin showed greater reaction compared to the brain in case of different olfactory stimuli. P-values for comparison of fractal dimension of GSR signal between different pairs of conditions are brought in Table 3. As can be seen in this table, the variations of fractal dimension between rest and third, and fourth odor was significant. However, there was not any significant difference in other comparisons. In addition, the results of effect size analysis in this table indicate that in general, fourth odor had the greatest effect on variations of fractal dimension of GSR signal. Therefore, the obtained results for fractal dimension of EEG and GSR signal in case of rest, first, second, third and fourth stimuli indicate that the variations of complexity of EEG and GSR signal are related to each other, as well as to the variations of complexity of odors as olfactory stimuli.
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4. Discussion In this paper, we investigated the relation between human brain and skin responses in case of rest and different olfactory stimulation. For this purpose, we computed the fractal dimension of EEG and GSR signals in different conditions. The applied olfactory stimuli were four odors that had increasing molecular complexities that enabled us to analyze the variations of complexity of EEG and GSR signals in case of variations of complexity of odors. The result of analysis showed that the fractal dimension of EEG signal increases when we move from rest to first, second, third and fourth odor. Similarly, the fractal dimension of GSR signal increases with this trend. Therefore, it can be said that EEG and GSR signals show greater variations in their complexities as we make greater variations in the molecular complexity of olfactory stimuli. In addition, statistical analysis showed that the effect of olfactory stimulation on complexity of GSR signals is significant. In general, it can be concluded that the variations of complexity of GSR signal are coupled with the variations of complexity of EEG signal, where by moving from rest to first, second, third and fourth stimulus, the greater variations in the complexity of EEG and GSR signal can be obtained. The coupling between brain and skin reactions that was investigated in this research is one step forward compared to the reported works [3, 5, 37-38] that only analysed the reaction of brain or skin in rest or in response the external stimuli without making any relation between them. The investigation in this research has been conducted in case of stimulation using different olfactory stimuli. In further research, we can extend our analysis to study the relation between brain and skin activities in case of other types of stimuli. For instance, we can apply auditory stimuli in the form of music to subjects and investigate how complexity of GSR signal changes with the complexity of EEG signal and even with the complexity of music. We can also do similar analysis in case of other physiological signals of human in order to investigate the relation between other related organs. For instance, we can do fractal analysis on ECG and EEG signals in order to find out the relation between human heart and brain responses in case of different stimuli. Since brain controls all parts of human body, we should be able to find the proper relation between them. It also should be noted that these analyses are not limited to healthy subjects. We can also extend our analysis in case of patients suffering from different brain or skin disorders and investigate how their brain and skin reactions are related. Mathematical modelling [48-49] is the ultimate goal in this area of research which enables us to mathematically formulate the relation between brain and skin activities in relation with applied stimuli. In this way, we should be able to predict the responses of brain and skin base on applied stimuli. In overall, all these efforts can help scientists for better understanding of the relation between brain and skin activities. Declaration of Competing Interest None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cmpb.2019.105293. References [1] I. Lieblich, S. Kugelmass, G. Ben-Shakhar, Efficiency of GSR detection of information as a function of stimulus set size, Psychophysiology 6 (5) (1970) 601– 608, doi:10.1111/j.1469-8986.1970.tb02249.x.
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Computer Methods and Programs in Biomedicine journal homepage: www.elsevier.com/locate/cmpb
Complexity-based decoding of brain-skin relation in response to olfactory stimuli Shafiul Omam a, Mohammad Hossein Babini a, Sue Sim a, Rui Tee b, Visvamba Nathan a, Hamidreza Namazi a,c,∗ a b c
School of Engineering, Monash University, Selangor, Malaysia School of Pharmacy, Monash University, Selangor, Malaysia Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
a r t i c l e
i n f o
Article history: Received 17 September 2019 Revised 12 December 2019 Accepted 20 December 2019
Keywords: Skin Brain Galvanic skin response (GSR) Electroencephalography (EEG) signals Fractal theory Complexity
a b s t r a c t Background and Objective: Human body is covered with skin in different parts. In fact, skin reacts to different changes around human. For instance, when the surrounding temperature changes, human skin will react differently. It is known that the activity of skin is regulated by human brain. In this research, for the first time we investigate the relation between the activities of human skin and brain by mathematical analysis of Galvanic Skin Response (GSR) and Electroencephalography (EEG) signals. Method: For this purpose, we employ fractal theory and analyze the variations of fractal dimension of GSR and EEG signals when subjects are exposed to different olfactory stimuli in the form of pleasant odors. Results: Based on the obtained results, the complexity of GSR signal changes with the complexity of EEG signal in case of different stimuli, where by increasing the molecular complexity of olfactory stimuli, the complexity of EEG and GSR signals increases. The results of statistical analysis showed the significant effect of stimulation on variations of complexity of GSR signal. In addition, based on effect size analysis, fourth odor with greatest molecular complexity had the greatest effect on variations of complexity of EEG and GSR signals. Conclusion: Therefore, it can be said that human skin reaction changes with the variations in the activity of human brain. The result of analysis in this research can be further used to make a model between the activities of human skin and brain that will enable us to predict skin reaction to different stimuli. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Human body is covered with skin in different parts. Human skin is reactive to different conditions around human. For instance, we can sense temperature or stress with our skin. Brain as the main controller of human body adjust all reactions of skin. In fact, brain does this task by sending impulses through the nervous system. During years, several researchers have worked on analysis of human skin reaction in different conditions. The major part of works belongs to the analysis of Galvanic Skin Response (GSR) signal as the indicator of skin reaction. The works that analyzed GSR signal to investigate the effect of stimulus set size on the ∗ Corresponding author at: School of Engineering, Monash University, Selangor, Malaysia. E-mail address: [email protected] (H. Namazi).
https://doi.org/10.1016/j.cmpb.2019.105293 0169-2607/© 2019 Elsevier B.V. All rights reserved.
efficiency of detection of information [1], to discriminate real from fake smiles [2], investigated how classical Turkish music can affect galvanic skin response and skin temperature of schizophrenic patients [3], did GSR signal analysis for detection of stress at a particular time [4], investigated the effect of visual, auditory and haptic stimuli on skin conductance response [5], examined facilitatory and inhibitory effects of instructions on the GSR index of the orienting reflex [6], analyzed how skin conductance changes due to a loud white noise and emotional pictures [7], and developed an algorithm for model-based analysis of evoked skin conductance responses [8], are noteworthy to be mentioned. Besides all the works done on analysis of skin reaction by investigating about GSR signal, no work has been reported yet that studied the relation between the activities of skin and brain. Since brain controls skin’s activity, there should be a relation between brain and skin reactions. For this purpose, in this research we analyze how the activity of the skin is related to the activity of brain.
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Complexity is the concept that can be used to define the structure of EEG and GSR signals. In fact, complexity characterises the behaviour of a system that has many parts, which interact with each other in highly variable way [9]. During years, scientists employed different techniques and methods [10] to analyze, model and sometimes to forecast the behaviour of different complex systems. These systems have wide variety in the form of trajectories / time series to spatial patterns, such as animal groups [11], biological data [12], and physiological signal [13]. Since EEG and GSR signals have complex structures, in this research we employ fractal theory for our analysis. The term “fractal” that was first introduced by mathematician Benoit Mandelbrot is used to define self-similar or self-affine objects with repeating patterns [14]. Self-similar objects can be identified easily from their repeating patterns. However, self-affine objects have similarity that is hidden in their complex structures and cannot be identified easily and therefore needs mathematical analysis. In fact, self-affine fractals have different scaling factors in different levels, whereas self-similar objects follow same scaling factor. Physiological signals such as EEG and GSR signals are considered as self-affine fractals. Fractal dimension is the main tool to define the complexity of fractal objects, where its greater values indicate greater complexity [15]. Many researchers have applied fractal analysis to investigate about the complex structure of different physiological signals and patterns. The works that employed fractal analysis to investigate about the complexity of Electromyogram (EMG) signal [1618], heart rate [19-20], human gait [21-22], Magnetoencephalography (MEG) signal [23-24], respiration signal [25-26], eye movement [27-29], speech-evoked Auditory Brainstem Responses (sABRs) [30], human face and DNA variations [31-32], are noteworthy to be mentioned. Besides that, fractal-based modelling of different physiological processes also has aroused the attention of some researchers. The reported studies that worked on the modelling of gait variability [33], EMG signal [34], cardiac electrical propagation [35], and EEG signal [36] using fractal theory can be mentioned. Similarly, there have been many works that employed fractal theory for analysis of EEG signal. The reported works that analyzed the effect of visual [37] and auditory stimuli [38], aging [39], brain diseases [40] and body movements [41] on variations of EEG signal using fractal theory, are noteworthy to be mentioned. On the other hand, by looking at literature, we can only find one reported work that analyzed skin reaction signal using fractal theory. This work identified distraction of drivers during a driving experiment on-the-road by fractal analysis of GSR signal [42]. Therefore, since there has not been any work that made a relation between skin and brain activities by analysis of GSR and EEG signals, in this research for the first time we employ fractal analysis to investigate how the reaction of skin is linked to the reaction of brain. In the rest of this paper, firstly we will bring our method of analysis that is based on fractal theory. After that, we discuss about data collection and analysis with details. The obtained results from analysis will be provided thereafter. In the last section of this paper, we bring conclusion and discussion about the obtained results and draw some future works. 2. Method In this paper, we aim to analyze the coupling between human brain and skin activities in response to different olfactory stimuli. In order to do this job, we employ complexity concept to relate the complexity of skin signal to the complexity of brain signal and olfactory stimuli. In case of EEG signal (brain reaction) and GSR signal (skin reaction), we consider fractal dimension as indicator
Table 1 The chemical formula and molecular complexity of different odors (stimulus). Odor
Chemical formula
Molecular complexity
Pineapple flavour Banana flavour Vanilla flavour Lemon flavour
C6H12O2 C7H14O2 C8H8O3 C10H16
68.9 86.9 135 163
of complexity. It is known that greater value of fractal dimension stands for greater level of complexity. There are several methods that have been used for computation of fractal dimension. These methods are mainly based on entropy concept. In this research, we use box counting method for our analysis. In this method, the object (signal or image) is covered with number of boxes that have the same size of ε . Then, the algorithm calculates the number of used boxes (N). This process will be repeated several times and the box size will be changed in each step. Finally, the fractal dimension of object is calculated from the slope of regression line that is fitted to log-log plot of number of boxes versus scale [43]:
F D = lim
ε →0
log N (ε ) log 1/ε
(1)
As can be seen in (1), fractals show power-law distribution that is dependent on the number of boxes (N) and scaling factor (ε ) [44]. In general, fractal dimension in Eq. (1) is a special case of a continuous spectrum of generalized dimensions of order c [45]:
N c 1 log j=1 rj FDc = lim ε →0 c − 1 log ε
(2)
where rj as the probability of occurrence is defined as:
rj = lim
T→∞
tj T
(3)
where tj and T represents the total time of occurrence in the jth bin and the total time span of the time series respectively. On the other hand, in order to quantify the complexity of olfactory stimuli, we consider molecular complexity. Four odors (as olfactory stimuli) were selected based on their respective molecular complexities that were calculated using Bertz Formula. Bertz Formula is composed of two terms. The first term (Cn ) measures the skeletal complexity as a function of bond connectivity (ƞ), and the second term (Ce ) is a function of diversity of atoms in the molecule [46]. In general, smaller and/or higher symmetrical molecules that have fewer distinct elements have lower complexity [47].
C = Cn + Ce
(4)
For our experiment, we chose four pleasant odors as olfactory stimuli. As can be seen in Fig. 1, first to fourth odors include pineapple, banana, vanilla and lemon flavour that have different molecular complexities. In fact, as we move from pineapple to lemon, complexity of odors increases. The reason of choosing these stimuli was due to their complexities that enable us to investigate the relation between the complexity of EEG, GSR and odors. The molecular complexities of these odors are brought in Table 1. Therefore, in case of different odors, we analyze how the variations of complexity of EEG and GSR signals are coupled. We stimulate subjects using different odors in different steps of experiment and then investigate how the variations of fractal dimension of EEG and GSR signals are related. The result of the variations in the fractal dimension for brain and skin signals will be discussed in relation with the variations of molecular complexity of odors.
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Fig. 1. The molecular structure of different odors (stimulus).
2.1. Data collection and analysis All procedures of recruiting subjects and conducting the experiment were approved by Monash University Human Research Ethics Committee (MUHREC) with ethical number 19,851. The study was carried out in accordance with the approved guidelines. We have conducted the experiment on eight healthy students (18–22 years old) from Monash University Malaysia. After explaining the experiment to subjects, we asked them several questions about their health conditions. The subjects that were found suitable for experiments signed the consent form and we started the experiment on them. In order to isolate subjects from unwanted external stimuli that can affect their EEG and GSR signals, we conducted the experiment in an quiet room. Subjects were sitting on a chair comfortably during the experiment and instructed to focus on sniffing the odors without doing any other job. We have non-invasively recorded EEG and GSR signals from subjects using Muse headband and Shimmer GSR devices with the sampling frequency of 256 Hz and 51.2 Hz respectively. The EEG device has been put on subject’s head, and two electrodes of GSR device were connected to subject’s right-hand fingers. We put 0.01 ml of each fruit flavour (Fig. 2), as olfactory stimulus, on a small piece of paper and kept near the nose of subjects (in front of their lips) and asked them to sniff it. Initially, we recorded EEG and GSR signals of subjects for one minute while they were at rest (closed-eyes condition), without
receiving any external stimulus. After that, we presented the first odor to subjects for one minute and they sniffed it while we recorded their EEG and GSR signals. Then, subjects had one minute of rest again while they sniffed coffee. In this way, brain forgot the presented odor and got ready for next olfactory stimulation. We continued this procedure to collect EEG and GSR signals in case of second, third and fourth odors with considering one minute of rest between stimulations. We repeated the data collection in the second session for each subject in order to consider the repeatability of results. It should be noted that although we recorded EEG signal from four channels, however for our data analysis we only analysed the data from AF7 and AF8 channels as they have closest positions to the olfactory bulb of the brain. Before fractal analysis, we did preprocessing on recorded EEG and GSR data in order to filter unwanted noises. For this purpose, we wrote a set of codes in MATLAB based on Butterworth filter. The frequency band of 0.5–30 Hz and cut-off frequency of 20 Hz were chosen for filtering of EEG and GSR signals respectively. We processed the filtered EEG and GSR signals by computing their fractal dimension in MATLAB using box counting algorithm. The computation of fractal dimension was based on box counting algorithm using boxes with sizes ( 12 , 14 , 18 . . .) as scaling factor. It should be noted that beside one minute of data collection duration in case of rest and each odor stimulation, since EEG and GSR devices sometimes did not have consistent sampling frequency, we had few seconds difference in the duration of collected data in case of different subjects, and therefore we considered 54 s of data for our processing in case of all subjects. We also did statistical analysis on the obtained results. We run one-way ANOVA test to compare the mean values of fractal dimension of EEG and GSR signals between different conditions. In order to check the effect of rest and different stimulation on fractal dimension of EEG and GSR signals, we run effect size analysis. In addition, we compared the significance of difference in fractal dimension of EEG and GSR signals between different pairs of conditions using Post-hoc Tukey test. The significance of level of 95% has been considered in case of all statistical analysis. 3. Results
Fig. 2. Fruits flavours as olfactory stimuli.
In this section, we report the result of our analysis. It should be noted that out of eight subjects that participated in this study and gave us total of eighty sets of data in case of rest and stimulation, five sets of data did not fall within the proper range of values and therefore we excluded those data from our analysis. Fig. 3 shows
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Fig. 4. The fractal dimension of GSR signal in case of different conditions.
Table 3 p-value and effect size (r) in case of comparison of fractal dimension of GSR signal between different conditions.
Fig. 3. The fractal dimension of EEG signal in different conditions (a) and the molecular complexity of odors (b). Table 2 p-value and effect size (r) in case of comparison of fractal dimension of EEG signal between different conditions. Comparison
p-value
Effect size (r)
Rest vs. first odor Rest vs. second odor Rest vs. third odor Rest vs. fourth odor First odor vs. second odor First odor vs. third odor First odor vs. fourth odor Second odor vs. third odor Second odor vs. fourth odor Third odor vs. fourth odor
1.0000 1.0000 0.9907 0.9608 1.0000 0.9960 0.9791 0.9965 0.9790 0.9995
0.0116 0.0167 0.0709 0.1261 0.0069 0.0677 0.1331 0.0571 0.1118 0.0373
the variations of fractal dimension of EEG signal (a) and the molecular complexity of odors (b) As it is clear in this figure, the fractal dimension of EEG signal increases as we move from rest to first, second, third and fourth stimulus. In fact, the increment in the molecular complexity of odors (Fig. 3(b)) causes the increment in the fractal dimension of EEG signal. Since fractal dimension shows the complexity of signal, therefore it can be said that presenting of an odor with greater molecular complexity causes greater complexity in subjects’ EEG signals. The result of ANOVA test shows that the effect of olfactory stimuli on fractal dimension of EEG signal was not significant (pvalue=0.9544). In fact, the sensitivity of variations of complexity of EEG signal also depends on the intensity and duration of stimulation, and therefore changing of these parameters can cause the significant effect on variations of fractal dimension of EEG signal. P-values for comparison of fractal dimension between different pairs of conditions are brought in Table 2. As can be seen in this
Comparison
p-value
Effect size (r)
Rest vs. first odor Rest vs. second odor Rest vs. third odor Rest vs. fourth odor First odor vs. second odor First odor vs. third odor First odor vs. fourth odor Second odor vs. third odor Second odor vs. fourth odor Third odor vs. fourth odor
0.1010 0.0592 0.0393 0.0056 0.9963 0.9998 0.9278 1.0000 0.9337 0.9595
0.5886 0.4988 0.5046 0.5861 0.0125 0.0323 0.1484 0.0177 0.1208 0.1020
table, the variations of fractal dimension between each pair of conditions was in-significant. By referring to the results of effect size analysis in this table, we can see that fourth odor had the greatest effect on variations of fractal dimension of EEG signal. Fig. 4 shows the variations of fractal dimension of GSR signal. Similar with Fig. 3(a), as can be seen in Fig. 4, by moving from rest to first, second, third and fourth stimulus, the fractal dimension of GSR signal increases. In fact, the increment in the molecular complexity of odors (Fig. 3(b)) causes the increment in the fractal dimension of GSR signal. Therefore, it can be said that presenting of an odor with greater molecular complexity causes greater complexity in subjects’ GSR signals. The result of ANOVA test shows the significant effect (p-value=0.0075, F-value=3.7947) of olfactory stimuli on fractal dimension of GSR signal. In comparison of fractal dimension of GSR signal with the fractal dimension of EEG signal, as it is clear in Fig. 4 and Fig. 3(a), the fractal dimension of GSR changes greater as we move from rest to fourth stimulus compared to the fractal dimension of EEG signal. Therefore, it can be said that olfactory stimuli had greater effect on GSR signal compared to EEG signal, or in other words, skin showed greater reaction compared to the brain in case of different olfactory stimuli. P-values for comparison of fractal dimension of GSR signal between different pairs of conditions are brought in Table 3. As can be seen in this table, the variations of fractal dimension between rest and third, and fourth odor was significant. However, there was not any significant difference in other comparisons. In addition, the results of effect size analysis in this table indicate that in general, fourth odor had the greatest effect on variations of fractal dimension of GSR signal. Therefore, the obtained results for fractal dimension of EEG and GSR signal in case of rest, first, second, third and fourth stimuli indicate that the variations of complexity of EEG and GSR signal are related to each other, as well as to the variations of complexity of odors as olfactory stimuli.
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4. Discussion In this paper, we investigated the relation between human brain and skin responses in case of rest and different olfactory stimulation. For this purpose, we computed the fractal dimension of EEG and GSR signals in different conditions. The applied olfactory stimuli were four odors that had increasing molecular complexities that enabled us to analyze the variations of complexity of EEG and GSR signals in case of variations of complexity of odors. The result of analysis showed that the fractal dimension of EEG signal increases when we move from rest to first, second, third and fourth odor. Similarly, the fractal dimension of GSR signal increases with this trend. Therefore, it can be said that EEG and GSR signals show greater variations in their complexities as we make greater variations in the molecular complexity of olfactory stimuli. In addition, statistical analysis showed that the effect of olfactory stimulation on complexity of GSR signals is significant. In general, it can be concluded that the variations of complexity of GSR signal are coupled with the variations of complexity of EEG signal, where by moving from rest to first, second, third and fourth stimulus, the greater variations in the complexity of EEG and GSR signal can be obtained. The coupling between brain and skin reactions that was investigated in this research is one step forward compared to the reported works [3, 5, 37-38] that only analysed the reaction of brain or skin in rest or in response the external stimuli without making any relation between them. The investigation in this research has been conducted in case of stimulation using different olfactory stimuli. In further research, we can extend our analysis to study the relation between brain and skin activities in case of other types of stimuli. For instance, we can apply auditory stimuli in the form of music to subjects and investigate how complexity of GSR signal changes with the complexity of EEG signal and even with the complexity of music. We can also do similar analysis in case of other physiological signals of human in order to investigate the relation between other related organs. For instance, we can do fractal analysis on ECG and EEG signals in order to find out the relation between human heart and brain responses in case of different stimuli. Since brain controls all parts of human body, we should be able to find the proper relation between them. It also should be noted that these analyses are not limited to healthy subjects. We can also extend our analysis in case of patients suffering from different brain or skin disorders and investigate how their brain and skin reactions are related. Mathematical modelling [48-49] is the ultimate goal in this area of research which enables us to mathematically formulate the relation between brain and skin activities in relation with applied stimuli. In this way, we should be able to predict the responses of brain and skin base on applied stimuli. In overall, all these efforts can help scientists for better understanding of the relation between brain and skin activities. Declaration of Competing Interest None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cmpb.2019.105293. References [1] I. Lieblich, S. Kugelmass, G. Ben-Shakhar, Efficiency of GSR detection of information as a function of stimulus set size, Psychophysiology 6 (5) (1970) 601– 608, doi:10.1111/j.1469-8986.1970.tb02249.x.
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