Comparison of different functional connectives based on EEG during concealed information test

Biomedical Signal Processing and Control 49 (2019) 149–159

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Biomedical Signal Processing and Control journal homepage: www.elsevier.com/locate/bspc

Comparison of different functional connectives based on EEG during concealed information test Wenwen Chang, Hong Wang ∗ , Chengcheng Hua, Qiaoxiu Wang, Yue Yuan School of Mechanical Engineering and Automation, Northeastern University, 110819, Shenyang, Liaoning, China

a r t i c l e

i n f o

Article history: Received 30 April 2017 Received in revised form 27 October 2018 Accepted 6 December 2018 Keywords: EEG Concealed information test Synchronization Functional connectivity Brain network P300-MERMER

a b s t r a c t Deception is a complex cognitive process in which liars always want to conceal some information. Concealed information test (CIT) is a useful paradigm which widely used in deception detection. Recent evidences from brain research suggest deception involves various cognitive activities, and most electroencephalograph (EEG) based concealed information test basically focuses on the signals from few electrodes and analyzed separately. In order to investigate the functional connectivity in different brain regions and the features from spatial domain, we applied graph theoretical concept to evaluate the changes of functional brain networks in guilty group compared with innocent in this article. Five different connectivity methods, including linear and nonlinear interdependence analysis, were explored to the multi-channel EEG signals, to explore which method is best for CIT. The result shows deception responses showed an increased connectivity level. Intraregional and interregional connectivity analysis also showed that deception was associated with increased activity in certain brain areas. Statistical analysis of network parameters showed these features form the two groups were significantly different, and mutual information was the best approach during which for network construction in CIT. Simultaneously, the deception response showed increased small-worldness. The results support the hypothesis that deception mainly involved in the process of working memory, which shows an enhanced connectivity and small-world properties. These findings reveal different dynamic networks in deception and truth telling state, and could be used to identify deception in individual subjects. © 2018 Elsevier Ltd. All rights reserved.

1. Introduction Human brain is considered to be a very complex system. Its intricate spatially interconnected structure and the neurophysiological process can reflect some cognitive activities [1]. Like other brain cognitive process, deception also needs mutual-activation between different brain areas and functional connectivity between these areas, which involves multi cognitive process [2–4]. The traditional approach for lie detection is the polygraph test [5–7], which based on the autonomic nervous system (ANS) measures, such as electrodermal activity, respiration, heart rate, and blood pressure. But in recent years, measures directly to central nervous system

∗ Corresponding author at: School of Mechanical Engineering and Automation, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang, 110004, China. E-mail addresses: [email protected] (W. Chang), [email protected] (H. Wang), [email protected] (C. Hua), [email protected] (Q. Wang), [email protected] (Y. Yuan). https://doi.org/10.1016/j.bspc.2018.12.008 1746-8094/© 2018 Elsevier Ltd. All rights reserved.

activity, such as functional magnetic resonance imaging (fMRI) and EEG have been introduced, and CIT is a widely used paradigm in deception study [2,8]. fMRI researches have shown that lie is characterized by specified cerebral activation and is distinguished from truth by increased prefrontal and parietal activity [2,8]. In contrast to fMRI, EEG based method measures event-related potentials (ERP) and have a better time resolution, which is widely studied in CIT and have achieved satisfying results [5–7]. However, these researches are limited to the discussion of few electrodes (Pz, Fz and Cz), and we are not sure whether it involves changes of functional network in the process of deception. The aim of our project is to design a BCI based lie detection system, since EEG signals are very sensitive to noise, to decode cognitive rhythm in deception from one channel or bipolar channels are not enough for achieve a good accuracy. Multi-channel based methods have been applied for EEG extraction to enhance the signal to noise ratio. Jiao et al proposed a novel sparse group representation model which can effectively reduce the required training samples from the target subject due to auxiliary data available from other subjects and thus can improve the efficiency of

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BCI system [9]. Wang et al utilized spatio-temporal feature extraction with multivariate linear regression to investigate steady-state visual evoked potentials (SSVEP) features, which can improve the real-time performance of SSVEP-BCIs [10]. Zhang et al applied temporally constrained sparse group spatial pattern to simultaneously optimize filer bands and time window within common spatial pattern, which can improve classification accuracy of motor imagery based BCI [11]. Zhang et al proposed a hybrid high order functional connectivity networks of the whole brain based on rs-fMRI time series to diagnosis cognitive impairment, which can achieve superior diagnosis [12]. It suggested functional connectivity is a new avenue for investment the cognitive mechanism of the brain. An effective method is to use graph theory to study the topological features of functional connectivity in the state of deception. Network analysis of functional connectivity based on graph theory provides models of complex cognitive activities in the brain, and allows us to better understand the relations between network structure and the processes taking place on those brain areas [13–15]. Network can be represented by a graph, which consisted by nodes (vertices) and connections (edges). Nodes represent the cortical areas corresponding to the EEG channels and connections denote the functional interaction between nodes, which can be calculated using different synchronization methods. The nodes connected by edges are called neighbors [15–17]. Connection structure of a graph is given by adjacency matrix, in which the value is the connections between two nodes. Various researches showed many networks in real life possess a so called small-world network features [17–20]. Small-world network appears as a graph in which most nodes are reached from each other by a few steps [19–21]. It is characterized by high amount of local clustering that signifying nodes are often connected to their neighbors and relatively short path length between any two nodes [13,22]. It has been reported that small-word networks have a balance for synchronizing neural activity between local specialization and global integration, and these networks show an optimal state with highly efficient information transmission and low wiring cost [20,23,24]. Network of human brain functional connectivity based on EEG, fMRI and MEG recording have exhibited small-world features [13,14,17,18]. Several empirical studies have shown small-world features could be found in task related conditions, such as finger tapping [18], working memory [25], music listening [22] and learning [3]. In this study, one goal is to address the question whether functional brain networks in deception state are characterized by small-world properties, and simultaneously discuss the difference between other network features. ERP based lie detection mainly based on “Oddball” protocol [5,7,27], in which P300 are elicited by rare, meaningful stimuli, followed the P300 is a late negative potential (LNP), and the two together have been termed P300-MERMER [28]. Stimuli that are meaningful to the individual are presented to the subjects infrequently in a series intermixed with irrelevant items, and they can elicit a P300-MERMER wave. The crime related items are thought to be meaningful for the guilty, but not for the innocent subject. Thus, if these items are presented to the subject, the crime-related items can elicit obvious P300-MERMER for guilty subject, but not for innocent subject, and the crime-unrelated items cannot elicit obvious P300-MERMER both for guilty and innocent subject [28–30]. The P300-MERMER based CIT use “3 stimulus protocols”, which present to subject on every trial: a rare probe (actually, in this study we used two type probes), a frequent irrelevant and a rare target. Probes are items with information related to guilty but not to innocent subject. Irrelevants are items unrelated to both guilty and innocent subject. Targets are irrelevant items which are designed to force the subject to pay attention to the test, for which subject are required to do some task, such as press a button, whenever they see the target items [6,7].

There are conventionally three methods for deception detection and signal analysis in P300-MERMER based CIT system. The first one is bootstrapped amplitude difference method, which compares the amplitude of P300 s between the three stimulus items [5,7]. The second one, we called bootstrapped cross-correlation method [5,6], which calculates the correlation between different items to identify the guilty. And the third one is based on wavelet features of the P300 signals combined with statistic classifier [5]. But all the three methods are based on the analysis of ERP signals from scalp areas at Pz, Fz and Cz. The study of fMRI-based CIT has shown deception can be distinguished from truth telling by increased activity in several brain regions [2,8,31], but the functional connectivity network role of deception has remained unclear. A researchable consideration is by studying the brain functional activity more directly, it might be possible to study the neuronal dynamics and synchronous oscillations of the special cortical areas [32,33]. Synchronization of certain types of neural activities relate to different cognitive and perceptual states and may be indicative to wider range of cognitive activities [34,35], such as deception. Assessment of synchronization between multivariate signals can give new insight into the function network of deception [33,35]. Human brain is a complex nonlinear system and the EEG- signal generated by brain is nonlinear and nonstationary [36,37], and the parameters of fluid viscosity, sex, and different brain regions significantly influence the index of non-stationarity value [38], it is much more appropriate to use nonlinear method for EEG analysis. However, the traditional but still the most common tools for estimate of the interdependence between neurophysiological data are cross-correlation in time domain and magnitude square coherence (MSC) or simply coherence in frequency domain [33,39]. These two approaches only measure the linear dependence between two signals and cannot identify the nonlinear synchrony. Phase synchronization is a nonlinear synchronizing method which focuses on the phase of signals [33,40,41]. Studies have shown that whether the amplitude of two signals is correlated, their phase may synchronize [33,41]. Another nonlinear synchronization tool is mutual information which based on information theory. It tries to find whether there is any common information between the signals as a sign of their interdependence [33,42]. Nonlinear interdependence measures have also been widely used in neurophysiological data, such as EEG data from Epileptic and Alzheimer patients [43,44], resting human subject [45] and some memory task studies [46]. Although the utility of nonlinear interdependence is in arguments with chaotic models [33], nonlinear interdependence measures are still used in EEG data. As these invariant quantities represent the signals in a state space, so such space signal can reveal some nonlinear structures even if there is no sign of chaos [33,47]. When we built the network, the five synchronization measures were used as the connectivity methods to get the adjacent matrix. In the current study, our goal is to investigate whether the synchronization and structure of functional networks in deception state can be distinguished from truth telling by using multi-channel EEGs in a mock deception test. And several synchronization methods were compared for functional connectivity to find the best one for CIT. The experiment used facial photos as visual stimuli in P300MERMER based CIT in a lab analogue task. Then, we employed the aforementioned concepts to find out the capabilities of both linear and nonlinear measures to reveal the functional connectivity of deception versus truth telling, and built the graphs to calculate and evaluate the clustering coefficient, path length and other network parameters. After the comparison of statistical analysis of network parameters, we got the best approach for network construction in CIT. The combined application of both linear and nonlinear synchronization and graph measures to different EEG channels may deepen our understanding of brain cognitive activities of deception. As shown in Fig. 1 is a simple flowchart for this study.

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Fig. 1. Flowchart of this study. Present visual stimuli to subject and record the EEG signal simultaneously, after get the P300-MERMER signal, construct the functional connectivity under the five methods (COH/COR/H/MI/PS). Then get the corresponding regional network and binary network, calculate the network parameters separately and make a comparison, finally we know which method is the best one for concealed information test analysis.

2. Materials and methods 2.1. Experiment procedures 2.1.1. Participants and materials The present study involved 20 right-handed subjects (mean age 23.6years, SD = 5.7, seven females). All subjects were recruited from our university and were paid for their participation after the text. The subjects had normal or corrected vision and were free from any historical neurological diseases or psychoactive drugs. The study was approved by the Ethics Committee of our university. Each subject was informed of the experiment procedure and signed the consent to participate. Four subjects were removed in the analysis due to the excessive artifacts. Before the experiment, each subject was required to provide two photos: one was their tutor’s and the other was their sibling’s. All photos were scanned and converted to grayscale image with the same resolution, brightness and contrast, and all the sizes were united to 37.3 by 50.3 mm2 . All those photos were processed by the same software and steps [5,6,26,29]. As each test trial has eight stimulus items and each subject provided two photos, the 20 subjects were randomly divided into five groups to make each group has four subjects. For each participant in the group, stimuli were presented randomly and successively in a photo sequence which consisted of two bitmap photos (target and probe2) provided by themselves combined with another six photos (five irrelevants and probe1) provided by the other three participants in this group. For half the participants, the photo of their tutor served as target and sibling as probe2 (guilty group), and for the other half participants the condition was just opposite. This was similar to the situation for probe1 (innocent group) and irrelevant. The experiment block was designed as a typical oddball paradigm and the present times of target, probe1, probe2 and irrelevant item were 15(12.5%), 15, 15 and 75(62.5%) respectively, and each block repeated three times [5,48]. Target and probe2 were photos of person who was familiar to the subject, and probe1 and irrelevant were unfamiliar one. Subjects were instructed to detect the target by pressing the button “YES” on the box placed next to their hand, and pressing “NO” for unfamiliar person. They were required to try to hide their information about probe2 by classifying it as unfamiliar one. Subjects were seated in a comfortable chair at a distance of 80 cm from the computer monitor, and visual stimuli were presented by a series of photos through STIM2 software. Each photo last about 1000 ms and the inter-stimulus interval was 1700 ms. Subjects were required to focus their gaze on the center of the monitor and pay attention to the stimuli presented.

tal electrooculograms (EOG) were recorded from electrodes placed laterally to both eyes as well as above and below the left eyes to monitor the eye movements and blinks. Inter-electrode impedance was below 5k. EEG was continuously recorded with a sampling rate at 1000 Hz, and with the bandpass filter at 0.01–100 Hz. Trials with overt incorrect responses or amplifier blocked were eliminated from the EEG data. The EOG artifacts were reduced by means of a regression approach implemented in Neuroscan software (Scan 4.5, NeuroScan, Inc., Herndon, VA, USA) [49]. For each subject, ERP epoch was starting at −100 ms prior to onset of stimuli and ending at 924 ms, and baseline corrected by using the pre-stimulus period. Any epoch had a voltage exceeding 80uv in any channels were excluded. The ERPs induced by different stimuli were derived by overlaying and averaging of the EEG signals corresponding to these stimuli for all the subjects. Then the averaged ERPs for each subject and grand averaged ERPs for all subjects were calculated to do the next connectivity analysis. 2.1.3. ERP extraction For every electrode and each ERP epoch from different stimuli, the P300 and LNP wave, which mainly consider is the slow wave range-delta frequency band [50,51], was obtained via wavelet packet decomposition and reconstruction (seven layer wavelet packet analysis). Wavelet packets analysis, based on the wavelet transform which represents both transient and stationary behaviors of an EEG signal, are generalization of the wavelet tree decomposition, capable of providing arbitrary frequency resolution, and can represent the time-frequency feature of the EEGs. In order to get the desired decomposition sub-tree, one orthogonal basis function was repeatedly pass the low and high pass filter, followed by decimation by two [5,52]. In this study, we used the wavelet packed analysis to decompose the ERPs into the four major rhythms (delta, theta, alpha, and beta-band), and the P300-MERMER frequency band was discussed in this study. The wavelet packet analysis and functional connectivity analysis were performed in MATALB, and these results were displayed in result section. 2.2. Functional network connectivity 2.2.1. Synchronization methods (1) Correction and Coherence: If we consider two simultaneously measured discrete time series xn and yn , n = 1,. . ., N. The cross-correlation function cxy is the most commonly used linear synchronization method [33,35], which is defined as, 1  cxy () = N− N−

2.1.2. EEG recording EEG was recorded with 32 electrodes arranged according to the international 10–20 system in the Neuroscan system, with a contralateral reference to left and right mastoid derivation. The mid-forehead electrode served as ground. Vertical and horizon-

i=1



xi − x x



yi+ − y y

 (1)

Where x¯ and x denote mean and variance, and  is the time lag. Magnitude squared coherence or simply coherence

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Table 1 Grand average value of connectivity for the five methods to different stimuli. Methods

Irrelevant Mean ± SD

Probe1 Mean ± SD

Probe2 Mean ± SD

Target Mean ± SD

Coha Cora Hb MIb PSa

0.5068 ± 0.0507 0.5156 ± 0.0657 1.5870 ± 0.1773 0.9262 ± 0.1081 0.6250 ± 0.0898

0.5048 ± 0.0570 0.5189 ± 0.0546 1.5244 ± 0.1056 0.9180 ± 0.1109 0.6279 ± 0.0651

0.6129 ± 0.0418 0.6049 ± 0.0565 1.7455 ± 0.1815 1.2685 ± 0.1754 0.6316 ± 0.0875

0.7123 ± 0.0472 0.7452 ± 0.0612 2.2791 ± 0.2034 1.6762 ± 0.1493 0.7042 ± 0.0720

*For each method, letter superscripts indicate significant differences (t−test, p < 0.05) between probe2 (deception condition) and probe1 (truth telling condition). Letter “a” means significant level p < 0.05, and “b” means p < 0.005.

is the cross-spectral density function Cxy (␻), which is calculated as the Fourier transform of the cross correlation. Then the coherence function is defined as the normalization of the cross spectrum, xy (ω) =



   Cxy (ω)  

Cxx (ω)

Cyy (ω)



(2)

It should be noted that coherence is a frequencydependent linear measure, while cross-correlation is a time-lag-dependent measure. We identified peaks in coherence and cross-correlation for each pair of electrodes and take these peak values as the measurements of the two connectivity measures, which were represented in Table 1. (2) Nonlinear interdependence: If two time series measured from x and y, we can reconstruct delay vectors [33,53] xn =(xn , . . ., xn-(m- 1) ) and yn =(yn , . . ., yn-(m- 1) ), where n = 1,. . ., N, m is the embedding dimension, and  denotes the time delay. Let rn,j and sn,j (j = 1,. . .,k) denote the time indices of the k nearest neighbors of xn and yn , respectively. For each xn , the mean squared Euclidean distance to its k neighbors is defined as, (k)

Rn (X) =

1 k xn − xrn,j k j=1

2

(3)

and the Y-conditioned mean squared Euclidean distance is defined by replacing the nearest neighbors by the equal time partners of the closest neighbors of yn , (k)

Rn

 1 k

2 X Y = xn − xsn,j k

j=1

(4)

Thus, an interdependence measure can be defined [33,54] as,

 1 S (k) X Y = k

(k) N Rn (X) n=1 R(k) X Y n





(5)

This measure ranges between 0 and 1 because of the

(k) (k) Rn X Y ≥ Rn (X). Following the Refs Arnhold et al. (1999) and Quiroga et al. (2000), we can define another nonlinear interdependence measure as, H

(k)

 1 N X Y = log N

n=1

Rn (X) (k)

Rn



X Y

(6)

Where Rn (X) is the average distance of vector xn to all the other vectors. Previous studies with coupled chaotic systems [53,55] have shown that H is more robust against noise and easier to interpret than S, and it is sensitive to weak dependences. (3) Phase synchronization: Empirical studies have shown that the phase of two coupled nonlinear systems may synchronize even if their amplitudes

are not correlated. Given a real-valued signal x(t), the analytical signal is defined as, H

ix (t) Zx (t) = x(t) + ixH (t) = AH x (t)e

(7)

Where xH (t) is the Hilbert transform of x(t). Then similarly H for another signal y(t), we define AH y and y . If we let the synchronization between x(t) and y(t) is n:m, we define the (n, m) H (t) = nH (t) − phase difference of their analytic signal as xy x myH (t),where n and m are integers. Then the phase synchronization index is defined as,

 

Ps =  e

H (t) ixy

      H (t) 2 + sin H (t) 2 cos xy = xy t t

(8)

t

The Ps will be zero when the phases are not synchronized, and will be one when the phases are perfect synchronized [33,39,41]. (4) Mutual information: The synchronization methods aforementioned are based on similarities in the time and frequency domain, or similarities in a phase space. Then we introduce a method based on information theory. For a discrete random variable X with M possible outcomes Xi , i = 1,. . ., M. Each outcome has a probability pi . Then we can define the Shannon entropy from this set of probabilities as [41],

M

I (X) = −

i=1

pi ln pi

(9)

Generally, entropy is used as a measurement of the uncertainty of the outcomes [32,41]. Then we try to consider the interdependence between X and another discrete random variable Y, so the mutual information (MI) between them is calculated as, MI(X, Y ) =



pij log

pij pi pj

(10)

Where pij is the joint probability of X=Xi and Y=Yj , and if the two systems are independence, pij =pi pj , then the MI is 0. MI reflects the true joint probability distribution different from the independence distribution assumed between X and Y, and it can also be defined as MI(X,Y)=I(X)+I(Y)-I(X,Y). These connectivity methods we discussed above were used to all pair-wise EEG electrodes, and the mean level of connectivity values of cortical areas were calculated and compared. Moreover, we grouped the EEG electrodes into five areas (frontal, temporal, central, parietal, and occipital) for both hemispheres. Averaged interregional and intraregional connectivity values were obtained, and the difference significant level between probe1 and probe2 was tested. 2.2.2. Graph measures Graph is a basic representation of a network which was defined as a set of nodes and corresponding sets of edges. We constructed

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Fig. 2. Grand averaged ERPs at Pz channel as a function of stimulus type (target, probe1, probe2 and irrelevant). (A) The superimposed and grand averaged ERPs for all subjects. (B) The P300-MERMER corresponding to Fig. 2.A after the wavelet packet analysis.

weighted graph with 30 nodes and studied the differences in features of functional brain networks under the states of deception and truth telling. The adjacent matrix with connectivity value we discussed above for all pairs of EEG channels were employed as the weight of the graph [21]. Each adjacent matrix was converted into a binary graph by using a threshold T. In this article, we mainly estimated several features for binary graph. The degree (D) of a node [15,56], which is defined as the number of edges connecting its region to the rest of the network, is a sensitive measure of centrality. The density () of a network [57,58], which denotes network is sparse or dense, is naturally defined as the ratio of edge with the same node set over the cardinality of the entire graph. The assortativity coefficient (r) is the pearson correlation coefficient of degree between pairs of linked nodes. It is said that for a network, if the nodes that have many edges tend to be connected to other nodes also with many edges, then the network shows assortative mixing [59]. Following the study of Newman, it can be calculated as, r=

|E|−1

−1

|E|

(i,j) ∈ E

(i,j) ∈ E

Di Dj − [|E|−1

(i,j) ∈ E

−1

(Di2 + Dj2 )/2 − [|E|

(Di + Dj )/2]

(i,j) ∈ E

2

(Di + Dj )/2]

2

(11)

Where |E| is the number of edges and the Di is the degree of the node i in the network. Other two measures frequently used to characterize the local and global structure of binary graphs are clustering coefficient and characteristics path length. When we try to compute clustering coefficient (C) of a certain node [23,24], we first need to determine other nodes it connected directly, which are called “neighbors”. The C of a node is defined as the ratio of the number of existing edges and the maximum possible number of edges between neighbors of it. Then define the C of the graph as the average of all nodes’. In general, we define the average of the shortest length path that connects any two nodes of the graph as the characteristic path length (L). The L reflects the property of a graph whether its nodes are interconnected. But for disconnected pairs, there is a problem that the path is an infinite value, so we consider to use the method based on global efficiency [23,57], where L is calculated as the reciprocal of the average of the path’s reciprocals. Besides, small-worldness is a parameter to measure small-world network, which has the property with high C but low L [19,23,56], and the small-worldness (S) of the network can be calculated as, S=

C/CR L/LR

(12)

Where CR and LR is the clustering and path length of a corresponding random graph, it is noticed that the network shows small-worldness when S >1. 2.3. Statistical analysis For statistical analysis, it was performed by taking the average functional connectivity and network features per subject and computing statistics between deception (guilty group) condition and

truth telling (innocent group) condition by using t-test in SPSS program. All analyses were performed with significance level set at 0.05, and post-hoc multiple comparisons were Bonferroni corrected.

3. Results Wavelet packet analysis for grand averaged ERPs elicited by different stimuli was preformed with seven-octave decomposition and reconstruction. Fig. 2 shows the ERP and P300-MERMER, the results were as predicted, it can be seen that target elicited a large P300, instead of irrelevant. The probes (probe2) elicited a P300 when they were relevant to the subject’s “crime”, which was in deception condition. A very small P300 was elicited by probes (probe1) when the subject was “innocent”. We also found that target and probe2 have more negative LNP, and irrelevant and probe1 have less negative LNP. The topology network of the connectivity is shown in Fig. 3. It was the normalized weighted networks of the five approaches, among which the sketchy differences could be observed via visual scrutiny. The connectivity level of Coh (coherence) method was similar to Cor (correlation), and they basically had a higher normalized value than the level of H (H index) and MI (mutual information), and among the five methods, PS (phase synchronization) had the highest connectivity level. For different stimuli, it can be seen that to the five methods, the connectivity level of irrelevant was similar to the probe1, and target had the highest level and then probe2. We could also observe the difference in connectivity level between probe2 (deception) and probe1 (truth telling). Table 1 shows the mean connectivity values of all EEG electrode pairs for the five methods. There were significant differences between probe1 and probe2. The connectivity levels of probe2 were higher than to probe1 for all the five methods. The difference level between probe1 and probe2 for mutual information (p = 0.0005) was higher than coherence (p = 0.016), correlation (p = 0.027), H index (p = 0.0008) and phase synchronization (p = 0.023). In order to study the statistical difference in intraregional and interregional connectivity values between probe1 and probe2, we investigated the regional results of the level of connectivity difference as shown in Fig. 4 and it is an indicative result. The connectivity difference in frontal, parietal and central areas in the right hemisphere was more obvious than other areas for the five methods, and MI method had the most statistical significant regions. Statistical significance between different regions also could be seen in Fig. 4, which was denoted by solid lines between two regions. Coh method had a high significant level in central-frontal, central-parietal, central-occipital, temporal-occipital and temporal-parietal regions which were similar to Cor condition. High significant level was found in central-frontal, frontal-parietal and temporal-frontal regions for H method and in central-frontal, central-parietal, central-occipital, central-temporal and temporal-frontal regions for MI method. PS method had the least statistical significant

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Fig. 3. Grand averaged weight networks after normalization for four stimuli items with the five connectivity methods: Coherence (Coh), Correlation (Cor), H index (H), Mutual information (MI) and Phase synchronization (PS). Use the position of 30 channels according to the 10–20 electrode system. The weight of the connectivity for all wise-pairs of EEG electrodes is indicated with a jet scale, from blue (connectivity value = 0) to deep red ( = 1).

Fig. 4. Regional results for statistical differences (t-test) in grand mean connectivity value between probe1 and probe2. (A) Spatial regions of the brain, with letters in the square indicated above, (B) for Coh method, (C) Cor method (D) H method (E) MI method and (F) PS method. The brain region is divided into 10 areas, and both the right and left hemisphere contain frontal (F), temporal (T), central (C), parietal (P) and occipital (O) regions. The solid lines denote the significant differences in interregional connectivity, and the filled square denotes statistically significant in intraregional differences.

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Fig. 5. The ratio of mean cluster coefficient C and path length L for guilty group (probe2) and innocent group (probe1) as a function of threshold for the five methods, (A) for Coh, (B) Cor, (C) H, (D) MI and (E) PS. The bars indicate the standard error of mean and the triangles indicate in which situation the difference between the two groups is significant (t-tests, p < 0.05).

regions, which was mainly in temporal-occipital, parietal-occipital and frontal-occipital. The ratios C/L as a function of the threshold (T) were calculated as shown in Fig. 5. There were significant between-group differences indicated by triangles (t-tests, p < 0.05). The ratio C/L showed a decrease for increasing the threshold values, due to the fact that more and more connections were lost. The ratio was consistently higher in the guilty group for these methods except PS. We found that the difference between the ratio of guilty group and innocent group was the most significant condition when T = 0.54 (p = 0.007), 0.58 (p = 0.005), 0.28 (p = 0.001), 0.32 (p = 0.003), 0.70 (p = 0.009) for Coh, Cor, H, MI and PS respectively, so we use these threshold to achieve the binarization of weighted networks. After we got the threshold, as shown in Fig. 6, the weighted mean connectivity networks were converted to the binary networks. The graph for all groups showed a complex network, and the irrelevant and probe1 groups showed a similar network. Compared with the two groups, the graphs of probe2 and target had a larger number of edges in the whole cerebral cortex areas. The difference of networks between probe1 (truth telling) and probe2 (deception) could be observed via visual scrutiny. There was no obvious difference among these methods from the graphs. The mean values of the network parameters in truth telling (probe1) and deception (probe2) condition for the five methods are presented in Table 2. It shows that for the five methods, the mean value of C, S, D and  in probe2 group were higher in comparison with probe1. The increase of S value means small-world property under lie condition was increased compare with truth telling. L was just the opposite except PS which almost had the same value for the two groups. For r, MI method had the different situation from other methods which had a negative value. The ttest demonstrated that the parameters C, L, D and  had the high significance level between probe1 and probe2. And among the five methods, MI had the most parameters that showed significance difference, which were C (p = 0.0325), L (p = 0.0016), S (p = 0.0036), D (p = 0.0029),  (p = 0.0029) and r (p = 0.1959), and PS had the least, which were C (p = 0.0703), L (p = 0.0605), S (p = 0.0501), D (p = 0.0469),  (p = 0.0469) and r (p = 0.0277).

4. Discussion In this study, we utilized different connectivity methods and graph theoretical analysis to differentiate multivariate EEG data for deception when participants intentionally conceal the identity of individuals they have previously known, which was designed in a P300-MERMER based CIT. We found that the linear and nonlinear interdependent connectivity, which measured by coherence, correlation, H index, mutual information and phase synchronization, increased in deception condition compared with truth telling condition. Connectivity analysis results suggested that correlation and coherence had similar network features in guilty and innocent condition respectively, and MI method had the highest statistical significant level. Network analysis results showed that the difference of the parameters L, D, and  between guilty and innocent group were significant, and the small-worldness increased in deception condition. It means the brain network under deception condition was tend to have a “small-world property”, which indicates in the process of lying, there are multiple brain regions were activated and the connectivity between these areas was increased, and also the information exchange became more common in the process. These findings complemented the linear and nonlinear effects of functional networks during deception and truth telling processes. Corresponding to the previous studies on P300-based CIT [5–7], the P300 amplitude of probe2 stimuli in deception condition (Fig. 2a) are larger than those in truth telling condition (probe1), which indicates an electrodermal differentiation between irrelevant and meaningful items. Visual ERPs are low-frequency events which contain various components [60–62], and the most popular method for visualizing these signals is using the ensemble averages of many single trials and then filtering, but it is difficult to handle the situation with few trials [61]. Empirical research indicated the P300 response and LNP has a dominant delta response oscillation [33,62], and the wavelet packet analysis can decompose the signal onto the time-frequency plane, which can preciously capture and measure the time-dependent and frequency-related information in ERP signals [60]. Thus a seven-octave quadratic B-spline

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Fig. 6. Weighted mean connectivity networks of Fig. 3 converted to binary network using individual thresholds we discussed above to different stimuli for the five methods, T = 0.54 (Coh), 0.58 (Cor), 0.28 (H), 0.32 (MI) and 0.70 (PS). If the connectivity value between two electrodes is above the threshold, a line is drawn (an edge exists between the two vertices), otherwise no.

Table 2 Average of the parameters of networks in Fig. 6, only for probe1 and probe2.

C L S D  r

probe1 probe2 probe1 probe2 probe1 probe2 probe1 probe2 probe1 probe2 probe1 probe2

Coh

Cor

H

MI

PS

Mean ± SD

Mean ± SD

Mean ± SD

Mean ± SD

Mean ± SD

0.7664 ± 0.0876∗ 0.8227 ± 0.0384 1.4212 ± 0.1300∗ 1.2384 ± 0.0716 1.4312 ± 0.2813∗ 1.7449 ± 0.1776 13.4500 ± 3.1049∗ 17.5083 ± 2.3932 0.4638 ± 0.1071∗ 0.6037 ± 0.0825 0. 2832 ± 0. 1496 0. 2354 ± 0. 1593

0.9031 ± 0.0694∗ 0. 9424 ± 0. 0431 1.4781 ± 0.1668∗ 1.2931 ± 0.0770 1.6135 ± 0.2300∗ 1.9000 ± 0.1836 13.6250 ± 2.5568∗ 17.2750 ± 2.3906 0.5022 ± 0.0881∗ 0.6280 ± 0.0868 0.2960 ± 0.1347 0.2660 ± 0.1687

0. 9233 ± 0.0532 0. 9336 ± 0.0522 1. 6961 ± 0.2644∗ 1. 4446 ± 0.2362 1. 4217 ± 0.1938∗∗ 1. 6837 ± 0.1697 8.3833 ± 1.5088∗∗ 12.3500 ± 3.0503 0.2256 ± 0.0522∗∗ 0.4283 ± 0.1051 0.2636 ± 0.1470∗ 0.3721 ± 0.1188

0.9156 ± 0.0690∗ 0.9591 ± 0.0442 1.8365 ± 0.3476∗∗ 1.3400 ± 0.2266 1.3431 ± 0.2802∗∗ 1.8721 ± 0.2928 10.9063 ± 2.7579∗∗ 17.5042 ± 3.8726 0.4105 ± 0.0950∗∗ 0.6381 ± 0.1335 −0.0424 ± 0.1440 −0.0556 ± 0.1168

0.9263 ± 0.0663 0.9679 ± 0.0548 1.5734 ± 0.2656 1.5798 ± 0.2587 1.5551 ± 0.2096 1.6443 ± 0.3323 12.475 ± 1.6344∗ 13.987 ± 3.0053 0.4500 ± 0.0801∗ 0.5239 ± 0.0790 0.3137 ± 0.1548∗ 0.3916 ± 0.1738

*For each method, sign superscripts indicate significant differences (t−test, p < 0.05) between probe2 (deception) and probe1 (truth telling). Sign “∗” means significance level p < 0.05, and “∗∗” means p < 0.005.

wavelet packet decomposition was applied to the ERPs to get the frequency components in delta, theta, alpha, and beta ranges, and only the delta band was reconstructed to get the P300 and LNP signal, as shown in Fig. 2.b, the signals’ high frequency component were removed and they became smoother compared with Fig. 2.a. Due to the advantage of high time resolution, wide variety of EEG based methods appeared, such as bootstrapped amplitude dif-

ference method, bootstrapped cross-correlation method, wavelet features based method and the improved one based on these methods. These EEG based methods basically explore the amplitude, correlation, latency and wavelet features of P300 s, but all those methods focus on the signals from few channels. fMRI studies suggested that deception is related to specific brain areas and is likely to engage theory of mind, recall, response inhibition and working

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memory [2,8,63]. Empirical research about EEG based functional network analysis in patient, animal, and cognitive neural functional imaging indicated different cognitive activities show different functional connectivity and network features [3,14,18,21,25]. In this study, it shows the functional network between lie and truth telling in nonlinear synchronization method is more obvious than linear methods, indicating the nonlinear dynamic feature of the brain increased when people are lying. Previous researches have shown deception involves working memory, inhibitory control and tasking selection [31,63], and it can be conceptualized as a confluence of multiple cognitive process. Synchronization analysis showed the brain functional connection (Fig. 2) and the mean synchronization value (Table 1) were increased both in linear and nonlinear methods. These findings are consistent with previous research about complex cognitive process [64], that higher cognitive function requires cooperation of multiple cortical areas and the connectivity among these areas was found to increase. And the increased connectivity was more significant in deception condition compared with truth telling. The intraregional and interregional connectivity in deception condition showed an increase (Fig. 4), especially at C (central), F (frontal) and P (parietal) areas, which were related to working memory [8,31,63] and the statistical difference was significant (Fig. 4). For linear methods, the connectivity difference between deception and truth condition in right hemisphere and parietal areas were most significant, and for nonlinear methods, the most significant regions were frontal and right hemisphere. Our findings were consistent with previous studies indicating that areas of the frontal and parietal cortex are more active when an individual engages in deception than when responding truthfully [8,31]. This result offers evidence for the concept that deception is a complex cognitive progress [2,63]. Among those methods, the connectivity method MI is the most useful one for identifying deception from truth, mainly because it has more information entropy among multi-channels in the deception condition. In order to explore the topological features of brain functional networks, each weighted graph was converted into a binary or unweighted graph by setting a threshold and choosing the strongest connections. In this study, the ratio of C and L as a function of threshold was calculated (Fig. 5). Due to the higher threshold resulted in a lower C and a higher L [20,23], the ratio decreased with increasing T for the five methods. The threshold we chose was at the condition that the difference of the ratio was the most significant one, and thus the binary graph we got had the most important information. It is different to compare networks in neuroscience, because the topological properties of a given network are generally dependent on the structure of the graph. Besides the basic parameters C and L, the term degree, density and assortativity coefficient are often used in this context [65], because differences in those parameters are associated with differences in topology networks. Compared to the binary graph in truth condition, the network had an increased C, D,  and decreased L in deception condition for the five methods. It was very interesting that associative mixing (r > 0) decreased for the linear methods but increased for the nonlinear methods. But for MI method, they showed dis-associative mixing (r < 0) both in the two conditions. For the five methods, all the connectivity value were increased in deception condition, but there were some differences between network features, especially for r, it is noteworthy that this is because different connectivity methods present different interdependencies between multivariate data. Table 2 also shows that S was increased in deception condition and the statistical difference was significant. This study for the first time shows that functional networks in deception condition may be more small-work like than the truth telling condition, which means deception network facilitated connectivity between distant neu-

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rons and transfer of efficient information. This was consistent with the hypothesis that working memory appears to play an important role in deception, and studies have shown working memory network exhibited small-world properties [25]. The results suggest deception process requests more information transfer. Although the main purpose of this study is to compare the network features between deception and truth telling for different synchronization methods, the final goal of our project is to design a brain-computer interface (BCI) based deception system. As we know that P300 related EEG patterns have been applied to BCI, and the connectivity network based features can be a good index for BCI? In the study of Daly et al [66], they found the potential of interregional connectivity features can be a highly effective feature for BCI control. They think compared with other traditional features in the classification of a finger tap BCI, functional connectivity dynamics can provide additional information and improve BCI control accuracies [66]. Zhang et al studied the relationship between the resting-state networks and the steady-state visual evoked potential (SSVEP)-based BCI and found that the SSVEPs were negatively correlated with the mean functional connectivity and clustering coefficient, but positively with path length. The combination of SSVEP and the three network features can improve the classification accuracy and performance [67]. Wang et al designed a P300 connectivity based BCI system for deception detection and got good results [68]. Combining with the results of this article, we think that functional connectivity based method can also be used in the BCI system, which will be our work in the future.

5. Conclusion, limitations and future research In conclusion, our research applied the connectivity method and graph theoretical tool to provide evidence for identifying deception through the brain functional networks. Increased linear and nonlinear connectivity was observed in deception condition and the topological networks appeared to be changed, and showed enhanced small-world features. Moreover, statistical analysis showed that compared with other connectivity methods, mutual information methods can reveal more concealed information. These findings suggest that deception is a very complex cognitive process, and the functional connectivity analysis can deepen our understanding of the neural substrates underlying deception, and the functional brain network analysis can be used in the concealed information test system. In the present study, there are some limitations. Firstly, we performed our research with a limited number of subjects. The participants were students in college aged from 22 to 28. Other age groups were not involved in, and we are not clear about the influence of age to deception condition. Secondly, this study did not do any analysis of the specific countermeasures which was extensively investigated by some researchers [7]. Thirdly, due to the low spatial resolution of EEG, it was difficult to evaluate neural activities of different cortex areas deeply, which might affect the ability to localize the brain regions involved in deception [63]. Future researches of the functional networks and structure networks using other techniques, such as fMRI, would give a comprehensive analysis to the limitation. Based on the limitations of this paper and the development of neuroimaging technology, some possible issues for future research might be proposed. In most articles, EEG based CIT studies are based on the statistical analysis of group subjects. Actually, the test is usually done for single subject, so based on the group features to distinguish one subject from truth telling to lie is the first issue to be considered. In this article and other CIT studies, subjects were required to cooperate with the test and do not make any countermeasures. But in the real test, criminal may use some tips to resist

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the test. Thus to design a paradigm which is more robust to countermeasure, such as a new paradigm with result feedback or the visual, auditory and tactile mixed module, is the second issue. In this article, the features used for CIT is based on EEG signal, which reflects the activities of central nervous system, and the polygraph based lie detection is based on some emotion dependent activities from autonomic nervous system (ANS), so we think the combination of EEG and other physiological signal from ANS (heartbeat, respiration, skin conductance etc) could improve the performance of the system. In recent years visual reality (VR) is widely used in neuroscience study and VR can be used to restore crime scenes, which can induce the crime related information in the brain more easily. Thus the VR based CIT should be a good tool for lie detection in the future. In summary, the ultimate goal for all these potential researches is to develop a real-time, efficient intelligent system for deception detection. Funding This study was funded by National Natural Science Foundation of China (grant number: 51405073), the University Innovation Team of Liaoning Province (grant number: LT2014006). Conflict of interest The authors declare that they have no conflict of interest. Acknowledgements We gratefully acknowledge the financial support from National Natural Science Foundation of China (51,405,073), the University Innovation Team of Liaoning Province (LT2014006) and Dr. Chi Zhang’s help for the experiment, and also thanks for Madiha Awais’ time of language check. References [1] O. Sporns, C.J. Honey, Small worlds inside big brains, PNAS 103 (2006) 19219–19220. [2] M.J. Farah, J.B. Hutchinson, E.A. Phelps, et al., Functional MRI-based lie detection: scientific and societal challenges, Nat. Rev. Neurosci. 15 (2014) 123–131. [3] D.S. Bassett, N.F. Wymbs, M.A. Porter, et al., Dynamic reconfiguration of human bran networks during learning, PNAS 108 (2011) 7641–7646. [4] M.G. Kitzbichler, R.N. Henson, M.L. Smith, et al., Cognitive effort drives workspace configuration of human brain functional networks, J. Neurosci. 31 (2011) 8259–8270. [5] V. Abootalebi, M.A. Khalilzadeh, A comparison of methods for ERP assessment in a P300-based GKT, Int. J. Psychophysiol. 62 (2006) 309–320. [6] L.A. Farwell, E. Donchin, The truth will out: interrogative polygraphy (¨lie detection)¨ with event-related brain potentials, Psychophysiology 28 (1991) 531–547. [7] J.P. Rosenfeld, M. Soskins, G. Bosh, Simple, effective countermeasures to P300-based tests of detection of concealed information, Psychophysiology 41 (2004) 205–219. [8] D.D. Langleben, J.W. Loughead, W.B. Bilker, et al., Telling truth from lie in individual subjects with fast event-related fMRI, Hum. Brain Mapp. 26 (2005) 262–272. [9] Y. Jiao, Y. Zhang, X. Chen, E. Yin, J. Jin, X. Wang, A. Cichocki, Sparse group representation model for motor imagery EEG classification, IEEE J. Biomed. Health Inform. 99 (2018) 1. [10] H. Wang, Y. Zhang, N.R. Waytowich, D.J. Krusienski, G. Zhou, J. Jin, X. Wang, A. Cichocki, Discriminative feature extraction via multivariate linear regression for SSVEP-based BCI, Ieee Trans. Neural Syst. Rehabil. Eng. 24 (2016) 532–541. [11] Y. Zhang, C.S. Nam, G. Zhou, J. Jin, X. Wang, A. Cichocki, Temporally constrained sparse group spatial patterns for motor imagery BCI, IEEE Trans. Cybern. 99 (2018) 1–11. [12] Y. Zhang, H. Zhang, X. Chen, S. Lee, D. Shen, Hybrid high-order functional connectivity network using resting-state functional MRI for Mild cognitive impairment diagnosis, Sci. Rep. 7 (2017) 6530. [13] C.J. Stam, W. de Haan, A. Daffertshofer, et al., Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease, Brain 132 (2008) 213–224.

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