Original Research Article

Support vector machine classification of brain statesexposed to social stress test using EEG-based brainnetwork measures

Saeed Lotfan a, Shima Shahyad b, Reza Khosrowabadi c,Alireza Mohammadi b, Boshra Hatef b,*aDepartment of Mechanical Engineering, University of Tabriz, Tabriz, IranbNeuroscience Research Centre, Baqiyatallah University of Medical Sciences, Tehran, Iranc Institute for cognitive and brain sciences, Shahid Beheshti University G.C., Tehran, Iran

b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 1 9 9 – 2 1 3

a r t i c l e i n f o

Article history:

Received 18 May 2018

Received in revised form

13 September 2018

Accepted 31 October 2018

Available online 12 November 2018

Keywords:

Trier social stress test

Complex brain network

Synchronization likelihood

Support vector machine

a b s t r a c t

Stress is one of the most significant health problems in the 21st century, and should be dealt

with due to the costs of primary and secondary cares of stress-associated psychological and

psychiatric problems. In this study, the brain network states exposed to stress were

monitored based on electroencephalography (EEG) measures extracted by complex network

analysis. To this regard, 23 healthy male participants aged 18–28 were exposed to a stress

test. EEG data and salivary cortisol level were recorded for three different conditions

including before, right after, and 20 min after exposure to stress. Then, synchronization

likelihood (SL) was calculated for the set of EEG data to construct complex networks, which

are scale reduced datasets acquired from multi-channel signals. These networks with

weighted connectivity matrices were constructed based on original EEG data and also by

using four different waves of the recorded signals including d, u, a, and b. In addition to these

networks with weighted connectivity, networks with binary connectivity matrices were also

derived using threshold T. For each constructed network, four measures including transi-

tivity, modularity, characteristic path length, and global efficiency were calculated. To select

the sensitive optimal features from the set of the calculated measures, compensation

distance evaluation technique (CDET) was applied. Finally, multi-class support vector

machine (SVM) was trained in order to classify the brain network states. The results of

testing the SVM models showed that the features based on the original EEG, a and b waves

have got better performances in monitoring the brain network states.

© 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish

Academy of Sciences. Published by Elsevier B.V. All rights reserved.

* Corresponding author at: Neuroscience Research Centre, Baqiyatallah University of Medical Sciences, Tehran, IranE-mail address: boshrahatef@yahoo.com (B. Hatef).

Available online at www.sciencedirect.com

ScienceDirect

journal homepage: www.elsevier.com/locate/bbe

https://doi.org/10.1016/j.bbe.2018.10.0080208-5216/© 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by ElsevierB.V. All rights reserved.

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1. Introduction

World health organization (WHO) has stated that stress is oneof the most noteworthy health problems in the last decades. Infact, stress is considered as the leading threat to individuals astheir daily life demands cannot be satisfactorily handled, aswell as a risk to their health and social aspects of the life. Forinstance, stress has far-reaching consequences on our abilityto learn and remember, with major implications for educa-tional settings [1]. In addition to the harmful effect onindividuals, stress increases the economic costs of a country'sannual inland gross net product up to 4% [2,3]. This explainsthe necessity of investigations into monitoring psychologicalor physiological changes triggered by stress [3,4]. The termstress, introduced by Selye [5], is defined as ‘‘the non-specificresponse of the body to any demand for change’’. In general,when individuals are uncertain about the result of somethreats from external environment or within the body, stresssystem is activated [6].

Various response measures have been used to monitorstress levels and its produced changes. Two groups ofpsychological and physiological markers are introduced forstress monitoring; however, it has been confirmed that theyare not always correlated with each other [7]. Previous studieshave shown that immediately after stress, for an individualreporting the perceived stress, biomarkers such as the level ofcortisol hormone, catecholamine, and alpha amylase enzymeincreased [8–10]. Besides that, under stress, changes in heartrate (HR) [11], blood pressure (BP) [12], pupil diameter (PD) [13],breathing pattern [14], emotion [15], voice intonation [16] andbody pose [17] were also observed. Several minutes afterrecovery, in spite of self-reporting stress-free status, some ofthe biomarkers such as hormones and some features ofcardiovascular and brain activity were not restored to baseline[10]. The identification of the correlations between psycholog-ical and physiological markers in uncontrolled conditionssuch as high work exhaustion has been studied by Pärkkä et al.[18]. In another study, it has been shown that if the repetitionof stress exposure was higher than the required time torecovery, the allostatic load would be imposed to hemostasis,and chronic stress would be anticipated [19]. Accordingly, theuse of a validated protocol to correctly induce stress byexperimenter is of high importance. Investigations thatdiscuss these experimental paradigms to induce mental stresscan be found in the literature [20,21].

Generally, the brain generates a response to stress byactivating two main axes, hypothalamus–pituitary–adrenal(HPA) and sympathetic-adrenal medullary (SAM). Severalneurotransmitters or neuropeptides are released due to stress,affecting the regions of brain that have the specific receptor ofthem [22], which leads to the changes in the brain activity.Accordingly, since the electroencephalogram (EEG) signals arethe reflection of changes in the brain activity, they are used, inthis study, to monitor the stress levels and its fluctuations. TheEEG signals are widely used for monitoring the brain statesbecause these time series may reveal functional states ofneural networks in the brain [23–26]. Therefore, a large numberof studies have been done on monitoring the functional orphysiological states of the brain such as sleeping [24], resting

[27], depression [28], and Alzheimer's disease [29]. Al-shargieet al. [30] demonstrated the use of EEG signals to distinguishbetween low/average/high mental stress levels. They usedfrequency-domain features to cluster the mentioned threelevels. They found that EEG alpha band signals were highlycorrelated with mental stress states, and a significantreduction in alpha rhythm power from one stress level toanother level occurs. Subhani et al. [31] developed a machinelearning framework involving EEG signal analysis of stressedparticipants based on frequency-domain features. Anand andKumar [32] developed a method to detect low/average/highmental stress levels based on physiological parameters. Intheir approach, EEG-metric time-domain parameters, such asstandard deviation, were used to classify stress. Moreover,some limited EEG studies demonstrated that the power ofalpha band and ratio of Theta to beta band fluctuates in thepresence of stress [33,34]. Besides using EEG data, electrocar-diogram (ECG) and thoracic electrical bioimpedance (TEB)signals were used to extract frequency-domain features todistinguish among different types of activities that are neutral,emotional, mental and physical [35]. Sharma and Gedeon [3]discussed the measures, sensors and clustering techniques instress monitoring field in a review paper. They stated thatphysical and physiological measures and sensors have beenwidely used in clustering stress level. The measures have beenconsidered in isolation or in some basic combination.However, the use of signals to measure stress, requiresconsideration of aligning multi-source signals. In theirresearch, they highlighted the need for investigating physio-logical and physical signals by fusion of these signals, and theuse of techniques to find an optimal alignment in the featuresextracted. These needs arise from the fact that using EEGdataset to monitor the brain activity creates increasingly largedatasets of functional connection patterns [36]. Notably,efforts to characterize these datasets have led to thedevelopment of a multidisciplinary approach known ascomplex network analysis [37–39]. In this regard, Alonsoet al. [40] assessed the effect of stress on functional brainconnectivity, by using EEG-based data. They studied thefunctional brain connectivity of participants under the Strooptest and sleep deprivation, and found that alpha powerdecreases, high beta band increases, and the approximateentropy in the connectivity decreases under these conditions.

Due to the importance of stress, there are many researchesthat monitor stress levels; however, the persistence of stressafter recovery is not taken account in the clustering levels.Moreover, the fusion of signals and complex network analysisis not carried out to monitor stress levels based on optimalfeatures. Accordingly, the brain network states exposed tostress are monitored in the present study based on EEG signals.The use of complex network analysis blends all these signalsinto brain networks, and the united measures are used tomonitor stress levels. Besides, by using compensation distanceevaluation technique (CDET), the optimal features are derivedto cluster stress levels. Section 2 includes the recording of thenecessary data including EEG signals and salivary cortisol forthree different conditions of before, right after, and 20 minafter exposure to stress. In Section 3, synchronizationlikelihood (SL) parameters are calculated to construct weight-ed/binary complex brain networks. In Section 4, for each

Fig. 1 – The mean and 95% confidence intervals of VAS scorein three conditions of the test. **p < 0.00001.

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constructed network, four measures including transitivity,modularity, characteristic path length, and global efficiencyare calculated. Then, in order to select the sensitive featuresfrom the set of calculated measures, CDET is applied in Section5. Multi-class support vector machine (SVM) is trained inSection 6 for the classification of the brain network states.Finally, numerical results and discussion on the classificationaccuracy are given in Section 7, which is followedby conclusions in Section 8.

2. Data acquisition

Due to the complex nature of monitoring brain networkstates, implementing straightforward mathematical modelsare usually avoided and experimental data are analyzed tomake the status diagnosis [41]. In this study, the status of thebrain activities of volunteers before, right after and 20 minafter being exposed to a social stress test are monitoredbased on complex network measures of brain connectivity.To this end, twenty-three healthy male participants (18–28years old) were included in collecting necessary data. Theinclusion criteria for participants included having generalphysical and mental health, no abnormal sleep pattern, nosmoking habit, no regular exercise, no surgery in the spineand cortex, and no regular neuropsychological medicationusage. In this regard, prior to testing, they underwent a briefmedical examination to check their healthiness. Moreover,participants signed the ethical consent approved by Baqiya-tallah University of Medical Science. Trier Social Stress Test(TSST) was administered to the participants in order tocollect the essential database including EEG recordings,emotional self-reporting about the perceived stress by visualanalogue scale (VAS) and salivary cortisol levels, andinvestigate the changes in these recordings due to stress.In the following, the details of TSST, measuring and result ofVAS, as well as salivary cortisol levels and EEG recordings arediscussed.

2.1. Trier social stress test

Here, we provide a detailed description of the applied TSSTprotocol. As introduced by Kirschbaum et al. [42], TSST is astandardized protocol for the generation of moderate psycho-social stress in laboratory settings, which consists of a briefpreparation period (3 min) followed by a test period in whichthe subject has to deliver a free speech (2 min) and performmental arithmetic (8 min) in front of an audience. Based onthis protocol, each participant in our study entered a roomwith two referees at the desk. The person standing expressedhis job description for 8 min. During his speech, referees,wearing a neutral face, only listened to him and warned him tocontinue when he stopped talking. After the first 2 min andgiving his speech, the participant was asked to count downfrom 1022 by 13, and at each wrong subtraction he was warnedto start counting down from the beginning. The perceivedstress and anxiety was measured by VAS based on horizontalline between 0 and 10 points. The VAS was recorded threetimes before and after TSST, and after recovery. The results areillustrated in Fig. 1.

2.2. Salivary cortisol level measuring

Following the activation of the HPA and SAM axes (see Ref.[43]), cortisol is released from the adrenal gland into thebloodstream and spreads throughout the body. Blood andsalivary cortisol upsurges have been introduced as a standardstress index and various studies have used this index toconfirm that their intervention, i.e. exposure to stress, waseffective [44,45]. It should be noted that, to confirm the resultsof TSST, the person was asked to eat nothing an hour beforethe test and get his mouth washed right before the test. Tomeasure the level of cortisol, the ELISA kit of IBL Company,made in Germany, was used and the procedures were donebased on Kit catalog. 0.5 ml of salivary sample was gathered inthree times, i.e. once for each condition, and freezed at �80 8C.The recorded salivary cortisol levels are reported in Fig. 2.Repeated measurement test and, in the following, pairwisecomparison using Bonferroni test showed that the cortisolsignificantly increased after stress ( p < 0.001) and afterrecovery ( p < 0.0001) in comparison with the pre-stresscondition. These values confirm the success of the performedTSST.

2.3. EEG recordings

As discussed above, the essential data was collected for threeconditions including before (C#1), right after (C#2), and 20 minafter (C#3) TSST for each participant. EEG signals wererecorded using experimental equipment setup consisting ofa Mistar system, EEG MCScap-26 hat, and 30 silver/silverchloride scalp electrodes based on the international 10–20system. The brain connectivity was rewired from fronto-temporal to tempro-parietal connections especially in thetaand beta bands immediately after and 20 min after stressrespectively [46]. The sampling frequency was set to 256 Hzand signals were recorded during 1-min eyes-opened and 1-min eyes-closed resting conditions while subjects sat on a

Fig. 2 – The mean and 95% confidence intervals of salivarycortisol level of three conditions in the test (before, rightafter, and 20 min after stress). *p < 0.001.

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reclining chair in a sound-attenuated room. Accordingly, therecorded EEG dataset as a function of time, t, can be describedas following:

EEGop ¼ fX1 op ðtÞ; X2 op ðtÞ; . . .; X30 op ðtÞgT; p ¼ 1; 2; . . .23 (1)

EEGcp ¼ fX1 cp ðtÞ; X2 cp ðtÞ; . . .; X30 cp ðtÞgT; p ¼ 1; 2; . . .23 (2)

in which EEGo p and EEGc p denote the EEG matrix of the pthperson in the eyes-opened and eyes-closed tests, respectively.Moreover, Xn o=cp ðtÞ shows the EEG signal collected from the nthelectrode in the pth person's dataset in the eyes-opened/closed condition.

Besides the original recorded EEG signals, four main waveswith different frequency ranges are also created using finiteimpulse response (FIR) filter (see Ref. [47]). These frequencybands from low to high are called delta (d), theta (u), alpha (a),and beta (b). Delta waves lie within the range of 0.5–4 Hz, whichare primarily associated with deep sleep and may be present inthe waking state. Theta waves lie within the range of 4–7.5 Hz.These waves appear as consciousness slips toward drowsi-ness. For alpha waves, the frequency lies within the range of 8–13 Hz, and have been thought to indicate a relaxed awarenesswithout any attention or concentration. It has been observedthat alpha waves are eliminated by opening the eyes, hearingunfamiliar sounds, anxiety, or mental concentration orattention. A beta wave is the electrical activity of the brainvarying within the range of 14–26 Hz (though in someliterature no upper bound is given). Beta wave is the usualwaking rhythm of the brain associated with active thinking,active attention, focus on the outside world, or solving

concrete problems, and is found in normal adults [48].Accordingly, the following dataset are also created to checkthe sensitivity of each brain rhythm to stress:

EEGdo=cp ¼ fXd

1 o=cp ðtÞ; Xd2 o=cp ðtÞ; . . .; Xd

30 o=cp ðtÞgT; p

¼ 1; 2; . . .; 23 (3)

EEGuo=cp ¼ fXu

1 o=cp ðtÞ; Xu2 o=cp ðtÞ; . . .; Xu

30 o=cp ðtÞgT; p

¼ 1; 2; . . .; 23 (4)

EEGao=cp ¼ fXa

1 o=cp ðtÞ; Xa2 o=cp ðtÞ; . . .; Xa

30 o=cp ðtÞgT; p

¼ 1; 2; . . .; 23 (5)

EEGbo=cp

¼ fXb1 o=cp

ðtÞ; Xb2 o=cp

ðtÞ; . . .; Xb30 o=cp

ðtÞgT; p

¼ 1; 2; . . .; 23 (6)

in which EEGko=cp denotes the EEG matrix of the pth person

based on k wave of the EEG signals for eyes-opened/closedconditions. Therefore, in addition to the original EEG record-ings as in Eqs. (1) and (2), the datasets above are also usedto construct the brain networks in Section 3.

3. Construction of brain network

The real-world complex system of the brain is modeled by anetwork which is defined by a collection of links betweenpairs of nodes. Nodes in the network correspond to brainregions, while links represent anatomical, functional, oreffective connections depending on the dataset [49,50]. Anymathematical relations such as classical correlations be-tween all pairs of EEG signals collected from scalp electrodes(nodes) can form the links in the network. Therefore, eachmatrix in the set of Eqs. (1)–(6) can be used to build brainnetworks with 30 nodes. To this end, connectivity matricesbetween all pair combinations of EEG signals were computedfor all subjects using the synchronization likelihood (SL)method [51].

SL index is the most popular method to estimateconnectivity matrix in neurophysiological data. This indexdepends on the detection of simultaneously happeningpatterns and provides a measure of coherence between twosignals. This measure is more sensitive than simply a linear-correlation because it does not assume linearity in the signal,and is sensitive to phase-shifted coherent frequency bands[52,53]. In order to calculate this index for the set of Eqs. (1)–(6),for each one of M number of signals Xn o=cp ðtÞ; Xn o=cp ðtÞ wasconstructed with time-delay embedding [54]:

Xn o=cp ðtÞ ¼ fXn o=cp ðtÞ; Xn o=cp ðt þ lÞ; . . .; Xn o=cp ðt þ ðm�1ÞlÞgT; n

¼ 1; 2; . . .; 30

(7)

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where l refers to the delay time and m to the embeddingdimension. The probability Pen; ti

for nth signal at time ti, inwhich the embedded vectors are closer to each other thana distance e, is defined as:

Pen; ti¼ 1

2ðw2�w1ÞXN

j¼1w1 < ji�jj < w2

Qðe�jXn o=cp ðtiÞ�Xn o=cp ðtjÞjÞ (8)

in which Q is Heaviside step function, w1 is the Theiler correc-tion, and w2 is a window that sharpens the time resolution ofthe synchronization measure and is chosen such thatw1 � w2 � N [55]. The number of channels with correspondingsignals, closer to each other than distance en,i, is also definedas:

Hi;j ¼XMn¼1

Qðen;i�jXn o=cp ðtiÞ�Xn o=cp ðtjÞjÞ (9)

where en,i is the critical distance satisfying Pen; in; ti

¼ pref such thatpref � 1:. Now, synchronization likelihood can be given by [51]:

SLn;i;j ¼Hi;j�1M�1

; jXn o=cp ðtiÞ�Xn o=cp ðtjÞj < en;i

0; Xn o=cp ðtiÞ�Xn o=cp ðtjÞ��� ���� en;i

8><>: (10)

Finally by averaging SLn;i;j over all j, and, then, for all i, SLn,which describes how strongly channel n is synchronized to allthe other M � 1 channels, can be obtained. To calculate SLvalues, all the parameters such as l, m, w1, w2 and pref were setas described in details by Montez et al. [56]. Based on thedescription above, 30 by 30 symmetrical matrices for eachparticipant were created. Each element aij in these matricesrepresents the SL between channels i and j. In other words, SLiand its synchronization to channel j was calculated. Accord-ingly, in this research the brain networks were constructedbased on the following SL connectivity matrices:

SLo=cp ! calculated based on EEGo=cp (11)

Fig. 3 – Illustrative weighted SL connectivity matrices of the 6thconditions including; (a) before TSST (C#1); (b) right after TSST (C

SLdo=cp ! calculated based on EEGdo=cp (12)

SLuo=cp ! calculated based on EEGuo=cp (13)

SLao=cp ! calculated based on EEGao=cp (14)

SLbo=cp ! calculated based on EEGbo=cp

(15)

In the above relations, SLko=cp is the SL connectivity matrixfor the pth person based on k wave of the EEG signals for eyes-opened/closed cases. The elements of the above SL matricestake on values between pref and unity. pref corresponds to thecase where all M time series are uncorrelated and unitycorresponds to the greatest synchronization of time series.Therefore, the connectivity matrices of the brain networkshave weighted links. In addition to these networks, binarygraphs were also created using threshold T. Weighted SLmatrices were converted to binary graphs via T values in therange 0.02 ≤ T ≤ 0.3 with increments of 0.005. Consequently, foreach SL matrices in Eqs. (11)–(15), 57 binary graphs were alsocreated as:

SLkT o=cp ! thresholded from SLko=cp ; 0:02�T�0:3 (16)

For each person in the eyes-opened/closed condition,based on the original wave or k wave, 58 connectivity matricesmodeling the brain network were created. The illustrativeweighted and binary connectivity matrices of the brainnetworks of the 6th participant in the eyes-opened test areshown in Figs. 3 and 4 respectively. The visual patterns inthese figures show that, due to the recovery after 20 min,patterns of C#1 and C#3 approximately resemble to eachother, and visual pattern of C#2 is different from the othercases due to the exposure to TSST. Although these visualpatterns are distinct, they do not give any quantitativeinformation to monitor and diagnose the status of brainactivity. Therefore, one needs to extract measures from the

participant in the eyes-opened test, ði:e: SLo6 Þ in three#2), (c) 20 min after TSST (C#3).

Fig. 4 – Illustrative binary SL connectivity matrices of the 6th participant with T = 0.15 for the case of eyes-opened test,ði:e: SLT o6 Þ in three conditions including: (a) before TSST (C#1); (b) right after TSST (C#2); (c) 20 min after TSST (C#3).

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constructed brain networks, which is discussed in the nextsection.

4. Brain network measures

An individual brain network measure may describe one orseveral aspects of global and local brain connectivity [36]. Inthis study, four measures including transitivity, modularity,characteristic path length, and global efficiency have beencalculated for each brain network and used as low dimension-al usable features to monitor the brain connectivity changesdue to the TSST exposure.

Transitivity and modularity are both measures of func-tional segregation. Measures of segregation define the abilityfor a particular processing that happens within the inter-connected groups of brain regions. These measures mostlyquantify the presence of such groups, known as clusters ormodules. However, characteristic path length and globalefficiency are measures of functional integration. Measuresof integration define the brain ability to rapidly combineparticular information from distributed brain regions. Mea-sures of integration characterize this concept by estimatingthe ease with which brain regions communicate and arecommonly based on the concept of a path [36]. These fourapplied measures are described in brief in the following.

4.1. Transitivity

Locally, the fraction of triangles around an individual node isknown as the clustering coefficient and is equivalent to thefraction of the node's neighbors that are also neighbors of eachother [57]. Hence, the mean clustering coefficient for thenetwork reflects, on average, the prevalence of clusteredconnectivity around individual nodes. The mean clusteringcoefficient is normalized individually for each node and may,therefore, be disproportionately influenced by nodes with alow degree. A classical variant of the clustering coefficient,known as the transitivity, is collectively normalized, andconsequently does not suffer from this problem [38].

The mathematical definition of calculating transitivity forbinary networks, TTrb, has been given in Refs. [58,59] in details.Moreover, the weighted networks, Trw, have been calculatedbased on the definition provided in Ref. [60]. Therefore, for anindividual network (e.g. for the case of eyes-closed test of thepth participant, based on a wave SL matrix), 58 transitivitymeasures were calculated as Trw & TrbTi

; ð i ¼ 1; 2; . . .; 57Þ:

4.2. Modularity

Modularity is the degree to which the brain network may bedivided into obviously outlined and non-overlapping groups[61]. The mathematical definitions of calculating modularityfor binary networks, QbTi

; and weighted ones, Qw, have beengiven in Ref. [62].

4.3. Characteristic path length

The average shortest path length between all pairs of nodes inthe network is known as the characteristic path length of thenetwork. For the binary/weighted brain network, characteris-tic path length LbTi

=Lw were calculated based on mathematicaldefinition described in Ref. [57].

4.4. Global efficiency

The average inverse shortest path length is a relative measureknown as the global efficiency. Mathematical definition usedto calculate this measure, EbTi

=Ew; can be found in Ref. [63].

5. Feature selection

In this study, weighted brain networks in the form SLko=cp wereconstructed based on synchronization likelihood, in which kcan be omitted for the case of original EEG signals, or stand forone of the bands d, u, a and b. Besides these weighted networks,57 binary networks were also created by T method as SLkT o=cp

:

Therefore, for pth participant, in each case of eyes-opened or-closed, 58 number of each measures (i.e. transitivity,

Fig. 5 – Distance evaluation criteria for all four brain network measures based on SLucp :

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modularity, characteristic path length, and global efficiency)were derived based on the original or k wave EEG data. In orderto find the optimal measures, which distinguish the stresslevels with higher accuracy, compensation distance evalua-tion technique (CDET) proposed by Lei et al. [64] were used.This method calculates a value aj for each measure, the biggervalues of aj mean that the associated feature parameter hasbetter quality to separate the cth condition. Regarding thesimilarity between the brain networks of conditions C#1 andC#3 (see Figs. 3 and 4), CDET was performed to find the featuresets with the highest difference in C#2 and C#1. Accordingly, byconsidering two different conditions (i.e. C#1, and C#2) for eachparticipant, the feature set of F for each type of measure wasconsidered as:

fFc;j; c ¼ 1; 2; j ¼ 1; 2; . . .; 58g (17)

where Fc,j denotes the jth feature under cth condition. Thesummarized process of calculating the distance evaluationcriteria, aj (j = 1, 2, . . ., 58), has been given in Ref. [64]. The biggervalues of aj can separate the cth condition (i.e. the separation of

Fig. 6 – The trained and observed patterns of brain networkbased on the original EEG data using transitivity andmodularity features.

the condition after exposure to TSST from before or 20 minafter exposure to TSST). So, the appropriate features may beselected when their distance evaluation criteria are large. Thecalculated values of 4 � 58 number of distance evaluationcriteria are illustrated in Fig. 5. These values were calculated forbrain network measures based on SLucp matrix. According to thisfigure, for transitivity, modularity, characteristic path length,and global efficiency, measures number 10, 50, 53, and 31 havegot the highest distance evaluation criterion, respectively.Therefore, for this case, sensitive feasible features vector, f, hasbeen defined as fTrbT9 ; QbT49

; LbT52 ; EbT30gT: For all measures of

the constructed brain networks, CDET was performed to derivethe sensitive feasible feature vectors; these selected featureswere used to monitor the condition of the brain activity beforeand after TSST.

6. Classification via SVM

After constructing brain networks based on experimentaldatabase and extracting the usable sensitive features, brainconnectivity status was diagnosed via a multi-class SVMalgorithm. SVM classifiers have been also used to characterizethe brain states in some other studies [65–70]. These classifiers

Fig. 7 – The trained and observed patterns of brain networkbased on the original EEG data using characteristic pathlength and global efficiency features.

Table 1 – Brain network status monitoring based on original EEG signals for the cases of eyes-opened and -closed tests.

Test Used features Condition The percentage of membership in

C#1 C#2 C#3

Eyes-opened test (i.e. SLop ) TrbT49 , QbT57C#1 25.0000 0 75.0000C#2 0 54.5455 45.4545C#3 11.1111 33.3333 55.5556

LbT34, EbT7

C#1 37.5000 25.0000 37.5000C#2 27.2727 63.6364 9.0909C#3 33.3333 44.4444 22.2223

TrbT49, QbT57

, LbT34, EbT7

C#1 63.7143 2.2307 34.0550C#2 10.1563 84.1362 5.7075C#3 3.1129 38.7164 58.1707

Eyes-closed test (i.e.SLcp ) TrbT43, QbT57

C#1 32.2122 0 67.7878C#2 0 61.3108 38.6892C#3 14.9871 33.5667 51.4462

LbT31 , EbT16C#1 36.5000 25.0000 38.5000C#2 26.5189 62.4415 11.0396C#3 51.3307 34.3367 14.3326

TrbT43 , QbT57, LbT31 , EbT16

C#1 82.3361 17.6639 0C#2 1.1258 83.8924 14.9818C#3 5.5535 31.5896 62.8569

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are binary and can only classify two conditions. Therefore,multi-classification based on one-against-all strategy wasapplied to separate 3 different conditions. In one-against-allapproach [71,72], the C-SVM models are constructed where C isthe number of conditions. For the ith SVM, the model wastrained by considering all the sample data for the ith conditionas positive and the remaining as negative [73].

In this study, the training data for 3 conditions was given byfðf 1; y1Þ; ðf 2; y2Þ; ðf 3; y3Þg; where f i 2 RJ refers to the set offeatures, and J refers to the number of selected features. Fortraining the ith SVM, one should consider yi ¼ þ1 and yj ¼�1; ð j 6¼ iÞ: The equation of separating hyperplane betweenclusters for the ith SVM has been given as [74]:

wTi ’ðf nÞ þ bi ¼ 0 (18)

where wi, w, and bi denote weight vector, nonlinear mappingfunction, and the bias parameter respectively. The best sepa-

Fig. 8 – The trained and observed patterns of brain networkbased on the EEG data within d range frequency usingtransitivity and modularity features.

rating hyperplane based on soft margin risk minimizationfinds wi and bi that minimizes the following equation:

min12kwik2 þ gi

Xn

zin

!(19)

subjected to

ynðwTi ’ðf nÞ þ biÞ � 1�zin (20)

zin � 0 (21)

in which zni denotes slack variable and gi refers to the penalty

parameter. It is computationally simpler to solve the dualquadratic programming problem [72]. To obtain the dual form

Fig. 9 – The trained and observed patterns of brain networkbased on the EEG data within d range frequency usingcharacteristic path length and global efficiency features.

Table 2 – Brain network status monitoring based on EEG data within d range frequency for the cases of eyes-opened and-closed tests.

Test Used features Condition The percentage of membership in

C#1 C#2 C#3

Eyes-opened test (i.e. SLdop ) TrbT49 , QbT14C#1 37.5000 25.0000 37.5000C#2 18.8306 62.3388 18.8306C#3 0 62.5000 37.5000

LbT9 , EbT17C#1 37.5000 0 62.5000C#2 0 45.4545 54.5455C#3 20.0000 0 80.0000

TrbT49, QbT14

, LbT9, EbT17

C#1 26.5000 20.0000 53.5000C#2 8.2425 48.2364 43.5211C#3 15.7894 31.8940 52.3166

Eyes-closed test (i.e. SLdcp ) TrbT49, QbT26

C#1 24.7165 32.0323 43.2512C#2 42.1267 45.5505 12.3228C#3 9.6134 58.8258 31.5608

Lw, EbT7C#1 37.5000 25.0000 37.5000C#2 0 42.2135 57.7865C#3 32.0000 12.5000 55.5000

TrbT49, QbT26

, Lw, EbT7C#1 44.2560 21.0259 34.7181C#2 14.5271 42.2561 43.2168C#3 14.2540 38.9106 46.8354

b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 1 9 9 – 2 1 3 207

of Eqs. (18)–(20), positive Lagrange multipliers hni and mn

i wereused, and the objective function was given as [75]:

12kwik2 þ g i

Xn

zin�Xn

hinðynðwTi ’ f n� �þ biÞ�1

þ zinÞ�Xn

minz

in (22)

The above equation should be minimized over wi, bi, and zni

and be maximized over Lagrange multipliers hni and mn

i.Looking for a stationary point of Eq. (22), the followingquadratic problem can be obtained:

min12hTi Gihi þ BThi

� �(23)

such that

Fig. 10 – The trained and observed patterns of brain networkbased on the EEG data within u range frequency usingtransitivity and modularity features.

Xn

hinyn ¼ 0 (24)

0�hin�g i (25)

The elements of matrices in Eq. (24) are:

hi : hin; n ¼ 1; 2; . . .N (26)

Gi : ginm ¼ yn’ðf nÞT’ðfmÞym; n; m ¼ 1; 2; . . .; N (27)

Fig. 11 – The trained and observed patterns of brain networkbased on the EEG data within u range frequency usingcharacteristic path length and global efficiency features.

Table 3 – Brain network status monitoring based on EEG data within u range frequency for the cases of eyes-openedand -closed tests.

Test Used features Condition The percentage of membership in

C#1 C#2 C#3

Eyes-opened test (i.e. SLuop ) TrbT56 , QbT13C#1 7.1429 0 92.8571C#2 4.5455 59.0909 36.3636C#3 14.2857 28.5714 57.1429

LbT7 , EbT26C#1 50.0000 0 50.0000C#2 9.0909 54.5455 36.3636C#3 0 57.1429 42.8571

TrbT56, QbT13

, LbT7 , EbT26C#1 27.5000 0 72.5000C#2 14.2163 49.2318 36.5519C#3 12.2635 34.6512 53.0853

Eyes-closed test (i.e. SLucp ) TrbT9, QbT49

C#1 8.1502 16.8971 74.9527C#2 36.9104 49.8761 13.2135C#3 10.1256 54.6231 35.2513

LbT52, EbT30

C#1 37.5000 25.0000 37.5000C#2 0 50.0000 50.0000C#3 34.2164 35.3233 30.4603

TrbT9, QbT49

, LbT52, EbT30

C#1 46.1208 0 53.8792C#2 12.0571 48.6103 39.3326C#3 10.3167 26.1509 63.5324

b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 1 9 9 – 2 1 3208

B : bn ¼ �1; n ¼ 1; 2; . . .; N (28)

By solving the quadratic optimization problem one canestablish the separating hyperplane and by means of thefunction sign(wi

Tw(fn) + bi), the belonging of fn to the ith classcan be recognized. In this study, radius basis function (RBF) hasbeen considered as a kernel function K with the followingproperty:

Kðf n; fmÞ ¼ ’ðf nÞT’ðfmÞ (29)

60% of all samples were chosen randomly and used fortraining the SVM and the remaining 40% were used for testingthe methodology.

Fig. 12 – The trained and observed patterns of brain networkbased on the EEG data within a range frequency usingtransitivity and modularity features.

7. Results and discussion

In this section, the results of brain connectivity monitoringusing the extracted features and support vector machines arediscussed. First, monitoring based on network constructed byoriginal EEG data is investigated. The brain network wasconstructed based on the original EEG data (i.e. EEGo=cp withSLo=cp connectivity matrix). CDET was performed for themeasures of this test, and sensitive feasible features vectorsfor eyes-opened and -closed cases were selected asfTrbT49 ; QbT57

; LbT34; EbT7

gT and fTrbT43 ; QbT57; LbT31

; EbT16gT; re-

spectively. In practice, we used all four features, and createdseparating hyperplanes by means of 3-SVM models. However,for visualization, the trained and observed patterns are shown

Fig. 13 – The trained and observed patterns of brain networkbased on the EEG data within a range frequency usingcharacteristic path length and global efficiency features.

Table 4 – Brain network status monitoring based on EEG data within a range frequency for the cases of eyes-opened and-closed tests.

Test Used features Condition The percentage of membership in

C#1 C#2 C#3

Eyes-opened test (i.e. SLaop ) TrbT47 , QbT44C#1 50.0000 0 50.0000C#2 9.0909 72.7273 18.1818C#3 18.7500 43.7500 37.5000

LbT9 , EbT16C#1 50.0000 33.3333 16.6667C#2 18.1818 54.5455 27.2727C#3 16.6667 33.3333 50.0000

TrbT47 , QbT44, LbT9

, EbT16C#1 65.2231 17.6631 17.1138C#2 12.2136 74.3216 13.4648C#3 13.6508 28.2618 58.0874

Eyes-closed test (i.e. SLacp ) TrbT42, QbT44

C#1 64.2130 0 35.7870C#2 8.9751 83.5612 7.4637C#3 35.2131 12.4704 52.3165

LbT7 , EbT18C#1 50.0000 10.0000 40.0000C#2 14.2017 72.3215 13.4768C#3 16.6667 33.3333 50.0000

TrbT42, QbT44

, LbT7 , EbT18C#1 62.2131 0 37.7869C#2 0 91.2108 8.7892C#3 50.0000 0 50.0000

b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 1 9 9 – 2 1 3 209

for pair of features in the following. In Figs. 6 and 7, visualtrained and observed patterns of brain network in eyes-opened test are depicted for all 23 participants. In these figures,C#1, C#2, and C#3 correspond to the brain connectivity before,right after, and 20 min after exposure to TSST, respectively.Each circle is the coordinates of the selected features of eachparticipant based on observed data, and trained zones are theresults of 3-SVM clustering. According to these figures, C#2zone is almost separated from C#1 and C#3, however,conditions before and 20 min after TSST are not completelyseparated from each other and have got overlapping zones.This observation is in agreement with the results shown inFigs. 3 and 4, i.e. the resemblance of C#1 and C#3.

In previous investigations on monitoring stress levels,frequency-/time-domain features have been used. Most ofthese studies, demonstrated their results in quantitativeforms. Al-shargie et al. [30] used EEG signals to distinguishbetween low/average/high mental stress levels. Their results

Fig. 14 – The trained and observed patterns of brain networkbased on the EEG data within b range frequency usingtransitivity and modularity features.

showed potential in classifying stress levels with an averageaccuracy of 94.79%. In a similar study, Subhani et al. [31]developed a machine learning framework involving EEG signalanalysis and showed that the proposed framework produced83.4% accuracy for multiple level identification. Anand andKumar [32] showed the maximum accuracy of 73.1% indetecting stress levels based on EEG-metric time-domainparameters, such as standard deviation. Accordingly, in orderto test the trained 3-SVM models quantitatively, 40% ofsamples that had not been used in the training process wereused. The membership percentage of samples in each clusterzone was assessed by means of the function sign(wi

Tw(fn) + bi),and then the decision on the condition of the brain networkwas made. The results of testing the SVM models based onSLo=cp for eyes-opened and -closed are reported in Table 1. Inthis table, SVM clustering results based on pair of features andall four features are reported. Values written in red boldindicate wrong classification, hence, only in the case of using

Fig. 15 – The trained and observed patterns of brain networkbased on the EEG data within b range frequency usingcharacteristic path length and global efficiency features.

Table 5 – Brain network status monitoring based on EEG data within b range frequency for the cases of eyes-opened and-closed tests.

Test Used features Condition The percentage of membership in

C#1 C#2 C#3

Eyes-opened test (i.e. SLbop ) TrbT49 , QbT30C#1 85.7143 0 14.2857C#2 9.0909 72.7273 18.1818C#3 37.5000 50.0000 12.5000

LbT7 , EbT30C#1 33.3333 0 66.6667C#2 10.0000 80.0000 10.0000C#3 37.5000 50.0000 12.5000

TrbT49, QbT30

, LbT7 , EbT30C#1 60.3126 14.2131 25.4743C#2 0 92.3125 7.6875C#3 10.7301 25.0519 64.2180

Eyes-closed test (i.e. SLbcp ) TrbT45 , QbT41C#1 58.2131 0 41.7869C#2 5.6191 87.7878 6.5931C#3 33.0000 12.5000 54.5000

LbT2, EbT30

C#1 50.0000 0 50.0000C#2 10.1011 89.8989 0C#3 25.0000 25.0000 50.0000

TrbT45 , QbT41, LbT2

, EbT30C#1 60.0000 0 40.0000C#2 0 93.6248 6.3752C#3 50.0000 0 50.0000

b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 1 9 9 – 2 1 3210

two features, wrong detection of conditions C#1 and C#3occurs, which can be justified by the resemblance of C#1 andC#3 to each other.

Here, we proceeded with monitoring the brain activitybased on EEG data within d range frequency (i.e. EEGd

o=cpwith

SLdo=cp connectivity matrix). Implementing CDET resulted insensitive feasible features vectors for eyes-opened and -closedcases as fTrbT49 ; QbT14

; LbT9 ; EbT17gT and fTrbT49 ; QbT26

; LwEbT7gT;

respectively. In Figs. 8 and 9, visual trained and observedpatterns of brain network with eyes-opened test are depictedfor all 23 participants. Accordingly, conditions are notclassified as well as the results based on original EEG data,however, to compare the results quantitatively, 40% ofsamples that are not used in the training process are usedto test the results of 3-SVM model. These results for eyes-opened and -closed are reported in Table 2. In this table, valueswritten in red bold form indicate wrong classification.

For the case of network based on EEG data within u rangefrequency (i.e. EEGu

o=cpwith SLuo=cp connectivity matrix), CDET

resulted in the sensitive feasible features vectors for eyes-opened and -closed cases as fTrbT56 ; QbT13

LbT7 ; EbT26gT and

fTrbT9 ; QbT49; LbT52

; EbT30gT; respectively. In Figs. 10 and 11, visual

trained and observed patterns of brain network with eyes-opened test are depicted. According to these figures, condi-tions are not separated very well. Besides that, testing the 3-SVM model quantitatively, as reported in Table 3, shows thelow ability of the 3-SVM models to classify the conditions.

For the case of network based on the EEG signals within a

range frequency (i.e. EEGao=cp

with SLao=cp connectivity matrix),CDET resulted in the sensitive feasible features vectors foreyes-opened and -closed cases as fTrbT47 ; QbT44

; LbT9 ; EbT16gT and

fTrbT42 ; QbT44; LbT7 ; EbT18

gT; respectively. In Figs. 12 and 13, visualtrained and observed patterns of brain network with eyes-opened test based on a wave networks are depicted.Accordingly, C#2 zone is well separated from C#1 and C#3,however, conditions before and 20 min after TSST are notcompletely separated from each other. This observation is in

agreement with results shown in Figs. 6 and 7. The results oftesting the trained 3-SVM models based on 40% of the samplesfor the cases of eyes-opened and -closed are reported inTable 4. According to this table, features based on a wavenetwork have got a better classification performance.

Finally, the results of monitoring brain network statesbased on the EEG data within b range frequency (i.e. EEGb

o=cpwith SLbo=cp connectivity matrix) are discussed. ImplementingCDET resulted in the sensitive feasible features vectors foreyes-opened and -closed cases as fTrbT49 ; QbT30

; LbT7 ; EbT30gT and

fTrbT45 ; QbT41; LbT2

; EbT30gT; respectively. In Figs. 14 and 15, visual

trained and observed patterns of brain network with eyes-opened test are depicted. According to these figures, condi-tions are separated and testing the 3-SVM model quantita-tively, as reported in Table 5, shows the ability of the 3-SVMmodels trained by features based on b wave network to classifythe conditions with higher accuracy.

8. Conclusions

In this study, brain network states exposed to trier social stresswere monitored using support vector machine. The brainnetworks with weighted/binary connectivity matrices wereconstructed by means of synchronization likelihood. The useof brain networks based on synchronization likelihood wereapplied for the first time to assess the ability of monitoringstress based on scale reduced data, extracted from multi-channel EEG signals. This approach blends all 30 signals intoone unique network. The advantage of this network is thatbased on scale reduced measures (e.g. transitivity), one can getinformation about the state of the brain. The features used totrain and test the SVM models were selected from the networkmeasures by implementing compensation distance evaluationtechnique. For each brain network, visual connectivity matri-ces showed that two conditions including before and 20 minafter exposure to TSST are alike. This means that, the

b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 1 9 9 – 2 1 3 211

persistence of stress after 20 min vanishes and the brainnetwork resembles to the case before stress. This fact could bealso observed in all of the SVM models' visual patterns, in away that C#1 and C#2 are not separated completely. Applyingcompensation distance evaluation technique on all of thecalculated measures resulted in the features with the bestsensitivity to stress exposure. These features were scalereduced data that accurately distinguished between eachstress levels. The results showed that almost all of thenetworks with binary connectivity matrices have got betterfeatures to monitor the brain states. This might be because ofthe fact that in the weighted brain networks, the connectivitydata in each node was not as specific as in the binary ones (i.e.0 or 1). Visual connectivity matrices of the weighted networks,also demonstrated that the changes in values of nodes werehard to diagnose, in other words, making comparison betweenvisual connectivity matrices of different stress levels in thecase of binary matrices was easier.

SVM classifiers trained by the original EEG signals brainnetworks, and using all four features detected the exposure toTSST, (i.e. C#2) by accuracies of 84.14% and 83.90% for eyes-opened and -closed cases respectively. However, SVM classi-fiers trained by the EEG signals within a and b rangefrequencies had got much better performances. For the caseof classifiers trained by a wave EEG signals brain networks andusing all four features, detected the exposure to TSST, (i.e. C#2)by accuracies of 74.32% and 91.21% for eyes-opened and-closed cases, respectively. Based on this observation one canconclude that a wave signals sensitivity to anxiety andconcentration in the case of eyes-closed is higher than theeyes-opened one. This might arise from the fact that a wavesignals eliminate by opening the eyes. However, SVMclassifiers trained by b wave EEG signals brain networks andusing all four features, detected the exposure to TSST, (i.e. C#2)by accuracies of 92.31% and 93.62% for eyes-opened and-closed cases, respectively.

The overall results showed that the proposed method canmonitor the stress levels by appropriate accuracy and giveinformation about the contribution of each wave (d, u, a, and b)in monitoring the stress levels. Finally one can conclude thatthe accuracy of classification based on original EEG datamainly arises from a and b wave bandwidths of the originalsignals.

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