Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer.

Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared to healthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta and delta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.

Epileptic seizure prediction based on features extracted from lagged Poincaré plots.

OBJECTIVE: The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR). METHODS: In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points. RESULTS: The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor. CONCLUSION: The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.

Estimating brain effective connectivity from EEG signals of patients with autism disorder and healthy individuals by reducing volume conduction effect.

Studying brain connectivity has shed light on understanding brain functions. Electroencephalogram signals recorded from the scalp surface comprise inter-dependent multi-channel signals each of which is a linear combination of simultaneously active brain sources as well as adjacent non-brain sources whose activity is widely volume conducted to the scalp through overlapping patterns. Evaluation of brain connectivity based on multivariate autoregressive (MVAR) model identification from neurological time series can be a proper tool for brain signal analysis. However, the MVAR model only considers the lagged influences between time series while ignoring the instantaneous effects (zero-lagged interactions) among simultaneously recorded neurological signals. Hence predicting instant interactions may result in fake connectivity, which may lead to misinterpreting in results. In this study, we aim to find instantaneous effects from coefficients of the MVAR model acquired using an ADALINE neural network and investigate the efficiency of the proposed algorithm by applying it to a simulated signal. We show that our coefficients are estimated accurately from channels of the simulated signal. Moreover, we apply the proposed method on a dataset of a group of 18 healthy children and 10 children with autism by comparing their effective connectivity estimated by direct directed transfer function method using new and old coefficients. Finally, to show the efficiency of the algorithm we exploit the support vector machine method for classifying the dataset. We show that there is a significant improvement in the results obtained from the proposed method.

Dynamic causal modeling of evoked responses during emergency braking: an ERP study.

Describing a neural activity map based on observed responses in emergency situations, especially during driving, is a challenging issue that would help design driver-assistant devices and a better understanding of the brain. This study aimed to investigate which regions were involved during emergency braking, measuring the interactions and strength of the connections and describing coupling among these brain regions by dynamic causal modeling (DCM) parameters that we extracted from event-related potential signals, which were then estimated based on emergency braking data with visual stimulation. The data were reanalyzed from a simulator study, which was designed to create emergency situations for participants during a simple driving task. The experimental protocol includes driving a virtual reality car, and the subjects were exposed to emergency situations in a simulator system, while electroencephalogram, electro-oculogram, and electromyogram signals were recorded. In this research, locations of active brain regions in montreal neurological institute coordinates from event-related responses were identified using multiple sparse priors method, in which sensor space was allocated to resource space. Source localization results revealed nine active regions. After applying DCM on data, a proposed model during emergency braking for all people was obtained. The braking response time was defined based on the first noticeable (above noise-level) braking pedal deflection after an induced braking maneuver. The result revealed a significant difference in response time between subjects who have the lateral connection between visual cortex, visual processing, and detecting objects areas have shorter response time (p-value = 0.05) than the subjects who do not have such connections.

Evaluating the Effect of Quran Memorizing on the Event-related Potential Features by Using Graphs Created from the Neural Gas Networks.

Background: Quran memorizing causes a state of trance, which its result is the changes in the amplitude and time of P300 and N200 components in the event related potential (ERP) signal. Nevertheless, a limited number of studies that have examined the effects of Quran memorizing on brain signals to enhance relaxation and attention, and improve the lives of patients with autism and stroke, generally have not presented any analysis based on comparing structural differences relevant to features extracted from ERP signal obtained from the two groups of Quran memorizer and nonmemorizer by using the hybrid of graph theory and competitive networks. Methods: In this study, we investigated structural differences relevant to the graph obtained from the weight of neural gas (NG) and growing NG (GNG) networks trained by features extracted from the ERP signal recorded from two groups during the PRM test. In this analysis, we actually estimated the ERP signal by averaging the brain background data in the recovery phase. Then, we extracted six features related to the power and the complexity of these signals and selected optimal channels in each of the features by using the t test analysis. Then, these features extracted from the optimal channels are applied for developing the NG and GNG networks. Finally, we evaluated different parameters calculated from graphs, in which their connection matrix was obtained from the weight matrix of the networks. Results: The outcomes of this analysis show that increasing the power of low frequency components and the power ratio of low frequency components to high frequency components in the memorizers, which represents patience, concentration, and relaxation, is more than that of the nonmemorizers. These outcomes also show that the optimal channels in different features, which were often in frontal, peritoneal, and occipital regions, had a significant difference (P < 0.05). It is remarkable that two parameters of the graphs established based on two competitive networks, i.e. average path length and the average of the weights in the memorizers, were larger than the nonmemorizers, which means more data scattering in this group. Conclusion: This condition in the mentioned graphs suggests that the Quran memorizing causes a significant change in ERP signals, so that its features have usually more scattering.

Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches.

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.

The Relation between Chaotic Feature of Surface EEG and Muscle Force: Case Study Report.

BACKGROUND: Nonlinear dynamics, especially the chaos characteristics, are useful in analyzing bio-potentials with many complexities. In this study, the evaluation of arm-tip force estimation method from the electroencephalography (EEG) signal in the vertical plane has been studied and chaos characteristics, including fractal dimension, Lyapunov exponent, entropy, and correlation dimension characteristics of EEG signals have been measured and analyzed at different levels of forces. METHOD: Electromyography signal was recorded with the help of the BIOPEC device (the Mp-100 model) and from the forearm muscle with surface electrodes, and the EEG signals were recorded from five major motor-related cortical areas according to 10-20 standard three times in a normal healthy 33-year-old male, athlete and right handed simultaneously with importing a force to 10 sinkers weighing from 10 to 100 Newton with step 10 Newton. RESULTS: The findings confirm that force estimation through EEG signals is feasible, especially using fractal dimension feature. The R-squared values for Fractal dimension, Lyapunov exponent, and entropy and correlation dimension features for linear trend line were 0.93, 0.7, 0.86, and 0.41, respectively. CONCLUSION: The linear increase of characteristics especially fractal dimension and entropy, together with the results from other EEG and neuroimaging studies, suggests that under normal conditions, brain recruits motor neurons at a linear progress when increasing the force.

Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy.

Directed information flow between brain regions might be disrupted in children with Attention Deficit Hyperactivity Disorder (ADHD) which is related to the behavioral characteristics of ADHD. This paper aims to investigate the different information pathways of brain networks in children with ADHD in comparison with healthy subjects. EEG recordings were obtained from 61 children with ADHD and 60 healthy children without neurological disorders during attentional visual task. Effective connectivity among all scalp channels was calculated using directed phase transfer entropy (dPTE) for delta, theta, alpha, beta, and lower-gamma frequency bands. Group differences were evaluated using permutation tests in connectivity between regions. Significant posterior to anterior patterns of information flow in theta frequency bands were found in healthy subjects (p-value < 0.05), while disrupted pattern flow, in an opposite way, was found in ADHD children. In the beta band, information flow in pathways between anterior regions was significantly higher in healthy individuals than in the ADHD group. These differences are more indicated in connectivity that leads from frontal and central regions to the right frontal regions of the brain (F8 electrode). Furthermore, connections from central and lateral parietal areas to Pz electrode areas are statistically significant and higher in healthy children in this band. In the delta band, internal connections in the anterior region show a significant difference between the two groups, as this amount is higher in the ADHD group. Our analysis may provide new insights into information flow in brain regions of ADHD children in comparison with healthy children.

Effect of inhibition indexed by auditory P300 on transmission of visual sensory information.

Early electroencephalographic studies that focused on finding brain correlates of psychic events led to the discovery of the P300. Since then, the P300 has become the focus of many basic and clinical neuroscience studies. However, despite its wide applications, the underlying function of the P300 is not yet clearly understood. One line of research among the many studies that have attempted to elucidate the underlying subroutine of the P300 in the brain has suggested that the physiological function of the P300 is related to inhibition. While some intracranial, behavioral, and event-related potential studies have provided support for this theory, little is known about the inhibitory mechanism. In this study, using alpha event-related desynchronization (ERD) and effective connectivity, based on the causal (one-way directed) relationship between alpha ERD and P300 sources, we demonstrated that P300's associated inhibition is implemented at a higher information processing stage in a localized brain region. We discuss how inhibition as the primary function of the P300 is not inconsistent with 'resource allocation' and 'working memory updating' theories about its cognitive function. In light of our findings regarding the scope and information processing stage of inhibition of the P300, we reconcile the inhibitory account of the P300 with working memory updating theory. Finally, based on the compensatory behavior of alpha ERD at the time of suppression of the P300, we propose two distinct yet complementary working memory mechanisms (inhibition and desynchronizing excitation) that render target perception possible.

EEG signal analysis during Ishihara's test in subjects with normal vision and color vision deficiency.

Color Vision Deficiency (CVD) is one of the most common types of vision deficiency. People with CVD have difficulty seeing color spectra depending on what types of retina photoreceptors are impaired. In this paper, the Ishihara test with 38 plates was used to examine the Electroencephalogram (EEG) of ten subjects with CVD plus ten healthy individuals. The recording was performed according to the 10-20 international system. The C-based software was programmed so that subjects could select the number or path in each test plate in the software options while recording EEG. Frequency features in different frequency bands were extracted from the EEG signals of the two groups during the Ishihara test. Statistically significant differences (P < 0.05) between features were assessed by independent samples t-test with False Discovery Rate (FDR) correction. Also, the K-nearest neighbor classifier (KNN) was used to classify the two groups. The results revealed that the most significant difference between the two groups in the Ishihara test images occurred for the electrodes located in the right temporoparietal areas (P4 and T6) of the brain in the Delta, Theta, Beta1, and Beta2 frequency bands. The KNN classifier, using the signals that reported the greatest statistical difference between the two groups, showed that the two groups were distinguishable with 85.2% accuracy. In this way, images from the Ishihara test that would provide the most accurate classification were identified. In conclusion, this research provided new insights into EEG signals of subjects with CVD and healthy subjects based on the Ishihara color vision test.

First Event-Related Potentials Evidence of Auditory Morphosyntactic Processing in a Subject-Object-Verb Nominative-Accusative Language (Farsi).

While most studies on neural signals of online language processing have focused on a few-usually western-subject-verb-object (SVO) languages, corresponding knowledge on subject-object-verb (SOV) languages is scarce. Here we studied Farsi, a language with canonical SOV word order. Because we were interested in the consequences of second-language acquisition, we compared monolingual native Farsi speakers and equally proficient bilinguals who had learned Farsi only after entering primary school. We analyzed event-related potentials (ERPs) to correct and morphosyntactically incorrect sentence-final syllables in a sentence correctness judgment task. Incorrect syllables elicited a late posterior positivity at 500-700 ms after the final syllable, resembling the P600 component, as previously observed for syntactic violations at sentence-middle positions in SVO languages. There was no sign of a left anterior negativity (LAN) preceding the P600. Additionally, we provide evidence for a real-time discrimination of phonological categories associated with morphosyntactic manipulations (between 35 and 135 ms), manifesting the instantaneous neural response to unexpected perturbations. The L2 Farsi speakers were indistinguishable from L1 speakers in terms of performance and neural signals of syntactic violations, indicating that exposure to a second language at school entry may results in native-like performance and neural correlates. In nonnative (but not native) speakers verbal working memory capacity correlated with the late posterior positivity and performance accuracy. Hence, this first ERP study of morphosyntactic violations in a spoken SOV nominative-accusative language demonstrates ERP effects in response to morphosyntactic violations and the involvement of executive functions in non-native speakers in computations of subject-verb agreement.

The 2017 and 2018 Iranian Brain-Computer Interface Competitions.

This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms and discuss the organizational strategies for future campaigns.

Nonlinear Analysis of Electroencephalogram Signals while Listening to the Holy Quran.

BACKGROUND: Electrical activity of the brain, resulting from electrochemical signaling between neurons, is recorded by electroencephalogram (EEG). The neural network has complex behavior at different levels that strongly confirms the nonlinear nature of interactions in the human brain. This study has been designed and implemented with the aim of determining the effects of religious beliefs and the effect of listening to Holy Quran on electrical activity of the brain of the Iranian Persian-speaking Muslim volunteers. METHODS: The brain signals of 47 Persian-speaking Muslim volunteers while listening to the Holy Quran consciously, and while listening to the Holy Quran and the Arabic text unconsciously were used. Therefore, due to the nonlinear nature of EEG signals, these signals are studied using approximate entropy, sample entropy, Hurst exponent, and Detrended Fluctuation Analysis. RESULTS: Statistical analysis of the results has shown that listening to the Holy Quran consciously increases approximate entropy and sample entropy, and decreases Hurst Exponent and Detrended Fluctuation Analysis compared to other cases. CONCLUSION: Consciously listening to the Holy Quran decreases self-similarity and correlation of brain signal and instead increases complexity and dynamicity in the brain.

Bypassing the volume conduction effect by multilayer neural network for effective connectivity estimation.

Differentiation of real interactions between different brain regions from spurious ones has been a challenge in neuroimaging researches. While using electroencephalographic data, those spurious interactions are mostly caused by the volume conduction (VC) effect between the recording sites. In this study, we address the problem by jointly modeling the causal relationships among brain regions and the mixing effects of volume conduction. The VC effect is formulated with a time-invariant linear equation, and the causal relationships between the brain regions are modeled with a nonlinear multivariate autoregressive process. These two models are simultaneously implemented by a multilayer neural network. The internal hidden layers represent the interactions among the regions, while the external layers are devoted for the relationship between the source activities and observed EEG measurements at the scalp. The causal interactions are estimated by the causality coefficient measure, which is based on the information (weights and parameters) embedded in the network. The proposed method is verified using various simulated data. It is then applied to the real EEG signals collected from a memory retrieval test. The results showed that the method is able to eliminate the volume conduction interferences and consequently leads to higher accuracy in identification of true causal interactions. Graphical abstract The proposed network structure used to simultaneously model the volume conduction and source interactions. By this special structure, we first move from the sensor space to the source space at the first layer. Then, within internal hidden layers, the interactions between the sources are represented in the form of a general (nonlinear) multivariate autoregressive (nMVAR) model. Finally, we return from the source space to the sensor space at the last layer of the network. The proposed method bypasses the effect of volume conduction and causes more accurate connectivity estimation.

nCREANN: Nonlinear Causal Relationship Estimation by Artificial Neural Network; Applied for Autism Connectivity Study.

Quantifying causal (effective) interactions between different brain regions are very important in neuroscience research. Many conventional methods estimate effective connectivity based on linear models. However, using linear connectivity models may oversimplify the functions and dynamics of the brain. In this paper, we propose a causal relationship estimator called nonlinear Causal Relationship Estimation by Artificial Neural Network (nCREANN) that identifies both linear and nonlinear components of effective connectivity in the brain. Furthermore, it can distinguish between these two types of connectivity components by calculating the linear and nonlinear parts of the network input-output mapping. The nCREANN performance has been verified using synthesized data and then it has been applied on EEG data collected during rest in children with autism spectrum disorder (ASD) and typically developing (TD) children. The results show that overall linear connectivity in TD subjects is higher, while the nonlinear connectivity component is more dominant in ASDs. We suggest that our findings may represent different underlying neural activation dynamics in ASD and TD subjects. The results of nCREANN may provide new insight into the connectivity between the interactive brain regions.

Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory.

Emotions play an important role in our life. Emotion recognition which is considered a subset of brain computer interface (BCI), has drawn a great deal of attention during recent years. Researchers from different fields have tried to classify emotions through physiological signals. Nonlinear analysis has been reported to be successful and effective in emotion classification due to the nonlinear and non-stationary behavior of biological signals. In this study, phase space reconstruction and Poincare planes are employed to describe the dynamics of electroencephalogram (EEG) in emotional states. EEG signals are taken from a reliable database and phase space is reconstructed. A new transformation is introduced in order to quantify the phase space. Dynamic characteristics of the new space are considered as features. Most significant features are selected and samples are classified into four groups including high arousal - high valence (HAHV), low arousal - high valence (LAHV), high arousal - low valence (HALV) and low arousal - low valence (LALV). Classification accuracy was about 90% on average. Results suggest that the proposed method is successful and classification performance is good in comparison with most studies in this field. Brain activity is also reported with respect to investigating brain function during emotion elicitation. We managed to introduce a new way to analyze EEG phase space. The proposed method is applied in a real world and challenging application (i.e. emotion classification). Not only does the proposed method describe EEG changes due to different emotional states but also it is able to represent new characteristics of complex systems. The suggested approach paves the way for researchers to analyze and understand more about chaotic signals and systems.

A self-organized recurrent neural network for estimating the effective connectivity and its application to EEG data.

OBJECTIVE: Effective connectivity is an important notion in neuroscience research, useful for detecting the interactions between regions of the brain. NEW METHOD: Since we are dealing with a dynamic system, it seems that using a dynamic tool could effectively achieve better results. In this paper, a novel approach, called "Recurrent Neural Network - Neuron Growth Using Error Whiteness - Granger Causality" (RNN-NGUEW-GC) is proposed to estimate the effective connectivity. An RNN is used for predicting and modeling time series and multivariate signals. NGUEW is used to determine the optimum time lag with the help of an error whiteness criterion. When this criterion is not satisfied, the number of neurons in the network input is increased, producing an increase in the time lag. Accordingly, the network achieves a self-organized structure. Finally, causal effects are determined for linear and nonlinear models using the concept of Granger causality. Also, an indicator of the ''intensity of causality'' is defined to approximate the strength of the linear interactions based on the structure of the network and the weights of the connections. CONCLUSIONS: RNN-NGUEW-GC had a major outcome in terms of both method accuracy on simulation data and prediction of epileptic seizures on the EEG dataset. The main advantages of this method in comparison with other methods of determining the effective connectivity are: 1) there is no need for physiological information; 2) it yields a self-organized network structure. In addition, the calculation of the appropriate time lag using NGUEW is another superiority of this method in comparison with multivariate auto-regressive models.

Effective connectivity of mental fatigue: Dynamic causal modeling of EEG data.

BACKGROUND: Recognition of sources in the brain and their interaction with mental fatigue states are interesting subjects for researchers. OBJECTIVE: The aim of this study was to investigate the mental fatigue effects on brain areas by dynamic casual modeling (DCM) parameters that are extracted from event-related potential (ERP) signals which were then estimated based on mental fatigue data with visual stimulation. METHODS: ERP were recorded based on a Continuous Performance Task in four consecutive trials. Active regions and brain sources were extracted by a Multiple Sparse Priors algorithm. RESULTS: Four models are proposed for DCM. The parameters and the structure of the best model were obtained by SPM software for ERP in each of the four trials. CONCLUSION: The results illustrate that an increase of mental fatigue through trials leads to increased likelihood of choosing forward models.

Network physiology of 'fight or flight' response in facial superficial blood vessels.

OBJECTIVE: We introduced a novel framework to identify the dynamic pattern of blood flow changes in the cutaneous superficial blood vessels of the face for 'fight or flight' responses through facial thermal imaging. APPROACH: For this purpose, a thermal dataset was collected from 41 subjects in a mock crime scenario. Five facial areas including periorbital, forehead, perinasal, cheek and chin were selected on the face. Due to the cause and effect movement of blood in the facial cutaneous vasculature, the effective connectivity approach and graph analysis were used to extract causality features. The effective connectivity was quantified using a modified version of the multivariate Granger causality (GC) method among each pair of facial region of interests. MAIN RESULTS: Validation was performed using statistical analysis, and the results demonstrated that the proposed method was statistically significant in detecting the physiological pattern of deceptive anxiety on the face. Moreover, the obtained graph is visualized by different schemes to show these interactions more effectively. We used machine learning techniques to classify our data based on the GC values, which result in a greater than 87% accuracy rate in discriminating between deceptive and truthful subjects.

A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.

BACKGROUND: Emotion recognition is an increasingly important field of research in brain computer interactions. INTRODUCTION: With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature. METHODS: The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence. RESULTS: The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced. CONCLUSIONS: The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.