Nonlinear Dynamic Complexity and Sources of Resting ... - IEEE Xplore

IEEE TRANSACTIONS ON NANOBIOSCIENCE, VOL. 16, NO. 5, JULY 2017

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Nonlinear Dynamic Complexity and Sources of Resting-state EEG in Abstinent Heroin Addicts Qinglin Zhao, Hua Jiang, Bin Hu,∗ Yonghui Li, Ning Zhong, Mi Li, Wenhua Lin, and Quanying Liu∗

Abstract — It has been reported that chronic heroin intake induces both structural and functional changes in human brain; however, few studies have investigated the carryover adverse effects on brain after heroin withdrawal. In this paper, we examined the neurophysiological differences between the abstinent heroin addicts (AHAs) and healthy controls (HCs) using nonlinear dynamic analysis and source localization analysis in resting-state electroencephalogram (EEG) data; 5 min resting EEG data from 20 AHAs and twenty age-, education-, and gender-matched HCs were recorded using 64 electrodes. The results of nonlinear characteristics (e.g., the correlation dimension, Kolmogorov entropy, and Lempel-Ziv complexity) showed that the EEG signals in alpha band from AHAs were significantly more irregular. Moreover, the source localization results confirmed the neuronal activities in alpha band in AHAs were significantly weaker in parietal lobe (BA3 and BA7), frontal lobe (BA4 and BA6), and limbic lobe (BA24). Together, our analysis at both the sensor level and source level suggested the functional abnormalities in the brain during heroin abstinence, in particular for the neuronal oscillations in alpha band. Index Terms — Heroin addicts, resting EEG, correlation dimension, kolmogorov entropy, lempel-Ziv complexity, sLORETA. Manuscript received May 6, 2017; accepted May 12, 2017. Date of publication May 29, 2017; date of current version August 11, 2017. This work was supported in part by the National Basic Research Program of China (973 Program) under Grant 2014CB744600, in part by the Program of International S&T Cooperation of MOST under Grant 2013DFA11140, in part by the National Natural Science Foundation of China under Grant 61210010, Grant 61632014, and Grant 6160201), in part by the National key foundation for developing scientific instruments under Grant61627808), in part by the Program of Beijing Municipal Science & Technology Commission under Grant Z171100000117005, in part by the Beijing Natural Science Foundation under Grant 4164080, in part by the Beijing Outstanding Talent Training Foundation under Grant 2014000020124G039. Asterisk indicates corresponding author. Q. Zhao, H. Jiang, and W. Lin are with the School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China (e-mail: [email protected]; [email protected]; linwh14@ lzu.edu.cn). ∗ B. Hu is with the School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China (e-mail: [email protected]). Y. Li is with the Psychological Research Institute, Chinese Academy of Sciences, Beijing 100101, China (e-mail: [email protected]). N. Zhong is with the Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Japan (e-mail: [email protected]). M. Li is with the Department of Automation and Information Technology, Beijing University of Technology, Beijing 100022, China (e-mail: [email protected]). ∗Q. Liu is with the Laboratory of Neural Control of Movement, ETH Zürich, 8057 Zürich, Switzerland (e-mail: [email protected]). Digital Object Identifier 10.1109/TNB.2017.2705689

I. I NTRODUCTION

H

EROIN addiction is a chronic, relapsing disease in the brain. According to statistics, the number of registered drug addicts at the end of 2005 was 1.16 million, more than 75% of whom were heroin addicts [1]. Heroin addiction therefore has been a devastating and severe problem, not only for the addicted individuals and their family, but also for the whole society. It has been documented that heroin dependence produces significant and lasting changes in brain chemistry and function [2], [3]; however, the carry-over adverse effects on brain after heroin withdrawal are still unclear. This might be the key to understand the high incidence of relapse after withdrawal treatment. The electroencephalogram (EEG), as a non-invasive measure of electrophysiological signals with millisecond time resolution, has been a potential tool to investigate the brain functions and cognitive process in both healthy and diseased subjects, at rest [4] or during task [5]. EEG has been used to study the neural correlates of heroin dependence, in terms of amplitude and frequency of the neuronal oscillations [6]–[8]. For example, qualitative analysis of EEG signals has reported that more than 70% of the heroin abuse subjects show a relatively lower amplitude of alpha activity, an increased beta activity, and a large amount of low amplitude waves in central regions in the early abstinence period [8]. Most of the studies reported the qualitative characteristics of EEG signals during the first 3-6 months of abstinence [7], [8]; however, the lasting effects on brain functions of heroin dependence after a longer withdrawal period are unclear. Furthermore, the underlying neurophysiological mechanism of EEG suggests that EEG signals stem from a highly nonlinear system [9], [10]. Nonlinear dynamics and chaos theory has permitted to gain new insight into the normal and disturbed brain functions [11]–[13]. On the one hand, the nonlinearity in the brain is rooted even at the cellular level, since the dynamical behavior of individual neurons is governed by threshold and saturation phenomena. On the other hand, the assumption of a stochastic and stationary brain is not theoretically suitable for the fact that human brain is able to perform sophisticated cognitive tasks. Thus, nonlinear dynamical analysis techniques may be an optimal approach, compared with the traditional linear methods, to investigate the abnormal dynamics in EEG signals [14], [15]. Nonlinear features such as the correlation dimension (D2), Kolmogorov

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TABLE I D EMOGRAPHIC D ATA

entropy (K2), and Lempel-Ziv complexity (LZC) have been used to detect differences in psychological states and to investigate the dynamic brain mechanisms underlying the EEG [16]–[19]. In particular, non-linear dynamic complexity of the human EEG has been a potential tool to study the evoked emotions [20], sleep stages [9] or mental states and brain disease [21], [22]. For example, K2 has been reported to be sensitive to the increased cortical dynamics of EEG signals during affective induction, which is associated with emotion processes [16]. More generally, studies have indicated that the basic processes underlying the generation of novel ideas show a strong increase in the EEG’s complexity, reflecting higher degrees of freedom in the competitive interactions among cortical neuron assemblies [18]. However, the applications of non-linear dynamic complexity in the heroin dependence are still limited. In this study, we will investigate the nonlinear features of the resting-state alpha waves in abstinent heroin addicts (AHAs), compared to healthy controls (HCs) with the hypothesis that the irregular oscillations of the neurons in AHAs will cause higher nonlinear dynamics. Moreover, EEG source imaging, compared with other brain imaging techniques, e.g. functional magnetic resonance imaging (fMRI) and positron-emission tomography (PET), has a specific merit of high temporal resolution, which has been an important tool for visualizing the temporal dynamics of the human brain’s large-scale neuronal circuits [23]. The standardized low resolution tomography analysis (sLORETA) has permitted to estimate the neuronal activities from scalp EEG and to model three-dimensional distributions of EEG sources [24], which has been used to investigate the cortical dysfunctions in epilepsy [25], dementia [26] and schizophrenia [27]. In this study, we will apply sLORETA to resting EEG signals to investigate the frequency-dependent functional abnormality in AHAs. Based on the previous studies [28], we hypothesized that sLORETA should reveal changes in cerebral source activity in certain brain regions for specific frequency band, particularly prefrontal and temporo-parietal areas, in relation to the cognitive effects of heroin.

of experiment. The AHAs were recruited from an isolated compulsory detoxification center (Gansu Province, China) with the duration of daily heroin abuse ranging from 2.1 years to 10.3 years, the dose of heroin use from 0.01 to 2 g/day, and the abstinence length ranging from 4 to 16 months. All AHAs in this study had normal intelligence, normal ability of learning and awareness, no mental illness or brain damage, no long-term intake of other drugs, meeting the criteria of Diagnosis and Statistics of Mental Disorder 5th edition (DSM-V) for heroin dependence. All AHAs were emotionally stable and cooperative during the experiment. Besides, 20 age, education-, and gender-matched HCs were recruited from the local community with no history of alcohol or drug abuse. Initial group comparisons between AHAs and HCs for demographics, e.g. age and years of education, were performed using two-tailed t-test. Demographic data of AHAs and HCs are demonstrated in Table I. Written informed consent was obtained from each subject before the experiment.

A. Subjects

B. Electrophysiological Recording and Processing 5 minutes resting EEG signals for each subject with closed eyes were recorded using a 64-channel electrode cap (Brain Products, Gilching, Germany) with the International 10/10 System sites. The scalp impedance of each sensor was kept below 20 k. Data were recorded at a sampling rate of 250 Hz with FCz as reference and amplified with an analog band-pass filter of 0.5-100 Hz. The raw EEG signals were inspected visually for the artefacts, such as the body movements, eye blinks, eye movements, electromyogram and bad electrodes. 40-second continuous EEG data (10000 samples) with the minimal artifacts were carefully selected from the raw 300s EEG signals, subject by subject. A band-pass filter of 1-40 Hz and independent component analysis (ICA) were applied to further reduce the artefacts, and then linked-mastoid reference was applied. For the following analyses, on the one hand, the cleaned EEG data were filtered to alpha band (8-13 Hz) by a FIR band pass filter to extract the nonlinear dynamic characteristics. Fig. 1 shows examples of 4s alpha waves from one subject in the heroin addicts group. On the other hand, the 40s cleaned EEG signals were equally split to 5 segments for source localization.

40 participants (all males) were recruited in the study, including 20 abstinent heroin addicts (AHAs) and 20 healthy controls (HCs). None of the subjects was taking any psychotropic, neurological, or psychiatric medications at the time

C. Nonlinear Dynamic Features Extraction We extracted three nonlinear dynamic features from the alpha waves, including the correlation dimension (D2),

II. M ATERIAL A ND M ETHODS

ZHAO et al.: NONLINEAR DYNAMIC COMPLEXITY AND SOURCES OF RESTING-STATE EEG IN AHAs

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D. EEG Source Localization

Fig. 1. Alpha rhythms from an abstinent heroin addict subject.

Kolmogorov entropy (K2), Lempel-Ziv complexity (LZC). These features were calculated for each 4s data (1000 points) with a 2s sliding window, channel by channel. In the end, each feature was averaged across epochs to reduce the variability. The correlation dimension can be used to measure the dynamical characteristics of EEG signal. The Grassberger and Procaccia algorithm with delay-time embedding was used to calculate the D2 [29]. Firstly, the correlation integral function Cm (r ) of a time sequence should be estimated [30]. Then the correlation dimension is defined as: D2 = lim ∂log2 (Cm (r))/∂log2r r−→0

(1)

Kolmogorov entropy is an important quantity for the characterization of chaotic system, which is measured according to the correlation integral of a number of increasing embedding dimensions [31]. According to Grasberger and Procaccia [32], K2 is defined as: K 2 = lim

lim

r−→0 m−→∞

1 1n(Cm (r )/Cm+1 (r)) τ

(2)

The Lempel-Ziv complexity analysis [33] is based on a coarse-graining of the measurements. Before calculating the complexity measure ±, the signal has to be transformed into a finite symbol sequence. In this case, we used 0-1 sequence conversion, and then calculated the LZC complexity based on the approach in Abásolo et al. [34]. In order to obtain a complexity ± measurement independent to the length of sequence, ± has to be normalized. If the length of the sequence is n and the number of different symbols in the symbol set is two, it has been proved [33] that the upper bound of is given by: lim c(n) = b(n) =

n−→∞

n log2 n

(3)

And c(n) can be normalized by b(n): C(n) =

c(n) bn

(4)

The forward head model was built by the boundary element method (BEM), using a MNI152 template [35] and the standard electrode positions of the EEG cap. After the head model template was established, the brain activity in each brain voxel was estimated by sLORETA method using the sLORETA & eLORETA software package [36]. The resulting sLORETA images represented the electrical neural activity of each voxel in the neuroanatomic Montreal Neurological Institute (MNI) space as amplitude of the computed current source density (μA/mm2). The brain sources were restrained in the cortical grey matter, resulting in 6239 cortical grey matter voxels at 5 × 5 × 5 mm spatial resolution. To obtain the reliable estimates of resting-state cortical sources, five artifact-free EEG segments were used to for calculating the sLORETA intracranial spectral density with a resolution of 1 Hz, from 1 to 40 Hz. The sLORETA functional images of spectral density were computed for five frequency bands: delta (1.5-4 Hz), theta (4.5-7.5 Hz), alpha (8-13 Hz), beta (13.5-30 Hz) and gamma (30.5-38 Hz).

E. Statistical Analyses Statistical analyses of the three nonlinear features were conducted using the SPSS Version 21. To examine the origin of the dysfunction for heroin addicts at sensor level, each nonlinear feature was summed for four main brain regions across the following sites according to their anatomical positions: frontal (Fp1, Fp2, FPz, F1, F2, F3, F4, F5, F6, F7, F8, Fz, AF3, AF4, AF7, AF8), central (C1, C2, C3, C4, C5, C6, Cz, FC1, FC2, FC3, FC4, FC5, FC6, CP1, CP2, CP3, CP4, CP5, CP6, CPZ), temporal (T7, T8, TP7, TP8, TP9, TP10, FT7, FT8, FT9, FT10), and parietal-occipital regions (P1, P2, P3, P4, P5, P6, P7, P8, Pz, PO3, PO4, PO7, PO8, POZ, O1, O2, Oz), and then divided by the number of electrodes. This lobe-based EEG regional analysis is supported by previous EEG and MRI study [37]–[39], which has been shown that neuroanatomical measures of structural changes for the frontal, parietal, temporal, and occipital lobes are consistent with absolute EEG power calculated based on this regional division [37]. In line with previous study [1], one-way analysis of variance (abbreviated one-way ANOVA) tests was used to evaluate the statistical differences between the estimated D2, K2, LZC values from AHAs and HCs. Moreover, receiver operating characteristic (ROC) plots were used to evaluate the ability of the nonlinear analysis methods to discriminate AHAs from HCs [40]. A rough guide to classify the precision of a diagnostic test was related to the area under the ROC curve. The precision of the diagnostic test is considered to be excellent for value of the area under the ROC curve between 0.90 and 1, good between 0.80 and 0.90, fair between 0.70 and 0.79, poor between 0.60 and 0.69, and bad between 0.50 and 0.59. In addition, the difference in source localization of cortical oscillations between groups in each frequency band was assessed by voxel-by-voxel independent sample F-ratiotests, based on sLORETA log-transformed current density power. In the resulting statistical three-dimensional images,

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Fig. 2. Topographical distribution of correlation dimension (A), Kolmogorov entropy (B) and Lempel-Ziv complexity (C) for AHA and HC group, respectively. TABLE II O NE -WAY ANOVA T ESTS R ESULTS OF THE AVERAGE VALUES OF THE T HREE F EATURES FOR F OUR R EGIONS

Fig. 3. ROC curves to discriminate AHAs and HCs with correlation dimension (A), Kolmogorov entropy (B) and Lempel-Ziv complexity (C). The nonlinear features were calculated from the frontal region.

cortical voxels showing significant differences were identified by a nonparametric approach (statistical nonparametric mapping or SnPM) via randomizations with a total 5000 permutations. This randomization strategy determined the critical probability threshold values for the actually observed t-values with correction for multiple comparisons across all voxels and all frequencies [41]. The statistical significance for two-tailed t-test was set to p = 0.0048 with family-wise error (FWE) correction for multiple comparisons. III. R ESULT In this study, we examined brain functional abnormalities in abstinent heroin addicts (AHAs), using the nonlinear dynamic features of resting EEG signals in alpha band, and frequencydependent sLORETA source localizations. Fig. 2 showed the topographical distribution of correlation dimension, Kolmogorov entropy and Lempel-Ziv complexity for AHAs and HCs, respectively. Consistent in these three

nonlinear measurements, AHAs generally showed higher nonlinear dynamic characteristics compared with HCs, in particular at the frontal and central areas (Table II). Furthermore, we evaluated whether D2, K2 and LZC from the four regions can discriminate AHAs and HCs using ROC curve, in line with the approach in previous study [40]. Table III showed the values for the area under the ROC curve in four regions, respectively. Specifically, the results of D2, K2 and LZC at frontal and central regions are relatively good, whereas the results of Kolmogorov entropy at temporal and parietaloccipital are considered to be poor. Moreover, the ROC curves from the frontal regions for D2, K2 and LZC were illustrated in Fig. 3. In addition, sLORETA was used to map the brain activities underlying specific frequency bands (Fig. 4 upper and middle). The differences between AHAs and HCs were estimated using two-tailed t-test (Fig. 4 bottom). Importantly, the significant differences were only found in alpha band, but not in


TABLE III A REA OF THE F EATURE FOR F OUR R EGIONS U NDER ROC C URVE

Fig. 4. The estimated sLORETA brain sources localization maps for delta,theta, alpha, beta and gamma band, for AHA group (upper) and HC group (middle) and the comparison between AHAs and HCs (bottom) using sLORETA in five frequency bands. The left side of the maps (top view) corresponds to the left hemisphere. Notably, the significant differences (threshold p < 0.0048) between AHAs and HCs were only observed in alpha band. TABLE IV T HE R ESULTS OF THE sLORETA

other bands. Specifically, compared to HCs, the alpha power of AHAs was significantly weaker in Brodmann areas (BA) 3, 7, 4, 6, 18, 19, 24, involved in frontal, parietal, occipital and limbic lobes (Table IV). The peak MNI coordinates and the corresponding brain regions for the significant brain areas in alpha band were shown in Table IV. IV. D ISSCUSSION To examine the association of dysfunction in AHAs with the irregular neuronal oscillations in alpha band, we have compared the nonlinear dynamic characteristics of EEG alpha

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rhythms using the correlation dimension (D2), Kolmogorov entropy (K2) and Lempel-Ziv complexity (LZC). Specifically, D2 of the alpha waves from AHAs had significant higher values in AHAs which suggested that the complexity of the cortical dynamics underlying alpha waves in AHAs are generally stronger than in HCs, in particular in frontal and central regions. This is in good agreement with previous studies which have shown higher D2 of neuronal oscillations reflects the more complex brain activities [42]. The Kolmogorov entropy is an index for the chaotic degree of a dynamic system which reflects the rate of information loss with time in dynamic processes [16]. The higher K2 values exhibited in frontal and central regions of AHAs indicate an increase of chaotic degree in neuronal activities in these regions. Similarly, larger LZC values in AHAs reflects higher probability to generate new patterns of signals, in line with previous studies [34]. Together, the results of these three nonlinear dynamic features imply that chronic heroin abuse may enhance the complexity of electrophysiological signals in the brain, especially in the frontal and central regions. Moreover, D2, K2 and LZC, as considerable indicators to reveal the complexity of neuronal oscillations in the brain, can be used to differentiate the AHAs and HCs (Fig.3, Table II and III). The previous studies have revealed slower alpha oscillations in methadone patients [43], we therefore investigated the frequency-related changes in brain sources in AHAs. In line with previous study which has shown the changes of alpha waves are mainly focused in frontal and central region [44], we have found the reduced alpha activities in AHAs in parietal lobe, frontal lobe and limbic lobe, where are considered to be relate to cognitive inhibition, learning and memory, motor circuits and critical brain regions in rewarding circuits. Previous studies on nicotine addiction have showed that smokingrelated cues could trigger neuronal activity in motor cortex (BA4, 6) and somatosensory cortex (BA3, 7) [45]. Hester and Garavan [46] has found that the main cause of abnormal brain activity in cerebral cortex in drug addicts. Moreover, our finding is consistent with previous studies in cocaine abuse and alcoholism [47]. Some scholars have documented that the anterior cingulate gyrus (BA24) plays a crucial role in regulating the integration of information cognition and modulating visual spatial and sensorimotor functions [48]. Our evidences of dysfunction in these brain regions for AHAs may confirm the adaptive changes of the brain after the longterm addiction and the carry-over effects during withdrawal treatment, which may be a cause reason why the AHAs easily encounter the negative emotions and hardly prevent themselves from heroin relapse. One limit of our study was that the low size of subjects constrained our findings from having any convincing clinical meanings or applications. This can be rather considered as a preliminary study to investigate the carry-over effects during abstinent period of heroin addicts. Moreover, the accuracy for source reconstruction using 64-channel EEG data is relatively limited, especially using a template rather than a realistic head model for inverse solution. The better way to estimate the brain sources is to use high-density EEG which includes over

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100 electrodes, combining the individual structural image to build the realistic head model. V. C ONCLUSION In conclusion, our results of nonlinear dynamic features have showed the increased brain complexity underlying alpha rhythms in AHAs, and the source localization has revealed a reduced alpha power in specific brain regions. Together, these findings shed further light on how the brain dysfunction is implemented at resting AHA subjects, and may motivate future studies to explore the neuronal abnormalities in drug dependence, towards the evaluation of withdrawal treatment for drug addicts. R EFERENCES [1] Y.-L. Tang and W. Hao, “Improving drug addiction treatment in China,” Addiction, vol. 102, no. 7, pp. 1057–1063, 2007. [2] A. T. McLellan, D. C. Lewis, C. P. O’Brien, and H. D. Kleber, “Drug dependence, a chronic medical illness: Implications for treatment, insurance, and outcomes evaluation,” J. Amer. Med. Assoc., vol. 284, no. 13, pp. 1689–1695, 2000. [3] K. Yuan et al., “Gray matter deficits and resting-state abnormalities in abstinent heroin-dependent individuals,” Neurosci. Lett., vol. 482, no. 2, pp. 101–105, 2010. [4] J. B. Frøkjær et al., “Integrity of central nervous function in diabetes mellitus assessed by resting state EEG frequency analysis and source localization,” J. Diabetes Complications, vol. 31, no. 2, pp. 400–406, 2016. [5] X. Li, B. Hu, T. Xu, J. Shen, and M. Ratcliffe, “A study on EEG-based brain electrical source of mild depressed subjects,” Comput. Methods Programs Biomed., vol. 120, no. 3, pp. 135–141, 2015. [6] L. Costa and L. Bauer, “Quantitative electroencephalographic differences associated with alcohol, cocaine, heroin and dual-substance dependence,” Drug Alcohol Dependence, vol. 46, nos. 1–2, pp. 87–93, 1997. [7] E. Shufman et al., “Electro-encephalography spectral analysis of heroin addicts compared with abstainers and normal controls,” Israel J. Psychiatry Rel. Sci., vol. 33, no. 3, pp. 196–206, 1995. [8] A. B. Gekht, A. G. Polunina, E. A. Briun, and D. M. Davydov, “Brain bioelectrical activities in heroin addicts during early abstinence period,” Zhurnal Nevrologii i Psikhiatrii Imeni SS Korsakova/Ministerstvo Zdravookhraneniia i Meditsinskoi Promyshlennosti Rossiiskoi Federatsii, Vserossiiskoe Obshchestvo Nevrologov [i] Vserossiiskoe Obshchestvo Psikhiatrov, vol. 103, no. 5, pp. 53–59, 2002. [9] J. Fell, J. Röschke, K. Mann, and C. Schäffner, “Discrimination of sleep stages: A comparison between spectral and nonlinear EEG measures,” Electroencephalogr. Clin. Neurophysiol., vol. 98, no. 5, pp. 401–410, 1996. [10] Q. Zhao et al., “An EEG based nonlinearity analysis method for schizophrenia diagnosis,” Biomed. Eng., vol. 9, no. 1, p. 136, 2012. [11] C. J. Stam, “Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field,” Clin. Neurophysiol., vol. 116, no. 10, pp. 2266–2301, 2005. [12] T. Elbert, W. J. Ray, Z. J. Kowalik, J. E. Skinner, K. E. Graf, and N. Birbaumer, “Chaos and physiology: Deterministic chaos in excitable cell assemblies,” Physiol. Rev., vol. 74, no. 1, pp. 1–47, 1994. [13] H. Korn and P. Faure, “Is there chaos in the brain? II. Experimental evidence and related models,” Comp. Rendus Biol., vol. 326, no. 9, pp. 787–840, 2003. [14] H. Kantz and T. Schreiber, Nonlinear Time Series Analysis, vol. 7. Cambridge, U.K.: Cambridge Univ. Press, 2004. [15] X.-S. Zhang, R. J. Roy, and E. W. Jensen, “EEG complexity as a measure of depth of anesthesia for patients,” IEEE Trans. Biomed. Eng., vol. 48, no. 12, pp. 1424–1433, Dec. 2001. [16] L. I. Aftanas, N. V. Lotova, V. I. Koshkarov, V. L. Pokrovskaja, S. A. Popov, and V. P. Makhnev, “Non-linear analysis of emotion EEG: Calculation of Kolmogorov entropy and the principal Lyapunov exponent,” Neurosci. Lett., vol. 226, no. 1, pp. 13–16, 1997. [17] H. T. Schupp, W. Lutzenberger, N. Birbaumer, W. Miltner, and C. Braun, “Neurophysiological differences between perception and imagery,” Cogn. Brain Res., vol. 2, no. 2, pp. 77–86, 1994.

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Qinglin Zhao received the Ph.D. degree from Lanzhou University, China, in 2016. He is currently an Associate Professor with the School of Information Science and Engineering, Lanzhou University. His research interests include EEG signal processing, automatic control, and the application design of electronic circuits.

Hua Jiang is currently pursuing the master’s degree with the School of Information Science and Engineering, Lanzhou University, China. Her research interests include biological signal acquisition, biological electrodes, and source localization analysis.

Bin Hu received the Ph.D. degree in computer science from the Institute of Computing Technology, Chinese Academy of Science, China, in 1998. Since 2008, he has been a Professor and the Dean of the School of Information Science and Engineering, Lanzhou University, China. He had been also a guest professorship with ETH Zürich, Switzerland, until 2011. His research interests include pervasive computing, computational psychophysiology, and data modeling.

Yonghui Li is a researcher of Health and Genetics in Psychological Research Institute, Chinese Academy of Sciences. His research contents are the brain mechanism of motivation and reward, the cognitive neural mechanism of behavioral decision making in patients with behavioral addiction and drug addiction, and the neural mechanism of fear of posttraumatic stress disorder.

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Ning Zhong is currently the Head of the Knowledge Information Systems Laboratory and a Professor with the Department of Life Science and Informatics, Maebashi Institute of Technology, Japan. He is also the Director and an Adjunct Professor with the International WIC Institute, Beijing University of Technology. He has conducted research in knowledge discovery and data mining, rough sets and granular computing, Web intelligence, brain informatics, and knowledge information systems, with over 200 journal and conference publications and 20 books. He has served as the Advisory Committee Member of IJCAI’03. He received the IEEE TCII/ICDM Outstanding Service Award in 2004, the ACM’s Recognition of Service Award in 2001 and 2012, and the Pacific-Asia Conference on Knowledge Discovery and Data Mining Most Influential Paper Award from 1999 to 2008. He is the Co-Chair of Web Intelligence Consortium and the Chair of the IEEE-CIS Task Force on Brain Informatics. He has served as the Chair of the IEEE-CS Technical Committee on Intelligent Informatics, the Conference Chair of ICDM’02 and WI-IAT’03, the Program Chair of ICDM’0, Brain Informatics 2009, and WI-IAT’04. He is the Editor-in-Chief of the Brain Informatics journal (Springer), and the founding Editor-inChief of the Web Intelligence journal (IOS Press). He serves as an Associate Editor/Editorial Board for several international journals and book series. He has served as an Associate Editor of TKDE.

Mi Li received the Ph.D. degree from the Beijing University of Technology, Beijing, China, in 2012. He is currently a Lecturer with the Department of Automation, Faculty of Information Technology, Beijing University of Technology. She has authored over 30 papers, and most of them are indexed by SCI and EI. Her research interests include artificial intelligence, cognitive psychology, cognitive neuroscience, and the identification methods of depression.

Wenhua Lin is currently pursuing the master’s degree with the School of Information Science and Engineering, Lanzhou University, China. His current research interests include EEG signal feature extraction and classification-based nonlinear dynamics, and nonlinear dynamic-based study of depressed people and heroin abusers’ EEG.

Quanying Liu received the B.S. degree in telecommunication, and the M.S. degree in computer science from Lanzhou University, China, in 2010 and 2013, respectively. She is currently pursuing the Ph.D. degree in biomedical engineering with ETH Zürich, Switzerland. From 2010 to 2013, she was a Research Student with the Ubiquitous Awareness and Intelligent Solution Laboratory, Lanzhou University. From 2014 to 2015, she was with the Department of Experimental Psychology, University of Oxford. She joined KU Leuven in 2015. Since 2013, she has been with the Neural Control of Movement Laboratory, ETH Zürich. Her research interest include the development of methodologies to investigate the large-scale brain connectivity using high-density EEG data, including EEG signal processing, head model, EEG source localization, and functional connectivity approaches, and the relevant applications in drug-related addition, social anxiety disorders and sleeping. Dr. Liu received the Excellent Graduate Student Scholarship, China, the Swiss National Science Foundation for Mobility, and the Swiss National Science Foundation for Doc.Mobility.