Analysis of functional brain connections for positive-negative emotions using phase locking value

EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion

Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods.

HiRENet: Novel convolutional neural network architecture using Hilbert-transformed and raw electroencephalogram (EEG) for subject-independent emotion classification

BACKGROUND AND OBJECTIVES

Convolutional neural networks (CNNs) are the most widely used deep-learning framework for decoding electroencephalograms (EEGs) due to their exceptional ability to extract hierarchical features from high-dimensional EEG data. Traditionally, CNNs have primarily utilized multi-channel raw EEG data as the input tensor; however, the performance of CNN-based EEG decoding may be enhanced by incorporating phase information alongside amplitude information.

METHODS

This study introduces a novel CNN architecture called the Hilbert-transformed (HT) and raw EEG network (HiRENet), which incorporates both raw and HT EEG as inputs. This concurrent use of HT and raw EEG aims to integrate phase information with existing amplitude information, potentially offering a more comprehensive reflection of functional connectivity across various brain regions. The HiRENet model was developed using two CNN frameworks: ShallowFBCSPNet and a CNN with a residual block (ResCNN). The performance of the HiRENet model was assessed using a lab-made EEG database to classify human emotions, comparing three input modalities: raw EEG, HT EEG, and a combination of both signals. Additionally, the computational complexity was evaluated to validate the computational efficiency of the ResCNN design.

RESULTS

The HiRENet model based on ResCNN achieved the highest classification accuracy, with 86.03% for valence and 84.01% for arousal classifications, surpassing traditional CNN methodologies. Considering computational efficiency, ResCNN demonstrated superiority over ShallowFBCSPNet in terms of speed and inference time, despite having a higher parameter count.

CONCLUSION

Our experimental results showed that the proposed HiRENet can be potentially used as a new option to improve the overall performance for deep learning-based EEG decoding problems.

Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior

Objective

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.

Approach

We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.

Main results

Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference.

Significance

Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

Research on Brain Networks of Human Balance Based on Phase Estimation Synchronization

Phase synchronization serves as an effective method for analyzing the synchronization of electroencephalogram (EEG) signals among brain regions and the dynamic changes of the brain. The purpose of this paper is to study the construction of the functional brain network (FBN) based on phase synchronization, with a special focus on neural processes related to human balance regulation. This paper designed four balance paradigms of different difficulty by blocking vision or proprioception and collected 19-channel EEG signals. Firstly, the EEG sequences are segmented by sliding windows. The phase-locking value (PLV) of core node pairs serves as the phase-screening index to extract the valid data segments, which are recombined into new EEG sequences. Subsequently, the multichannel weighted phase lag index (wPLI) is calculated based on the new EEG sequences to construct the FBN. The experimental results show that due to the randomness of the time points of body balance adjustment, the degree of phase synchronization of the datasets screened by PLV is more obvious, improving the effective information expression of the subsequent EEG data segments. The FBN topological structures of the wPLI show that the connectivity of various brain regions changes structurally as the difficulty of human balance tasks increases. The frontal lobe area is the core brain region for information integration. When vision or proprioception is obstructed, the EEG synchronization level of the corresponding occipital lobe area or central area decreases. The synchronization level of the frontal lobe area increases, which strengthens the synergistic effect among the brain regions and compensates for the imbalanced response caused by the lack of sensory information. These results show the brain regional characteristics of the process of human balance regulation under different balance paradigms, providing new insights into endogenous neural mechanisms of standing balance and methods of constructing brain networks.

ST-SCGNN: A Spatio-Temporal Self-Constructing Graph Neural Network for Cross-Subject EEG-Based Emotion Recognition and Consciousness Detection

In this paper, a novel spatio-temporal self-constructing graph neural network (ST-SCGNN) is proposed for cross-subject emotion recognition and consciousness detection. For spatio-temporal feature generation, activation and connection pattern features are first extracted and then combined to leverage their complementary emotion-related information. Next, a self-constructing graph neural network with a spatio-temporal model is presented. Specifically, the graph structure of the neural network is dynamically updated by the self-constructing module of the input signal. Experiments based on the SEED and SEED-IV datasets showed that the model achieved average accuracies of 85.90% and 76.37%, respectively. Both values exceed the state-of-the-art metrics with the same protocol. In clinical besides, patients with disorders of consciousness (DOC) suffer severe brain injuries, and sufficient training data for EEG-based emotion recognition cannot be collected. Our proposed ST-SCGNN method for cross-subject emotion recognition was first attempted in training in ten healthy subjects and testing in eight patients with DOC. We found that two patients obtained accuracies significantly higher than chance level and showed similar neural patterns with healthy subjects. Covert consciousness and emotion-related abilities were thus demonstrated in these two patients. Our proposed ST-SCGNN for cross-subject emotion recognition could be a promising tool for consciousness detection in DOC patients.

A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition

Introduction

Affective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application.

Methods

In the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied.

Results

The public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%.

Discussion

The experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.

Impact of negative links on the structural balance of brain functional network during emotion processing

Activation of specific brain areas and synchrony between them has a major role in process of emotions. Nevertheless, impact of anti-synchrony (negative links) in this process still requires to be understood. In this study, we hypothesized that quantity and topology of negative links could influence a network stability by changing of quality of its triadic associations. Therefore, a group of healthy participants were exposed to pleasant and unpleasant images while their brain responses were recorded. Subsequently, functional connectivity networks were estimated and quantity of negative links, balanced and imbalanced triads, tendency to make negative hubs, and balance energy levels of two conditions were compared. The findings indicated that perception of pleasant stimuli was associated with higher amount of negative links with a lower tendency to make a hub in theta band; while the opposite scenario was observed in beta band. It was accompanied with smaller number of imbalanced triads and more stable network in theta band, and smaller number of balanced triads and less stable network in beta band. The findings highlighted that inter regional communications require less changes to receive new information from unpleasant stimuli, although by decrement in beta band stability prepares the network for the upcoming events.

Altered resting-state network connectivity patterns for predicting attentional function in deaf individuals: An EEG study

Multiple aspects of brain development are influenced by early sensory loss such as deafness. Despite growing evidence of changes in attentional functions for prelingual profoundly deaf, the brain mechanisms underlying these attentional changes remain unclear. This study investigated the relationships between differential attention and the resting-state brain network difference in deaf individuals from the perspective of brain network connectivity. We recruited 36 deaf individuals and 34 healthy controls (HC). We recorded each participant's resting-state electroencephalogram (EEG) and the event-related potential (ERP) data from the Attention Network Test (ANT). The coherence (COH) method and graph theory were used to build brain networks and analyze network connectivity. First, the ERPs of analysis in task states were investigated. Then, we correlated the topological properties of the network functional connectivity with the ERPs. The results revealed a significant correlation between frontal-occipital connection in the resting state and the amplitude of alert N1 amplitude in the alpha band. Specifically, clustering coefficients and global and local efficiency correlate negatively with alert N1 amplitude, whereas the characteristic path length positively correlates with alert N1 amplitude. In addition, deaf individuals exhibited weaker frontal-occipital connections compared to the HC group. In executive control, the deaf group had longer reaction times and larger P3 amplitudes. However, the orienting function did not significantly differ from the HC group. Finally, the alert N1 amplitude in the ANT task for deaf individuals was predicted using a multiple linear regression model based on resting-state EEG network properties. Our results suggest that deafness affects the performance of alerting and executive control while orienting functions develop similarly to hearing individuals. Furthermore, weakened frontal-occipital connections in the deaf brain are a fundamental cause of altered alerting functions in the deaf. These results reveal important effects of brain networks on attentional function from the perspective of brain connections and provide potential physiological biomarkers to predicting attention.

Decoding lexical tones and vowels in imagined tonal monosyllables using fNIRS signals

Objective.Speech is a common way of communication. Decoding verbal intent could provide a naturalistic communication way for people with severe motor disabilities. Active brain computer interaction (BCI) speller is one of the most commonly used speech BCIs. To reduce the spelling time of Chinese words, identifying vowels and tones that are embedded in imagined Chinese words is essential. Functional near-infrared spectroscopy (fNIRS) has been widely used in BCI because it is portable, non-invasive, safe, low cost, and has a relatively high spatial resolution.Approach.In this study, an active BCI speller based on fNIRS is presented by covertly rehearsing tonal monosyllables with vowels (i.e. /a/, /i/, /o/, and /u/) and four lexical tones in Mandarin Chinese (i.e. tones 1, 2, 3, and 4) for 10 s.Main results.fNIRS results showed significant differences in the right superior temporal gyrus between imagined vowels with tone 2/3/4 and those with tone 1 (i.e. more activations and stronger connections to other brain regions for imagined vowels with tones 2/3/4 than for those with tone 1). Speech-related areas for tone imagery (i.e. the right hemisphere) provided majority of information for identifying tones, while the left hemisphere had advantages in vowel identification. Having decoded both vowels and tones during the post-stimulus 15 s period, the average classification accuracies exceeded 40% and 70% in multiclass (i.e. four classes) and binary settings, respectively. To spell words more quickly, the time window size for decoding was reduced from 15 s to 2.5 s while the classification accuracies were not significantly reduced.Significance.For the first time, this work demonstrated the possibility of discriminating lexical tones and vowels in imagined tonal syllables simultaneously. In addition, the reduced time window for decoding indicated that the spelling time of Chinese words could be significantly reduced in the fNIRS-based BCIs.

Relations between family cohesion and adolescent-parent's neural synchrony in response to emotional stimulations

BACKGROUND

The interaction between parent and adolescent is more challenging than in other age periods. Family cohesion seriously impacts parent-adolescent emotional interactions. However, the underlying neural mechanism has not been fully examined. This study examined the differences in the neural synchrony in response to emotional film clips between high and low family cohesion adolescent-parent dyads by using the electroencephalograph (EEG) hyperscanning.

RESULTS

Simultaneously electroencephalograph (EEG) was recorded while 15 low family cohesion parent-adolescent dyads (LFCs)and 14 high family cohesion parent-adolescent dyads (HFCs)received different emotional induction when viewing film clips. Interbrain phase-locking-value (PLV) in gamma band was used to calculate parent-adolescent dyads' interbrain synchrony. Results showed that higher gamma interbrain synchrony was observed in the HFCs than the LFCs in the positive conditions. However, there was no significant difference between the HFCs and LFCs in other conditions. Also, the HFCs had significantly higher gamma interbrain synchrony in the positive conditions than in the negative conditions.

CONCLUSION

Interbrain synchrony may represent an underlying neural mechanism of the parent-adolescent emotional bonding, which is the core of family cohesion.

Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients with Major Depressive Disorder

Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.

Dynamic Functional Connectivity of Emotion Processing in Beta Band with Naturalistic Emotion Stimuli

While naturalistic stimuli, such as movies, better represent the complexity of the real world and are perhaps crucial to understanding the dynamics of emotion processing, there is limited research on emotions with naturalistic stimuli. There is a need to understand the temporal dynamics of emotion processing and their relationship to different dimensions of emotion experience. In addition, there is a need to understand the dynamics of functional connectivity underlying different emotional experiences that occur during or prior to such experiences. To address these questions, we recorded the EEG of participants and asked them to mark the temporal location of their emotional experience as they watched a video. We also obtained self-assessment ratings for emotional multimedia stimuli. We calculated dynamic functional the connectivity (DFC) patterns in all the frequency bands, including information about hubs in the network. The change in functional networks was quantified in terms of temporal variability, which was then used in regression analysis to evaluate whether temporal variability in DFC (tvDFC) could predict different dimensions of emotional experience. We observed that the connectivity patterns in the upper beta band could differentiate emotion categories better during or prior to the reported emotional experience. The temporal variability in functional connectivity dynamics is primarily related to emotional arousal followed by dominance. The hubs in the functional networks were found across the right frontal and bilateral parietal lobes, which have been reported to facilitate affect, interoception, action, and memory-related processing. Since our study was performed with naturalistic real-life resembling emotional videos, the study contributes significantly to understanding the dynamics of emotion processing. The results support constructivist theories of emotional experience and show that changes in dynamic functional connectivity can predict aspects of our emotional experience.

Aberrant Whole-Brain Resting-State Functional Connectivity Architecture in Obsessive-Compulsive Disorder: An EEG Study

Obsessive-compulsive disorder (OCD) is a common neuropsychiatric disorder characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions), and few studies have assessed the whole-brain functional connectivity architecture of OCD with electroencephalogram (EEG) during different resting states. Graph theory and network-based statistics (NBS) were employed to examine the neural synchronization and the whole-brain functional connectivity (FC) based on the phase-locking value (PLV) of OCD patients and healthy controls (HCs) during eyes-closed (EC) and eyes-open (EO) states. Compared with HCs, OCD patients exhibited not only decreased global synchronization in terms of phase synchrony but also aberrant global topological properties (decreased average shortest path lengths and normalized shortest path lengths together with increased global efficiencies and normalized clustering coefficients) together with inhibited intra-hemispheric and interhemispheric FCs during rest, which suggested an imbalance between functional integration and segregation of brain networks for OCD patients. Meanwhile, OCD patients had increased global efficiencies and normalized clustering coefficients, but decreased average clustering coefficients and normalized shortest path lengths together with significantly decreased FCs in the alpha band from EC to EO states, which suggested a dynamic switch between highly integrated (EC state) and highly specialized (EO state) modes of information processing. Moreover, the decreased FCs of OCD patients showed obvious hemispheric asymmetry within or between groups during EC and EO states, which might serve as a potential biomarker to classify OCD patients from HCs.

Fusion of EEG-Based Activation, Spatial, and Connection Patterns for Fear Emotion Recognition

At present, emotion recognition based on electroencephalograms (EEGs) has attracted much more attention. Current studies of affective brain-computer interfaces (BCIs) focus on the recognition of happiness and sadness using brain activation patterns. Fear recognition involving brain activities in different spatial distributions and different brain functional networks has been scarcely investigated. In this study, we propose a multifeature fusion method combining energy activation, spatial distribution, and brain functional connection network (BFCN) features for fear emotion recognition. The affective brain pattern was identified by not only the power activation features of differential entropy (DE) but also the spatial distribution features of the common spatial pattern (CSP) and the EEG phase synchronization features of phase lock value (PLV). A total of 15 healthy subjects took part in the experiment, and the average accuracy rate was 85.00% ± 8.13%. The experimental results showed that the fear emotions of subjects were fully stimulated and effectively identified. The proposed fusion method on fear recognition was thus validated and is of great significance to the development of effective emotional BCI systems.

Functional neuronal networks reveal emotional processing differences in children with ADHD

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that, in addition to inattention, excessive activity, or impulsivity, makes it difficult for children to process facial emotions and thus to interact with their peers. Here we analyze neuronal networks of children with this disorder by means of the phase-locking value (PLV) method. In particular, we determine the level of phase synchronization between 62 EEG channels of 22 healthy boys and 22 boys with ADHD, recorder whilst observing facial emotions of anger, happiness, neutrality, and sadness. We construct neuronal networks based on the gamma sub-band, which according to previous studies, shows the highest response to emotional stimuli. We find that the functional connectivity of the frontal and occipital lobes in the ADHD group is significantly (P-value < 0.01) higher than in the healthy group. More functional connectivity in these lobes shows more phase synchronization between the neurons of these brain regions, representing some problems in the brain emotional processing center in the ADHD group. The shortest path lengths in these lobes are also significantly (P-value < 0.01) higher in the ADHD group than in the healthy group. This result indicates less efficiency of information transmission and segregation in occipital and frontal lobes of ADHD neuronal networks, responsible for visual and emotional processing in the brain, respectively. We hope that our approach will help obtain further insights into ADHD with methods of network science.

Investigating EEG-based functional connectivity patterns for multimodal emotion recognition

Objective. Previous studies on emotion recognition from electroencephalography (EEG) mainly rely on single-channel-based feature extraction methods, which ignore the functional connectivity between brain regions. Hence, in this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. Approach. After constructing the brain networks by the correlations between pairs of EEG signals, we calculated critical subnetworks through the average of brain network matrices with the same emotion label to eliminate the weak associations. Then, three network features were conveyed to a multimodal emotion recognition model using deep canonical correlation analysis along with eye movement features. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public datasets: SEED, SEED-V, and DEAP. Main results. The experimental results reveal that the strength feature outperforms the state-of-the-art features based on single-channel analysis. The classification accuracies of multimodal emotion recognition are 95.08 ± 6.42 % on the SEED dataset, 84.51 ± 5.11 % on the SEED-V dataset, and 85.34 ± 2.90 % and 86.61 ± 3.76 % for arousal and valence on the DEAP dataset, respectively, which all achieved the best performance. In addition, the brain networks constructed with 18 channels achieve comparable performance with that of the 62-channel network and enable easier setups in real scenarios. Significance. The EEG functional connectivity networks combined with emotion-relevant critical subnetworks selection algorithm we proposed is a successful exploration to excavate the information between channels.

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and visualization play an important role in evaluating brain cognitive function. However, existing similar FC analysis tools are not only visualized in 2 dimensions (2D) but also are highly prone to cause visual clutter and unable to dynamically reflect brain connectivity changes over time. Therefore, we design and implement an EEG-based FC visualization framework in this study, named EEG-FCV, for brain cognitive state evaluation. EEG-FCV is composed of three parts: the Data Processing module, Connectivity Analysis module, and Visualization module. Specially, FC is visualized in 3 dimensions (3D) by introducing three existing metrics: Pearson Correlation Coefficient (PCC), Coherence, and PLV. Furthermore, a novel metric named Comprehensive is proposed to solve the problem of visual clutter. EEG-FCV can also visualize dynamically brain FC changes over time. Experimental results on two available datasets show that EEG-FCV has not only results consistent with existing related studies on brain FC but also can reflect dynamically brain FC changes over time. We believe EEG-FCV could prompt further progress in brain cognitive function evaluation.

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.

Comparative analysis of default mode networks in major psychiatric disorders using resting-state EEG

Default mode network (DMN) is a set of functional brain structures coherently activated when individuals are in resting-state. In this study, we constructed multi-frequency band resting-state EEG-based DMN functional network models for major psychiatric disorders to easily compare their pathophysiological characteristics. Phase-locking values (PLVs) were evaluated to quantify functional connectivity; global and nodal clustering coefficients (CCs) were evaluated to quantify global and local connectivity patterns of DMN nodes, respectively. DMNs of patients with post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), panic disorder, major depressive disorder (MDD), bipolar disorder, schizophrenia (SZ), mild cognitive impairment (MCI), and Alzheimer's disease (AD) were constructed relative to their demographically-matched healthy control groups. Overall DMN patterns were then visualized and compared with each other. In global CCs, SZ and AD showed hyper-clustering in the theta band; OCD, MCI, and AD showed hypo-clustering in the low-alpha band; OCD and MDD showed hypo-clustering and hyper-clustering in low-beta, and high-beta bands, respectively. In local CCs, disease-specific patterns were observed. In the PLVs, lowered theta-band functional connectivity between the left lingual gyrus and the left hippocampus was frequently observed. Our comprehensive comparisons suggest EEG-based DMN as a useful vehicle for understanding altered brain networks of major psychiatric disorders.

Emotional Information in News Reporting on Audience Cognitive Processing in the Age of Posttruth: An Electroencephalogram and Functional Connectivity Approach

The purpose of the present study is to explore how the emotionalized expression of news content in the posttruth era affects the cognitive processing of the audiences. One news that was text-written with two different expression types (emotional expression vs. neutral expression) was adopted as an experiment material in the study, and changes in cortical activity during news reporting reading tasks were examined with electroencephalograms, sampled from nine sites and four channels and analyzed with weighted phase lag index (wPLI) based on brain functional connectivity (FC) method. The results show that emotional discourses caused a stronger cortical brain activity and more robust brain FC (beta oscillations); besides, reading emotionalized expression consumed more attention resources but fewer cognitive resources, which may impede further rational thinking of the audiences.

A novel facial attractiveness evaluation system based on face shape, facial structure features and skin

Facial attractiveness is an important research direction of genetic psychology and cognitive psychology, and its results are significant for the study of face evolution and human evolution. However, previous studies have not put forward a comprehensive evaluation system of facial attractiveness. Traditionally, the establishment of facial attractiveness evaluation system was based on facial geometric features, without facial skin features. In this paper, combined with big data analysis, evaluation of face in real society and literature research, we found that skin also have a significant impact on facial attractiveness, because skin could reflect age, wrinkles and healthful qualities, thus affected the human perception of facial attractiveness. Therefore, we propose a comprehensive and novel facial attractiveness evaluation system based on face shape structural features, facial structure features and skin texture feature. In order to apply face shape structural features to the evaluation of facial attractiveness, the classification of face shape is the first step. Face image dataset is divided according to face shape, and then facial structure features and skin texture features that represent facial attractiveness are extracted and fused. The machine learning algorithm with the best prediction performance is selected in the face shape structural subsets to predict facial attractiveness. Experimental results show that the facial attractiveness evaluation performance can be improved by the method based on classification of face shape and multi-features fusion, the facial attractiveness scores obtained by the proposed system correlates better with human ratings. Our evaluation system can help people project their cognition of facial attractiveness into artificial agents they interact with.

The late parietal event-related potential component is hierarchically sensitive to chunk tightness during chunk decomposition

The current study analyzed event-related potentials (ERPs) associated with visuo-spatial transformation in order to examine how "chunk tightness" affects the difficulty of chunk decomposition problems. Participants completed a Chinese character decomposition task in three conditions according to the tightness of the to-be-decomposed chunk (tight vs. medium vs. loose). Behavioral data showed that performance became worse (longer reaction time, lower accuracy) as chunk tightness increased. ERP data showed that, as chunk tightness increased, the LPC exhibited a significant decrease at posterior electrode sites. The results indicate that chunk tightness might exert its primary effect on chunk decomposition difficulty by increasing the difficulty of visuo-spatial transformation, a process linked to the parietal LPC.

Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data

Investigating human brain activity during expressing emotional states provides deep insight into complex cognitive functions and neurological correlations inside the brain. To be able to resemble the brain function in the best manner, a complex and natural stimulus should be applied as well, the method used for data analysis should have fewer assumptions, simplifications, and parameter adjustment. In this study, we examined a functional magnetic resonance imaging dataset obtained during an emotional audio-movie stimulus associated with human life. We used Jackknife Correlation (JC) method to derive a representation of time-varying functional connectivity. We applied different binary measures and thoroughly investigated two weighted measures to study different properties of binary and weighted temporal networks. Using this approach, we indicated different aspects of human brain function during expressing different emotions. The findings of global and nodal measures could demonstrate a significant difference between emotions and significant regions in each emotion, respectively. Also, the temporal centrality properties of nodes were different in emotional states. Ultimately, we showed that the resulting measures of temporal snapshots created by JC method can distinguish between different emotions.

Brain activity during time to contact estimation: an EEG study

Understanding the neural mechanisms associated with time to contact (TTC) estimation is an intriguing but challenging task. Despite the importance of TTC estimation in our everyday life, few studies have been conducted on it, and there are still a lot of unanswered questions and unknown aspects of this issue. In this study, we intended to address one of these unknown aspects. We used independent component analysis to systematically assess EEG substrates associated with TTC estimation using two experiments: (1) transversal motion experiment (when a moving object passes transversally in the frontoparallel plane from side to side in front of the observer), and (2) head-on motion experiment (when the observer is on the motion path of the moving object). We also studied the energy of all EEG sources in these two experiments. The results showed that brain regions involved in the transversal and head-on motion experiments were the same. However, the energy used by some brain regions in the head-on motion experiment, including some regions in left parietotemporal and left frontal lobes, was significantly higher than the energy used by those regions in the transversal motion experiment. These brain regions are dominantly associated with different kinds of visual attention, integration of visual information, and responding to visual motion.

Decoding disparity categories in 3-dimensional images from fMRI data using functional connectivity patterns

Humans use binocular disparity to extract depth information from two-dimensional retinal images in a process called stereopsis. Previous studies usually introduce the standard univariate analysis to describe the correlation between disparity level and brain activity within a given brain region based on functional magnetic resonance imaging (fMRI) data. Recently, multivariate pattern analysis has been developed to extract activity patterns across multiple voxels for deciphering categories of binocular disparity. However, the functional connectivity (FC) of patterns based on regions of interest or voxels and their mapping onto disparity category perception remain unknown. The present study extracted functional connectivity patterns for three disparity conditions (crossed disparity, uncrossed disparity, and zero disparity) at distinct spatial scales to decode the binocular disparity. Results of 27 subjects' fMRI data demonstrate that FC features are more discriminatory than traditional voxel activity features in binocular disparity classification. The average binary classification of the whole brain and visual areas are respectively 87% and 79% at single subject level, and thus above the chance level (50%). Our research highlights the importance of exploring functional connectivity patterns to achieve a novel understanding of 3D image processing.

Efficiency of Repetitive Transcranial Direct Current Stimulation of the Dorsolateral Prefrontal Cortex in Disorders of Consciousness: A Randomized Sham-Controlled Study

Conventional transcranial direct current stimulation (tDCS) targeting the left dorsolateral prefrontal cortex (DLPFC) could improve arousal in disorders of consciousness (DOC). However, the comparative effectiveness of anodal stimulation of the left DLPFC and the electrophysiological effect of tDCS are yet to be determined. In this randomized sham-controlled design, patients were separated into three groups (left/right anodal tDCS, sham). Data on the clinical assessments and EEG were collected at baseline and after 2 weeks of tDCS. The outcome at 3-month follow-up was evaluated using the Glasgow Outcome Scale-Extended. Results showed that sessions of the left tDCS facilitated the excitability of the prefrontal cortex, whereas only one patient had a positive outcome. Targeting the right DLPFC was less effective, merely leading to activation of the stimulation site, with no effect on the state of arousal. Moreover, sham stimulation had minimal or no effect on any of the outcomes. These results provide evidence for a hemispheric asymmetry of tDCS effects in patients with DOC. Left anodal tDCS might be more effective for modulating cortical excitability compared to tDCS on the right DLPFC. However, future studies with large sample sizes are needed to confirm these findings. This trial is registered with NCT03809936.

Excessive phase synchronization in cortical activation during locomotion in persons with Parkinson's disease

INTRODUCTION

Parkinson's disease (PD) is characterized by gait disturbances, which become severe during the advanced stages of the disease. Though gait impairments in Parkinson's disease have been extensively described in terms of spatiotemporal gait parameters, little is known regarding associated patterns of cortical activity. The objective of the present study is to test if interhemispheric synchronization differs between participants with PD and healthy elderly controls (NPD). We analyzed electroencephalography (EEG) signals recorded during bilateral movements, i.e., locomotion and hand tapping.

METHODS

Fifteen participants with PD ('OFF' their anti-parkinsonian medications) and eight NPD were assessed during quiet standing, straight-line walking, turning, and hand tapping tasks. Using a 32-electrode EEG array, we quantified the synchronization in periodic cortical activation between the brain hemispheres (interhemispheric phase synchronization; inter-PS). Theta, alpha, beta, and gamma bands were evaluated.

RESULTS

In all bands, inter-PS was significantly higher for the PD group as compared with the NPD group during standing and walking (p < 0.001) and during bimanual tasks (p = 0.026).

CONCLUSIONS

Persons with PD exhibit increased inter-PS as compared with NPD participants. These findings support previous evidence from animal studies, that bilateral cortical hypersynchronization emerges from the asymmetric neural degeneration that is at the base of the disease. Future studies should elucidate the long-term temporal development of this hypersynchronization and its clinical relevance (e.g., can it 'serve' as prodromal marker?).

Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods

Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50 min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.

The beta oscillation conditions in a simplified basal ganglia network

Parkinson's disease is a type of motor dysfunction disease that is induced mainly by abnormal interactions between the subthalamic nucleus (STN) and globus pallidus (GP) neurons. Periodic oscillatory activities with frequencies of 13-30 Hz are the main physiological characteristics of Parkinson's disease. In this paper, we built a class of STN-GP networks to explore beta oscillation conditions. A theoretical formula was obtained for generating oscillations without internal GP connections. Based on this formula, we studied the effects of cortex inputs, striatum inputs, coupling weights and delays on oscillation conditions, and the theoretical results are in good agreement with the numerical results. The onset mechanism can be explained by the model, and the internal GP connection has little effect on oscillations. Finally, we compared oscillation conditions with those in previous studies and found that the delays and coupling weights required for generating oscillations may decrease as the number of nuclei increases. We hope that the results obtained will inspire future theoretical and experimental studies.

Analysis of heart rate signals during meditation using visibility graph complexity

In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation. The simple process of calculating the GIC of VG and its adaptability to the chaotic nature of the biological signals can help in estimating the heart rate complexity in meditation.

EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations

OBJECTIVE

Spectral power analysis plays a predominant role in electroencephalogram-based emotional recognition. It can reflect activity differences among multiple brain regions. In addition to activation difference, different emotions also involve different large-scale network during related information processing. In this paper, both information propagation patterns and activation difference in the brain were fused to improve the performance of emotional recognition.

METHODS

We constructed emotion-related brain networks with phase locking value and adopted a multiple feature fusion approach to combine the compensative activation and connection information for emotion recognition.

RESULTS

Recognition results on three public emotional databases demonstrated that the combined features are superior to either single feature based on power distribution or network character. Furthermore, the conducted feature fusion analysis revealed the common characters between activation and connection patterns involved in the positive, neutral, and negative emotions for information processing.

SIGNIFICANCE

The proposed feasible combination of both information propagation patterns and activation difference in the brain is meaningful for developing the effective human-computer interaction systems by adapting to human emotions in the real world applications.

Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis

Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.

EEG spectral powers and source localization in depressing, sad, and fun music videos focusing on gender differences

Previously, gender-specific affective responses have been shown using neurophysiological signals. The present study intended to compare the differences in electroencephalographic (EEG) power spectra and EEG brain sources between men and women during the exposure of affective music video stimuli. The multi-channel EEG signals of 15 males and 15 females available in the database for emotion analysis using physiological signals were studied, while subjects were watching sad, depressing, and fun music videos. Seven EEG frequency bands were computed using average Fourier cross-spectral matrices. Then, standardized low-resolution electromagnetic tomography (sLORETA) was used to localize regions involved specifically in these emotional responses. To evaluate gender differences, independent sample t test was calculated for the sLORETA source powers. Our results showed that (1) the mean EEG power for all frequency bands in the women's group was significantly higher than that of the men's group; (2) spatial distribution differentiation between men and women was detected in all EEG frequency bands. (3) This difference has been related to the emotional stimuli, which was more evident for negative emotions. Taken together, our results showed that men and women recruited dissimilar brain networks for processing sad, depressing, and fun audio-visual stimuli.

Developmental trends of theta-beta interelectrode power correlation during resting state in normal children

The possibility that power-to-power (theta-beta) frequency coupling increases during development was analyzed. Three minutes of spontaneous EEG in an open eyes condition were recorded in a sample of 160 subjects ranging from 6 to 26 years old. Theta (4-7 Hz) and beta band (15-20 Hz) power was calculated in a trial-by-trial basis. Inter-electrode power correlations (IPC) were computed in each subject as the correlation between the power of two frequency bands recorded in two electrodes. An increase in theta-beta IPC with age was obtained. IPCs were higher when theta was seeded in posterior regions than in anterior or central regions. Moreover, the significant correlations between each individual IPC and age were calculated, making it possible to draw IPC versus age correlation maps in order to capture the IPC development topography. An increase was found in significant correlations in the left hemisphere compared to the right hemisphere. There were no differences in the inter-hemispheric versus intra-hemispheric IPC maturation spatial patterns. An increase in power-to-power-frequency coupling in theta-beta occurs during development, suggesting an increase in functional connectivity with age. Frequency coupling between theta and beta rhythms would be one of the mechanisms facilitating integration of long distance functional networks during development.