Human brain EEG indices of emotions: delineating responses to affective vocalizations by measuring frontal theta event-related synchronization

Height matters: An EEG study of the relative height disadvantage promoting third-party punishment

The study employed event-related potential (ERP), time-frequency analysis, and functional connectivity to comprehensively explore the influence of male's relative height on third-party punishment (TPP) and its underlying neural mechanism. The results found that punishment rate and transfer amount are significantly greater when the height of the third-party is lower than that of the recipient, suggesting that male's height disadvantage promotes TPP. Neural results found that the height disadvantage induced a smaller N1. The height disadvantage also evoked greater P300 amplitude, more theta power, and more alpha power. Furthermore, a significantly stronger wPLI between the rTPJ and the posterior parietal and a significantly stronger wPLI between the DLPFC and the posterior parietal were observed when third-party was at the height disadvantage. These results imply that the height disadvantage causes negative emotions and affects the fairness consideration in the early processing stage; the third-party evaluates the blame of violators and makes an appropriate punishment decision later. Our findings indicate that anger and reputation concern caused by height disadvantage promote TPP. The current study holds significance as it underscores the psychological importance of height in males, broadens the perspective on factors influencing TPP, validates the promoting effect of personal disadvantages on prosocial behavior, enriches our understanding of indirect reciprocity theory, and extends the application of the evolution theory of Napoleon complex.

Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning

Notification systems that convey urgency without adding cognitive burden are crucial in human-computer interaction. Haptic feedback systems, particularly those utilizing vibration feedback, have emerged as a compelling solution, capable of providing desirable levels of urgency depending on the application. High-risk applications require an evaluation of the urgency level elicited during critical notifications. Traditional evaluations of perceived urgency rely on subjective self-reporting and performance metrics, which, while useful, are not real-time and can be distracting from the task at hand. In contrast, EEG technology offers a direct, non-intrusive method of assessing the user's cognitive state. Leveraging deep learning, this study introduces a novel approach to evaluate perceived urgency from single-trial EEG data, induced by vibration stimuli on the upper body, utilizing our newly collected urgency-via-vibration dataset. The proposed model combines a 2D convolutional neural network with a temporal convolutional network to capture spatial and temporal EEG features, outperforming several established EEG models. The proposed model achieves an average classification accuracy of 83% through leave-one-subject-out cross-validation across three urgency classes (not urgent, urgent, and very urgent) from a single trial of EEG data. Furthermore, explainability analysis showed that the prefrontal brain region, followed by the central brain region, are the most influential in predicting the urgency level. A follow-up neural statistical analysis revealed an increase in event-related synchronization (ERS) in the theta frequency band (4-7 Hz) with the increased level of urgency, which is associated with high arousal and attention in the neuroscience literature. A limitation of this study is that the proposed model's performance was tested only the urgency-via-vibration dataset, which may affect the generalizability of the findings.

Physiologic Network-Based Brain-Heart Interaction Quantification During Visual Emotional Elicitation

In recent years, there has been a surge in interest regarding the intricate physiological interplay between the brain and the heart, particularly during emotional processing. This has led to the development of various signal processing techniques aimed at investigating Brain-Heart Interactions (BHI), reflecting a growing appreciation for their bidirectional communication and influence on each other. Our study contributes to this burgeoning field by adopting a network physiology approach, employing time-delay stability as a quantifiable metric to discern and measure the coupling strength between the brain and the heart, specifically during visual emotional elicitation. We extract and transform features from EEG and ECG signals into a 1 Hz format, facilitating the calculation of BHI coupling strength through stability analysis on their maximal cross-correlation. Notably, our investigation sheds light on the critical role played by low-frequency components in EEG, particularly in the δ , θ , and α bands, as essential mediators of information transmission during the complex processing of emotion-related stimuli by the brain. Furthermore, our analysis highlights the pivotal involvement of frontal pole regions, emphasizing the significance of δ - θ coupling in mediating emotional responses. Additionally, we observe significant arousal-dependent changes in the θ frequency band across different emotional states, particularly evident in the prefrontal cortex. By offering novel insights into the synchronized dynamics of cortical and heartbeat activities during emotional elicitation, our research enriches the expanding knowledge base in the field of neurophysiology and emotion research.

Multi-scale 3D-CRU for EEG emotion recognition

In this paper, we propose a novel multi-scale 3D-CRU model, with the goal of extracting more discriminative emotion feature from EEG signals. By concurrently exploiting the relative electrode locations and different frequency subbands of EEG signals, a three-dimensional feature representation is reconstructed wherein the Delta (δ) frequency pattern is included. We employ a multi-scale approach, termed 3D-CRU, to concurrently extract frequency and spatial features at varying levels of granularity within each time segment. In the proposed 3D-CRU, we introduce a multi-scale 3D Convolutional Neural Network (3D-CNN) to effectively capture discriminative information embedded within the 3D feature representation. To model the temporal dynamics across consecutive time segments, we incorporate a Gated Recurrent Unit (GRU) module to extract temporal representations from the time series of combined frequency-spatial features. Ultimately, the 3D-CRU model yields a global feature representation, encompassing comprehensive information across time, frequency, and spatial domains. Numerous experimental assessments conducted on publicly available DEAP and SEED databases provide empirical evidence supporting the enhanced performance of our proposed model in the domain of emotion recognition. These findings underscore the efficacy of the features extracted by the proposed multi-scale 3D-GRU model, particularly with the incorporation of the Delta (δ) frequency pattern. Specifically, on the DEAP dataset, the accuracy of Valence and Arousal are 93.12% and 94.31%, respectively, while on the SEED dataset, the accuracy is 92.25%.

Graph theoretical brain connectivity measures to investigate neural correlates of music rhythms associated with fear and anger

The present study tests the hypothesis that emotions of fear and anger are associated with distinct psychophysiological and neural circuitry according to discrete emotion model due to contrasting neurotransmitter activities, despite being included in the same affective group in many studies due to similar arousal-valance scores of them in emotion models. EEG data is downloaded from OpenNeuro platform with access number of ds002721. Brain connectivity estimations are obtained by using both functional and effective connectivity estimators in analysis of short (2 sec) and long (6 sec) EEG segments across the cortex. In tests, discrete emotions and resting-states are identified by frequency band specific brain network measures and then contrasting emotional states are deep classified with 5-fold cross-validated Long Short Term Memory Networks. Logistic regression modeling has also been examined to provide robust performance criteria. Commonly, the best results are obtained by using Partial Directed Coherence in Gamma (31.5 - 60.5 H z ) sub-bands of short EEG segments. In particular, Fear and Anger have been classified with accuracy of 91.79%. Thus, our hypothesis is supported by overall results. In conclusion, Anger is found to be characterized by increased transitivity and decreased local efficiency in addition to lower modularity in Gamma-band in comparison to fear. Local efficiency refers functional brain segregation originated from the ability of the brain to exchange information locally. Transitivity refer the overall probability for the brain having adjacent neural populations interconnected, thus revealing the existence of tightly connected cortical regions. Modularity quantifies how well the brain can be partitioned into functional cortical regions. In conclusion, PDC is proposed to graph theoretical analysis of short EEG epochs in presenting robust emotional indicators sensitive to perception of affective sounds.

Enhancing EEG-based attachment style prediction: unveiling the impact of feature domains

Introduction

Attachment styles are crucial in human relationships and have been explored through neurophysiological responses and EEG data analysis. This study investigates the potential of EEG data in predicting and differentiating secure and insecure attachment styles, contributing to the understanding of the neural basis of interpersonal dynamics.

Methods

We engaged 27 participants in our study, employing an XGBoost classifier to analyze EEG data across various feature domains, including time-domain, complexity-based, and frequency-based attributes.

Results

The study found significant differences in the precision of attachment style prediction: a high precision rate of 96.18% for predicting insecure attachment, and a lower precision of 55.34% for secure attachment. Balanced accuracy metrics indicated an overall model accuracy of approximately 84.14%, taking into account dataset imbalances.

Discussion

These results highlight the challenges in using EEG patterns for attachment style prediction due to the complex nature of attachment insecurities. Individuals with heightened perceived insecurity predominantly aligned with the insecure attachment category, suggesting a link to their increased emotional reactivity and sensitivity to social cues. The study underscores the importance of time-domain features in prediction accuracy, followed by complexity-based features, while noting the lesser impact of frequency-based features. Our findings advance the understanding of the neural correlates of attachment and pave the way for future research, including expanding demographic diversity and integrating multimodal data to refine predictive models.

Alpha-band oscillations and emotion: A review of studies on picture perception

Although alpha-band activity has long been a focus of psychophysiological research, its modulation by emotional value during picture perception has only recently been studied systematically. Here, we review these studies and report that the most consistent alpha oscillatory pattern indexing emotional processing is an enhanced desynchronization (ERD) over posterior sensors when viewing emotional compared with neutral pictures. This enhanced alpha ERD is not specific to unpleasant picture content, as previously proposed for other measures of affective response, but has also been observed for pleasant stimuli. Evidence suggests that this effect is not confined to the alpha band but that it also involves a desynchronization of the lower beta frequencies (8-20 Hz). The emotional modulation of alpha ERD occurs even after massive stimulus repetition and when emotional cues serve as task-irrelevant distractors, consistent with the hypothesis that evaluative processes are mandatory in emotional picture processing. A similar enhanced ERD has been observed for other significant cues (e.g., conditioned aversive stimuli, or in anticipation of a potential threat), suggesting that it reflects cortical excitability associated with the engagement of the motivational systems.

Real-Time EEG-Based Emotion Recognition

Most studies have demonstrated that EEG can be applied to emotion recognition. In the process of EEG-based emotion recognition, real-time is an important feature. In this paper, the real-time problem of emotion recognition based on EEG is explained and analyzed. Secondly, the short time window length and attention mechanisms are designed on EEG signals to follow emotion change over time. Then, long short-term memory with the additive attention mechanism is used for emotion recognition, due to timely emotion updates, and the model is applied to the SEED and SEED-IV datasets to verify the feasibility of real-time emotion recognition. The results show that the model performs relatively well in terms of real-time performance, with accuracy rates of 85.40% and 74.26% on SEED and SEED-IV, but the accuracy rate has not reached the ideal state due to data labeling and other losses in the pursuit of real-time performance.

Multimodal assessment of effects of urban environments on psychological wellbeing

The built environment is a ubiquitous factor of modern human life, which fundamentally affects human wellbeing. Most existing research on the psychological effects of urban environments is predominantly based on subjective self-report measures, which provide valuable insights into subjective experiences, but are also affected by conscious and subconscious bias. In the current study, we evaluate a multimodal approach to capturing wellbeing by employing objective mobile physiological and neurophysiological measurement technologies alongside self-reports to capture the impact of two different urban environments. Moreover, we endeavoured to comprehensively measure and, when possible, control the physical aspects of the environment. Our study specifically aimed to discover differences between psychological wellbeing indicators in adults across low-density vs. moderate-density urban environments. Data collection took place in two urban outdoor locations in Australia. Statistical comparison of the two locations indicated that low urban density led to comparatively higher levels of psychological wellbeing compared to moderate urban density. Self-reports indicated that the low-density environment led to higher levels of subjective feelings of comfort and safety and reduced levels of negative mood. In line with the subjective reports, in the low-density environment compared to the moderate-density environment, individuals showed higher levels of EEG theta activity, while EEG beta and heart rate measures were lower. The research outcome provides insights into how urban density affects people's wellbeing and showcases the benefits of employing ecologically-valid multimodal psychological-environmental measurement approaches to effectively evaluate the psychological impacts of built environments.

Dynamic fluctuations in ascending heart-to-brain communication under mental stress

Dynamical information exchange between central and autonomic nervous systems, as referred to functional brain-heart interplay, occurs during emotional and physical arousal. It is well documented that physical and mental stress lead to sympathetic activation. Nevertheless, the role of autonomic inputs in nervous system-wise communication under mental stress is yet unknown. In this study, we estimated the causal and bidirectional neural modulations between electroencephalogram (EEG) oscillations and peripheral sympathetic and parasympathetic activities using a recently proposed computational framework for a functional brain-heart interplay assessment, namely the sympathovagal synthetic data generation model. Mental stress was elicited in 37 healthy volunteers by increasing their cognitive demands throughout three tasks associated with increased stress levels. Stress elicitation induced an increased variability in sympathovagal markers, as well as increased variability in the directional brain-heart interplay. The observed heart-to-brain interplay was primarily from sympathetic activity targeting a wide range of EEG oscillations, whereas variability in the efferent direction seemed mainly related to EEG oscillations in the γ band. These findings extend current knowledge on stress physiology, which mainly referred to top-down neural dynamics. Our results suggest that mental stress may not cause an increase in sympathetic activity exclusively as it initiates a dynamic fluctuation within brain-body networks including bidirectional interactions at a brain-heart level. We conclude that directional brain-heart interplay measurements may provide suitable biomarkers for a quantitative stress assessment and bodily feedback may modulate the perceived stress caused by increased cognitive demand.

Concurrent TMS-EEG and EEG reveal neuroplastic and oscillatory changes associated with self-compassion and negative emotions

Background/Objective

Self-compassion has a consensual relevance for overall mental health, but its mechanisms remain unknown. Using intermittent theta burst stimulation (iTBS) and concurrent transcranial magnetic stimulation-electroencephalography (TMS-EEG), this study investigated the causal relationship of the dorsolateral prefrontal cortex (DLPFC) with self-compassion and explored the changes in neuroplasticity and neural dynamics.

Method

Thirty-two healthy participants received iTBS or sham stimulation over the DLPFC, before and after which they were instructed to either use self-compassionate strategies or to be rejected in the context of social rejection and to report the level of self-compassion or negative affect. TMS-evoked potentials were evaluated as novel neuroplastic techniques with N45, P60, N100, and P180.

Results

iTBS uniquely decreased P180 amplitude measured with TMS-EEG whereby sham stimulation had no effect on neuroplasticity. In line with neuroplasticity changes, iTBS enhanced a widespread gamma band power and coherence, which correlated consistently with increased engagement in self-compassion. Meanwhile, iTBS demonstrated opposite effects on theta activity dependent on the social contexts whereby self-compassion decreased and social rejection enhanced it respectively. This unique effect of iTBS on theta activity was also supplemented by the enhancement of theta band coherence following iTBS.

Conclusions

We found a causal relationship between DLPFC and self-compassion. We also provide evidence to indicate widespread gamma activity and connectivity to correlate with self-compassion as well as the critical role of the DLPFC in modulating theta activity and negative emotions.

Dysfunction of Emotion Regulation in Mild Cognitive Impairment Individuals Combined With Depressive Disorder: A Neural Mechanism Study

Depression increases the risk of progression from mild cognitive impairment (MCI) to dementia, where impaired emotion regulation is a core symptom of depression. However, the neural mechanisms underlying the decreased emotion regulation in individuals with MCI combined with depressive symptoms are not precise. We assessed the behavioral performance by emotion regulation tasks and recorded event-related electroencephalography (EEG) signals related to emotion regulation tasks simultaneously. EEG analysis, including event-related potential (ERP), event-related spectral perturbation (ERSP), functional connectivity and graph theory, was used to compare the difference between MCI individuals and MCI depressed individuals in behavioral performance, the late positive potential (LPP) amplitudes, neural oscillations and brain networks during the processing of emotional stimuli. We found that MCI depressed individuals have negative preferences and are prone to allocate more attentional resources to negative stimuli. Results suggested that theta and alpha oscillations activity is increased, and gamma oscillations activity is decreased during negative stimulus processing in MCI depressed individuals, thus indicating that the decreased emotion regulation in MCI depressed individuals may be associated with enhanced low-frequency and decreased high-frequency oscillations activity. Functional connectivity analysis revealed a decrease in functional connectivity in the left cerebral hemisphere of the alpha band and an increase in functional connectivity in the right cerebral hemisphere of the alpha band in MCI depressed individuals. Graph theory analysis suggested that global network metrics, including clustering coefficients and disassortative, decreased, while nodal and modular network metrics regarding local nodal efficiency, degree centrality, and betweenness centrality were significantly increased in the frontal lobe and decreased in the parieto-occipital lobe, which was observed in the alpha band, further suggesting that abnormal alpha band network connectivity may be a potential marker of depressive symptoms. Correlational analyses showed that depressive symptoms were closely related to emotion regulation, power oscillations and functional connectivity. In conclusion, the dominant processing of negative stimuli, the increased low-frequency oscillations activity and decreased high-frequency activity, so as the decrease in top-down information processing in the frontal parieto-occipital lobe, results in the abnormality of alpha-band network connectivity. It is suggested that these factors, in turn, contribute to the declined ability of MCI depressed individuals in emotion regulation.

Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network

The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals. First, we use filtering and Euclidean alignment for data preprocessing. In the feature extraction, we use short-time Fourier transform and Hilbert-Huang transform to extract time-frequency features, and convolutional neural networks to extract spatial features. Finally, bi-directional long short-term memory explored the timing relationship. Before performing the convolution operation, according to the unique topology of the EEG channel, the EEG features are converted into 3D tensors. This study has achieved good results on two emotion databases: SEED and Emotional BCI of 2020 WORLD ROBOT COMPETITION. We applied this method to the recognition of depression based on EEG and achieved a recognition rate of more than 70% under the five-fold cross-validation. In addition, the subject-independent protocol on SEED data has achieved a state-of-the-art recognition rate, which exceeds the existing research methods. We propose a novel EEG emotion recognition framework for depression detection, which provides a robust algorithm for real-time clinical depression detection based on EEG.

Social appropriateness perception of dynamic interactions

The current study explored the judgement of communicative appropriateness while processing a dialogue between two individuals. All stimuli were presented as audio-visual as well as audio-only vignettes and 24 young adults reported their social impression (appropriateness) of literal, blunt, sarcastic, and teasing statements. On average, teasing statements were rated as more appropriate when processing audiovisual statements compared to the audio-only version of a stimuli, while sarcastic statements were judged as less appropriate with additional visual information. These results indicate a rejection of the Tinge Hypothesis for audio-visual vignettes while confirming it for the reduced, audio-only counterparts. We also analyzed time-frequency EEG data of four frequency bands that have been related to language processing: alpha, beta, theta and low gamma. We found desynchronization in the alpha band literal versus nonliteral items, confirming the assumption that the alpha band reflects stimulus complexity. The analysis also revealed a power increase in the theta, beta and low gamma band, especially when comparing blunt and nonliteral statements in the audio-only condition. The time-frequency results corroborate the prominent role of the alpha and theta bands in language processing and offer new insights into the neural correlates of communicative appropriateness and social aspects of speech perception.

Musical components important for the Mozart K448 effect in epilepsy

There is growing evidence for the efficacy of music, specifically Mozart’s Sonata for Two Pianos in D Major (K448), at reducing ictal and interictal epileptiform activity. Nonetheless, little is known about the mechanism underlying this beneficial “Mozart K448 effect” for persons with epilepsy. Here, we measured the influence that K448 had on intracranial interictal epileptiform discharges (IEDs) in sixteen subjects undergoing intracranial monitoring for refractory focal epilepsy. We found reduced IEDs during the original version of K448 after at least 30-s of exposure. Nonsignificant IED rate reductions were witnessed in all brain regions apart from the bilateral frontal cortices, where we observed increased frontal theta power during transitions from prolonged musical segments. All other presented musical stimuli were associated with nonsignificant IED alterations. These results suggest that the “Mozart K448 effect” is dependent on the duration of exposure and may preferentially modulate activity in frontal emotional networks, providing insight into the mechanism underlying this response. Our findings encourage the continued evaluation of Mozart’s K448 as a noninvasive, non-pharmacological intervention for refractory epilepsy.

Electrophysiological Studies of Cognitive Reappraisal Success and Failure in aMCI

BACKGROUND

Although successful reappraisal relies on cognitive resources, how cognitive impairment affects brain processes related to cognitive reappraisal is not yet clear.

METHODS

Forty-four amnestic mild cognitive impairment (aMCI) subjects and 72 healthy elderly controls (HECs) were divided into the MCI-Failure (n = 23), MCI-Success (n = 21), HEC-Failure (n = 26), and HEC-Success (n = 46) groups according to changes in self-reported affect using reappraisal. All participants viewed 30 negative and 30 neutral images preceded by straightforward descriptions of these images and 30 negative images preceded by more neutral descriptions.

RESULTS

Reappraisal failure was found to be more common in people with MCI. Reappraisal failure is associated with altered neurophysiological indices of negative-reappraisal stimuli processing that are reflected in smaller theta responsivity to negative-reappraisal stimuli between 350-550 ms. The MCI-Success group showed enhanced LPP for negative-reappraisal stimuli from 1200 to 3500 ms, reflecting compensatory effort to complete the reappraisal task, while subjects in other groups showed reduced LPP for negative-reappraisal stimuli from 550 to 1200 ms.

CONCLUSIONS

These findings deepen our understanding of how cognitive decline impacts reappraisal and informs early diagnosis and interventions for MCI.

A novel beamformer-based imaging of phase-amplitude coupling (BIPAC) unveiling the inter-regional connectivity of emotional prosody processing in women with primary dysmenorrhea

Objective. Neural communication or the interactions of brain regions play a key role in the formation of functional neural networks. A type of neural communication can be measured in the form of phase-amplitude coupling (PAC), which is the coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations. This paper presents a beamformer-based imaging method, beamformer-based imaging of PAC (BIPAC), to quantify the strength of PAC between a seed region and other brain regions.Approach. A dipole is used to model the ensemble of neural activity within a group of nearby neurons and represents a mixture of multiple source components of cortical activity. From ensemble activity at each brain location, the source component with the strongest coupling to the seed activity is extracted, while unrelated components are suppressed to enhance the sensitivity of coupled-source estimation.Main results. In evaluations using simulation data sets, BIPAC proved advantageous with regard to estimation accuracy in source localization, orientation, and coupling strength. BIPAC was also applied to the analysis of magnetoencephalographic signals recorded from women with primary dysmenorrhea in an implicit emotional prosody experiment. In response to negative emotional prosody, auditory areas revealed strong PAC with the ventral auditory stream and occipitoparietal areas in the theta-gamma and alpha-gamma bands, which may respectively indicate the recruitment of auditory sensory memory and attention reorientation. Moreover, patients with more severe pain experience appeared to have stronger coupling between auditory areas and temporoparietal regions.Significance. Our findings indicate that the implicit processing of emotional prosody is altered by menstrual pain experience. The proposed BIPAC is feasible and applicable to imaging inter-regional connectivity based on cross-frequency coupling estimates. The experimental results also demonstrate that BIPAC is capable of revealing autonomous brain processing and neurodynamics, which are more subtle than active and attended task-driven processing.

Children's shyness and neural responses to social exclusion: Patterns of midfrontal theta power usually not observed until adolescence

Adverse peer experiences, such as social exclusion, are known risks for socioemotional problems among shy youth. Yet, little is known about how shy children and adolescents process social exclusion in the brain and whether these responses are amplified in adolescence. Using the Cyberball task, we examined mediofrontal theta (4-7 Hz) event-related EEG spectral power during conditions of fair play and social exclusion in 122 participants (58 children, ages 10-12 years, and 64 adolescents, ages 14-17 years). Age effects of the task showed that adolescents displayed heightened theta power to both outright rejection and baseline "not my turn" events, whereas children showed higher theta power to rejection compared with "not my turn" events. Further results on individual differences showed that children with relatively higher levels of shyness displayed enhanced theta power to both rejection and "not my turn" events-a pattern that also was observed in adolescents. These findings suggest that a pattern of heightened neural sensitivity to both outright social exclusion and threats of exclusion, which is the norm by adolescence, also is observed in children with higher levels of shyness. The similar neural response pattern might be driven by salient social motivations that similarly modify the social cognition and behaviors of these groups and might reflect neural antecedents of rejection sensitivity.

Comparison of Smoothing Filters' Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain-Computer Interface Headset during Audio Stimulation

Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky-Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.

Oscillatory EEG Signatures of Affective Processes during Interaction with Adaptive Computer Systems

Affect monitoring is being discussed as a novel strategy to make adaptive systems more user-oriented. Basic knowledge about oscillatory processes and functional connectivity underlying affect during naturalistic human-computer interactions (HCI) is, however, scarce. This study assessed local oscillatory power entrainment and distributed functional connectivity in a close-to-naturalistic HCI-paradigm. Sixteen participants interacted with a simulated assistance system which deliberately evoked positive (supporting goal-achievement) and negative (impeding goal-achievement) affective reactions. Electroencephalography (EEG) was used to examine the reactivity of the cortical system during the interaction by studying both event-related (de-)synchronization (ERD/ERS) and event-related functional coupling of cortical networks towards system-initiated assistance. Significantly higher α-band and β-band ERD in centro-parietal and parieto-occipital regions and β-band ERD in bi-lateral fronto-central regions were observed during impeding system behavior. Supportive system behavior activated significantly higher γ-band ERS in bi-hemispheric parietal-occipital regions. This was accompanied by functional coupling of remote β-band and γ-band activity in the medial frontal, left fronto-central and parietal regions, respectively. Our findings identify oscillatory signatures of positive and negative affective processes as reactions to system-initiated assistance. The findings contribute to the development of EEG-based neuroadaptive assistance loops by suggesting a non-obtrusive method for monitoring affect in HCI.

Brain Activity during Different Throwing Games: EEG Exploratory Study

The purpose of this study is to explore the differences in brain activity in various types of throwing games by making encephalographic records. Three conditions of throwing games were compared looking for significant differences (simple throwing, throwing to a goal, and simultaneous throwing with another player). After signal processing, power spectral densities were compared through variance analysis (p ≤ 0.001). Significant differences were found especially in high-beta oscillations (22-30 Hz). "Goal" and "Simultaneous" throwing conditions show significantly higher values than those shown for throws without opponent. This can be explained by the higher demand for motor control and the higher arousal in competition situations. On the other hand, the high-beta records of the "Goal" condition are significantly higher than those of the "Simultaneous" throwing, which could be understood from the association of the beta waves with decision-making processes. These results support the difference in brain activity during similar games. This has several implications: opening up a path to study the effects of each specific game on brain activity and calling into question the transfer of research findings on animal play to all types of human play.

Emotional Networked maps from EEG signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:34-37. [PMID: 33017924 DOI: 10.1109/embc44109.2020.9175935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The EEG has showed that contains relevant information about recognition of emotional states. It is important to analyze the EEG signals to understand the emotional states not only from a time series approach but also determining the importance of the generating process of these signals, the location of electrodes and the relationship between the EEG signals. From the EEG signals of each emotional state, a functional connectivity measurement was used to construct adjacency matrices: lagged phase synchronization (LPS), averaging adjacency matrices we built a prototype network for each emotion. Based on these networks, we extracted a set node features seeking to understand their behavior and the relationship between them. We found through the strength and degree, the group of representative electrodes for each emotional state, finding differences from intensity of measurement and the spatial location of these electrodes. In addition, analyzing the cluster coefficient, degree, and strength, we find differences between the networks from the spatial patterns associated with the electrodes with the highest coefficient. This analysis can also gain evidence from the connectivity elements shared between emotional states, allowing to cluster emotions and concluding about the relationship of emotions from EEG perspective.

Imagery-induced negative affect, social touch and frontal EEG power band activity

Social touch seems to modulate emotions, but its brain correlates are poorly understood. Here, we investigated if frontal power band activity in the electroencephalogram (EEG) during aversive mental imagery is modulated by social touch from one's romantic partner and a stranger. We observed the highest theta and beta power when imaging alone, next so when being touched by a stranger, with lowest theta and beta activity during holding hands with the loved one. Delta power was higher when being alone than with a stranger or a partner, with no difference between the two. Gamma power was highest during the stranger condition and lower both when being alone and with the partner, while alpha power did not change as a function of social touch. Theta power displayed a positive correlation with electrodermal activity supporting its relation to emotional arousal. Attachment style modulated the effect of touch on the EEG as only secure but not insecure partner bonding was associated with theta power reductions. Because theta power was sensitive to the experimental perturbations, mapped onto peripheral physiological arousal and reflected partner attachment style we suggest that frontal theta power might serve as an EEG derived bio-marker for social touch in emotionally significant dyads.

Sex differences in high-level appreciation of automobile design-evoked gamma broadband synchronization

The present study was conducted to provide neuroimaging correlates for neurodesign of automobile for marketing aesthetics, using event-related spectral perturbations (ERSPs) and participant reports. Thirty men and women aged 22-27 years were presented with various 3-dimensional automobile modelling shapes (rectangular, streamlined, and round), which were cross-matched with various interior colour tones (pure hue/vivid, light, and dark tones) in the experimental conditions, i.e., rectangular exterior with a vivid tone interior. The stimuli pairs were to be rated by participants to facilitate our understanding of the emotional dimensions of automotive design qualities. Significant differences were observed in the high gamma band of 80-100 Hz in the left temporal area between the two sexes. Men elicited a stronger high gamma band signals for dark colour tone interiors and rectangular or round automobile modelling designs because of the meaningful and comprehensible signals associated with the mechanisms of working memory. In contrast, women had fewer reactions than men, and elicited higher beta-band dynamics in the anterior cingulate cortex for rectangular automobile modelling design, and higher gamma-band dynamics for light colour tone interiors, which might relate to their higher self-awareness of positive emotional reward.

The Influence of Listening to Music on Adults with Left-behind Experience Revealed by EEG-based Connectivity

The human brain has a close relationship with music. Music-induced structural and functional brain changes have been demonstrated in the healthy adult. In the present study, adults with left-behind experience (ALB) were divided into two groups. The experimental group (ALB-E) took part in the music therapy experiment with three stages, including before listening to music (pre-stage), initially listening to music (mid-stage) and after listening to music (post-stage). The control group (ALB-C) did not participate in music therapy. Scalp resting-state EEGs of ALB were recorded during the three stages. We found no significant frequency change in the ALB-C group. In the ALB-E group, only the theta power spectrum was significantly different at all stages. The topographical distributions of the theta power spectrum represented change in trends from the frontal regions to the occipital regions. The result of Granger causal analysis (GCA), based on theta frequency, showed a stronger information flow from the middle frontal gyrus to the middle temporal gyrus (MFG → MTG) in the left hemisphere at the pre-stage compared to the post-stage. Additionally, the experimental group showed a weaker information flow from inferior gyrus to superior temporal gyrus (IFG → STG) in the right hemisphere at post-test stage compared to the ALB-C group. Our results demonstrate that listening to music can play a positive role on improving negative feelings for individuals with left behind experience.

The temporal course of vicarious embarrassment: An electrophysiological study

Vicarious embarrassment as a social pain emotion has been studied from cognitive and neuroimaging perspectives. However, the dynamic time course of vicarious embarrassment remains unclear. We conducted an event-related potential (ERP) study to investigate the temporal dynamics of vicarious embarrassment and examine the role of perspective-taking in an emotional judgment task. The ERP results showed that vicarious embarrassment evoked more negative MFN (250-310 ms) and N400 (400-520 ms) components than the neutral condition over the frontal region. The time-frequency analysis results showed that, in the 200-1,600 ms time window, greater alpha power was elicited when participants imagined themselves rather than others in an awkward situation; in the 500-1,900 ms time window, for both groups, vicarious embarrassment involved greater theta oscillations than the neutral condition over the frontal region. These results extend previous findings that vicarious emotion involves mentalizing processes, and demonstrate that people first detect and evaluate the target's misconduct and negative outcomes and then experience the vicarious embarrassment.

Brainwaves Oscillations as a Potential Biomarker for Major Depression Disorder Risk

Major depressive disorder (MDD) is a multidimensional disorder that is characterized by the presence of alterations in mood, cognitive capacity, sensorimotor, and homeostatic functions. Given that about half of the patients diagnosed with MDD do not respond to the various current treatments, new techniques are being sought to predict not only the course of the disease but also the characteristics that differentiate responders from non-responders. Using the electroencephalogram, a noninvasive and inexpensive tool, most studies have proposed that patients with MDD have some lateralization in brain electrical activity, with alterations in alpha and theta rhythms being observed, which would be related to dysfunctions in emotional capacity such as the absence or presence of responses to the different existing treatments. These alterations help in the identification of subjects at high risk of suffering from depression, in the differentiation into responders and nonresponders to various therapies (pharmacological, electroconvulsive therapy, and so on), as well as to establish in which period of the disease the treatment will be more effective. Although the data are still inconclusive and more research is needed, these alpha and theta neurophysiological markers could support future clinical practice when it comes to establishing an early diagnosis and treating state disorders more successfully and accurately of mood disorders.

Functional Linear and Nonlinear Brain–Heart Interplay during Emotional Video Elicitation: A Maximum Information Coefficient Study

Brain and heart continuously interact through anatomical and biochemical connections. Although several brain regions are known to be involved in the autonomic control, the functional brain–heart interplay (BHI) during emotional processing is not fully characterized yet. To this aim, we investigate BHI during emotional elicitation in healthy subjects. The functional linear and nonlinear couplings are quantified using the maximum information coefficient calculated between time-varying electroencephalography (EEG) power spectra within the canonical bands (δ,θ,α,β and γ), and time-varying low-frequency and high-frequency powers from heartbeat dynamics. Experimental data were gathered from 30 healthy volunteers whose emotions were elicited through pleasant and unpleasant high-arousing videos. Results demonstrate that functional BHI increases during videos with respect to a resting state through EEG oscillations not including the γ band (>30 Hz). Functional linear coupling seems associated with a high-arousing positive elicitation, with preferred EEG oscillations in the θ band ([4,8) Hz) especially over the left-temporal and parietal cortices. Differential functional nonlinear coupling between emotional valence seems to mainly occur through EEG oscillations in the δ,θ,α bands and sympathovagal dynamics, as well as through δ,α,β oscillations and parasympathetic activity mainly over the right hemisphere. Functional BHI through δ and α oscillations over the prefrontal region seems primarily nonlinear. This study provides novel insights on synchronous heartbeat and cortical dynamics during emotional video elicitation, also suggesting that a nonlinear analysis is needed to fully characterize functional BHI.

Frontal EEG Asymmetry and Middle Line Power Difference in Discrete Emotions

A traditional model of emotion cannot explain the differences in brain activities between two discrete emotions that are similar in the valence-arousal coordinate space. The current study elicited two positive emotions (amusement and tenderness) and two negative emotions (anger and fear) that are similar in both valence and arousal dimensions to examine the differences in brain activities in these emotional states. Frontal electroencephalographic (EEG) asymmetry and midline power in three bands (theta, alpha and beta) were measured when participants watched affective film excerpts. Significant differences were detected between tenderness and amusement on FP1/FP2 theta asymmetry, F3/F4 theta and alpha asymmetry. Significant differences between anger and fear on FP1/FP2 theta asymmetry and F3/F4 alpha asymmetry were also observed. For midline power, midline theta power could distinguish two negative emotions, while midline alpha and beta power could effectively differentiate two positive emotions. Liking and dominance were also related to EEG features. Stepwise multiple linear regression results revealed that frontal alpha and theta asymmetry could predict the subjective feelings of two positive and two negative emotions in different patterns. The binary classification accuracy, which used EEG frontal asymmetry and midline power as features and support vector machine (SVM) as classifiers, was as high as 64.52% for tenderness and amusement and 78.79% for anger and fear. The classification accuracy was improved after adding these features to other features extracted across the scalp. These findings indicate that frontal EEG asymmetry and midline power might have the potential to recognize discrete emotions that are similar in the valence-arousal coordinate space.

Effect of 5-HTTLPR on current source density, connectivity, and topological properties of resting state EEG networks

The S allele of serotonin transporter gene (5-HTTLPR) has been found to increase the risk of depression and other mental health problems, but some evidence suggests that S-allele carriers outperform subjects carrying the long allele in an array of cognitive tasks. Evidence linking this polymorphism with individual variation in electrophysiological properties of resting state brain networks is very limited. This study investigated the effect of 5-HTTLPR polymorphism on EEG current source density, connectivity, and topological properties of resting state networks. We collected genetic and resting state EEG data in 113 Caucasians. As compared to L-homozygotes, S-allele carriers showed lower current source density and connectivity in most frequency bands in areas overlapping with the default mode and emotion regulation regions. The analysis of graph-theoretical measures showed that S-allele carriers, as compared to L-homozygotes, have less optimal topological properties of brain networks in theta, but more optimal in alpha band. This dissociation may reflect the predisposition to emotional disorders, which is inherent to S-allele carriers, and, on the other hand, their superior functioning in some cognitive domains.

Cooperate or not cooperate EEG, autonomic, and behavioral correlates of ineffective joint strategies

Introduction

The neural activity in response to ineffective joint actions was explored in the present study. Subjects involved in a cooperative but frustrating task (poor performance as manipulated by an external feedback) were required to cooperate (T1) during an attentional task in a way to synchronize their responses and obtain better outcomes.

Methods

We manipulated their strategies by providing false feedbacks (T2) signaling the incapacity to create a synergy, which was reinforced by a general negative evaluation halfway through the game. A control condition was provided (no cooperation required, T0) as well as a check for possible learning effect (time series analysis). The effects of the feedback in modulating subjects' behavioral performance and electrocortical activity were explored by means of brain oscillations (delta, theta, alpha, beta) and autonomic activity (heart rate, HR; skin conductance activity, SCR).

Results

Results showed a specific pattern of behavioral, neural, and peripheral responses after the social feedback. In fact, within this condition, worse behavioral outcomes emerged, with longer response times with respect to the prefeedback one. In parallel, a specific right-lateralized effect was observed over the dorsolateral prefrontal cortex (DLPFC), with increased delta and theta power compared to the previous condition. Moreover, increased SCR was observed with respect to the first part.

Conclusions

Two interpretations are put forward to explain the present findings: 1) the contribution of negative emotions in response to failing interactions or 2) a motivational disengagement toward goal-oriented cooperation elicited by frustrating evaluations.

Hearing, Emotion, Amplification, Research, and Training Workshop: Current Understanding of Hearing Loss and Emotion Perception and Priorities for Future Research

The question of how hearing loss and hearing rehabilitation affect patients' momentary emotional experiences is one that has received little attention but has considerable potential to affect patients' psychosocial function. This article is a product from the Hearing, Emotion, Amplification, Research, and Training workshop, which was convened to develop a consensus document describing research on emotion perception relevant for hearing research. This article outlines conceptual frameworks for the investigation of emotion in hearing research; available subjective, objective, neurophysiologic, and peripheral physiologic data acquisition research methods; the effects of age and hearing loss on emotion perception; potential rehabilitation strategies; priorities for future research; and implications for clinical audiologic rehabilitation. More broadly, this article aims to increase awareness about emotion perception research in audiology and to stimulate additional research on the topic.

Discrimination of emotional states from scalp- and intracranial EEG using multiscale Rényi entropy

A data-adaptive, multiscale version of Rényi's quadratic entropy (RQE) is introduced for emotional state discrimination from EEG recordings. The algorithm is applied to scalp EEG recordings of 30 participants watching 4 emotionally-charged video clips taken from a validated public database. Krippendorff's inter-rater statistic reveals that multiscale RQE of the mid-frontal scalp electrodes best discriminates between five emotional states. Multiscale RQE is also applied to joint scalp EEG, amygdala- and occipital pole intracranial recordings of an implanted patient watching a neutral and an emotionally charged video clip. Unlike for the neutral video clip, the RQEs of the mid-frontal scalp electrodes and the amygdala-implanted electrodes are observed to coincide in the time range where the crux of the emotionally-charged video clip is revealed. In addition, also during this time range, phase synchrony between the amygdala and mid-frontal recordings is maximal, as well as our 30 participants' inter-rater agreement on the same video clip. A source reconstruction exercise using intracranial recordings supports our assertion that amygdala could contribute to mid-frontal scalp EEG. On the contrary, no such contribution was observed for the occipital pole's intracranial recordings. Our results suggest that emotional states discriminated from mid-frontal scalp EEG are likely to be mirrored by differences in amygdala activations in particular when recorded in response to emotionally-charged scenes.

Why to cooperate is better than to compete: brain and personality components

Background

Cooperation and competition were compared in the present study. Brain correlates (electroencephalography, EEG frequency band, delta, theta, alpha, and beta) and hemodynamic measure of functional near-infrared spectroscopy (fNIRS, O2Hb) were acquired during a joined cooperative (Experiment 1) or competitive (Experiment 2) task. Subjects were required to match each other’s cognitive performance (cooperation) or to make better than others (competition) in terms of accuracy (error rate, ER) and response time (RT). In addition, a personality trait measure (behavioral activation system, BAS) was used to distinguish subjects based on their rewarding attitude. Self-perception of social ranking and real performance were considered in response to subjects’ performance (that was artificially manipulated to show an increasing or decreasing profile during the task).

Results

An increased left prefrontal cortical (PFC) responsiveness was found for subjects who had higher BAS rating in case of both cooperation and competition conditions. Moreover, subjects with higher BAS ratings showed greater frontal left activity during the cooperative task. These subjects also concomitantly perceived an increasing in social ranking and improved their performance.

Conclusions

Present results demonstrated that some trait components (BAS) and cooperative condition induce a positive self-representation in term of ranking and a best way to perform the task, as underlined by self-perception and cognitive outcomes. Indeed the higher BAS trait proved to be related with the representation of higher social ranking and with the perception of improved cognitive outcomes, with also a significant increased left PFC activity in cooperative contexts.

Abnormal small-world brain functional networks in obsessive-compulsive disorder patients with poor insight

BACKGROUND

There are limited data on neurobiological correlates of poor insight in obsessive-compulsive disorder (OCD). This study explored whether specific changes occur in small-world network (SWN) properties in the brain functional network of OCD patients with poor insight.

METHOD

Resting-state electroencephalograms (EEGs) were recorded for 12 medication-free OCD patients with poor insight, 50 medication-free OCD patients with good insight, and 36 healthy controls.

RESULTS

Both of the OCD groups exhibited topological alterations in the brain functional network characterized by abnormal small-world parameters at the beta band. However, the alterations at the theta band only existed in the OCD patients with poor insight.

LIMITATIONS

A relatively small sample size. Subjects were naïve to medications and those with Axis I comorbidity were excluded, perhaps limiting generalizability.

CONCLUSIONS

Disrupted functional integrity at the beta bands of the brain functional network may be related to OCD, while disrupted functional integrity at the theta band may be associated with poor insight in OCD patients, thus this study might provide novel insight into our understanding of the pathophysiology of OCD.

Toward an analysis of emotion regulation in children using late positive potential

The ability of emotion regulation and emotional responses in children is a main component of emotional competence through the development process. We had employed electroencephalography (EEG) a well-known noninvasive method for recording of brain emotion signals in order to analyze the emotion regulations in children. The international affective picture system (IAPS) pictures dataset was used for selection of stimuli presentation. The stimulus presentation was commonly practiced to induce the emotional responses from human brain. Brain signals were recorded using EEG electrodes and analyzed after preprocessing. There are rare studies available for detection of emotion regulation in children using late positive potential (LPP) analysis. The event related potential (ERP) is well known for analysis in cognitive neuroscience that was used in this paper for analysis of LPP. In this paper, we proposed the LPP as a neural marker for physiatrists and neurophysiologists to detect the mood disruption in children. We employed 21 subjects in this investigation, which were aged from 12 to 14 years. The ERP was analyzed through stimulus lock strategy which includes 180 stimuli of four emotions (arousal-valence). Each stimulus time duration was 1.5 s following of 0.5 s of rest time. Results show the increased modulation of LPP amplitude under all brain regions.

Empathy in Negative and Positive Interpersonal Interactions. What is the Relationship Between Central (EEG, fNIRS) and Peripheral (Autonomic) Neurophysiological Responses?

Emotional empathy is crucial to understand how we respond to interpersonal positive or negative situations. In the present research, we aim at identifying the neural networks and the autonomic responsiveness underlying the human ability to perceive and empathize with others' emotions when positive (cooperative) or negative (uncooperative) interactions are observed. A multimethodological approach was adopted to elucidate the reciprocal interplay of autonomic (peripheral) and central (cortical) activities in empathic behavior. Electroencephalography (EEG, frequency band analysis) and hemodynamic (functional Near-Infrared Spectroscopy, fNIRS) activity were all recorded simultaneously with systemic skin conductance response (SCR) and heart rate (HR) measurements as potential biological markers of emotional empathy. Subjects were required to empathize in interpersonal interactions. As shown by fNIRS/EEG measures, negative situations elicited increased brain responses within the right prefrontal cortex (PFC), whereas positive situations elicited greater responses within the left PFC. Therefore, a relevant lateralization effect was induced by the specific valence (mainly for negative conditions) of the emotional interactions. Also, SCR was modulated by positive/negative conditions. Finally, EEG activity (mainly low-frequency theta and delta bands) intrinsically correlated with the cortical hemodynamic responsiveness, and they both predicted autonomic activity. The integrated central and autonomic measures better elucidated the significance of empathic behavior in interpersonal interactions.

Brain-heart linear and nonlinear dynamics during visual emotional elicitation in healthy subjects

This study investigates brain-heart dynamics during visual emotional elicitation in healthy subjects through linear and nonlinear coupling measures of EEG spectrogram and instantaneous heart rate estimates. To this extent, affective pictures including different combinations of arousal and valence levels, gathered from the International Affective Picture System, were administered to twenty-two healthy subjects. Time-varying maps of cortical activation were obtained through EEG spectral analysis, whereas the associated instantaneous heartbeat dynamics was estimated using inhomogeneous point-process linear models. Brain-Heart linear and nonlinear coupling was estimated through the Maximal Information Coefficient (MIC), considering EEG time-varying spectra and point-process estimates defined in the time and frequency domains. As a proof of concept, we here show preliminary results considering EEG oscillations in the θ band (4-8 Hz). This band, indeed, is known in the literature to be involved in emotional processes. MIC highlighted significant arousal-dependent changes, mediated by the prefrontal cortex interplay especially occurring at intermediate arousing levels. Furthermore, lower and higher arousing elicitations were associated to not significant brain-heart coupling changes in response to pleasant/unpleasant elicitations.

Towards Building a Computer Aided Education System for Special Students Using Wearable Sensor Technologies

Human computer interaction is a growing field in terms of helping people in their daily life to improve their living. Especially, people with some disability may need an interface which is more appropriate and compatible with their needs. Our research is focused on similar kinds of problems, such as students with some mental disorder or mood disruption problems. To improve their learning process, an intelligent emotion recognition system is essential which has an ability to recognize the current emotional state of the brain. Nowadays, in special schools, instructors are commonly use some conventional methods for managing special students for educational purposes. In this paper, we proposed a novel computer aided method for instructors at special schools where they can teach special students with the support of our system using wearable technologies.

Emotional Modulation of Conflict Processing in the Affective Domain: Evidence from Event-related Potentials and Event-related Spectral Perturbation Analysis

Previous studies have revealed the impact of emotion on conflict processing. The present study was conducted to investigate whether cognitive control in the affective domain is also affected by emotion. Emotional face-word and body-word Stroop tasks were explored and contrasted, and both behavioural and electrophysiological measures were recorded. Behavioural results showed that both tasks replicated previous robust interference effects. At the physiological level, the two tasks showed dissociable neural activity in the early attention and perception stages. It was also found that the face-word task evoked more pronounced N1 and P2 amplitudes than the body-word task. However, the two tasks evoked comparable N450 amplitudes. At later processing stages, positive slow potentials were modulated by target emotion and congruency. In addition, time-frequency analyses also revealed that the face-word task induced enhanced theta activity compared to the body-word task at both early and later stages of processing. The present findings provide support for the dual competition framework and suggest the dynamic modulation of emotion on cognitive control in the affective domain.

The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication

Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples frequency bands supports a predictive coding model of multisensory emotion perception in which emotional facial and body expressions facilitate the processing of emotional vocalizations.

Combining electroencephalographic activity and instantaneous heart rate for assessing brain-heart dynamics during visual emotional elicitation in healthy subjects

Emotion perception, occurring in brain areas such as the prefrontal cortex and amygdala, involves autonomic responses affecting cardiovascular dynamics. However, how such brain-heart dynamics is further modulated by emotional valence (pleasantness/unpleasantness), also considering different arousing levels (the intensity of the emotional stimuli), is still unknown. To this extent, we combined electroencephalographic (EEG) dynamics and instantaneous heart rate estimates to study emotional processing in healthy subjects. Twenty-two healthy volunteers were elicited through affective pictures gathered from the International Affective Picture System. The experimental protocol foresaw 110 pictures, each of which lasted 10 s, associated to 25 different combinations of arousal and valence levels, including neutral elicitations. EEG data were processed using short-time Fourier transforms to obtain time-varying maps of cortical activation, whereas the associated instantaneous cardiovascular dynamics was estimated in the time and frequency domains through inhomogeneous point-process models. Brain-heart linear and nonlinear coupling was estimated through the maximal information coefficient (MIC). Considering EEG oscillations in theθband (4-8 Hz), MIC highlighted significant arousal-dependent changes between positive and negative stimuli, especially occurring at intermediate arousing levels through the prefrontal cortex interplay. Moreover, high arousing elicitations seem to mitigate changes in brain-heart dynamics in response to pleasant/unpleasant visual elicitation.

EEG Resting State Functional Connectivity Analysis in Children with Benign Epilepsy with Centrotemporal Spikes

In this study, we investigated changes in functional connectivity (FC) of the brain networks in patients with benign epilepsy with centrotemporal spikes (BECTS) compared to healthy controls using high-density EEG data collected under eyes-closed resting state condition. EEG source reconstruction was performed with exact Low Resolution Electromagnetic Tomography (eLORETA). We investigated FC between 84 Brodmann areas using lagged phase synchronization (LPS) in four frequency bands (δ, θ, α, and β). We further computed the network degree, clustering coefficient and efficiency. Compared to controls, patients displayed higher θ and α and lower β LPS values. In these frequency bands, patients were also characterized by less well ordered brain networks exhibiting higher global degrees and efficiencies and lower clustering coefficients. In the β band, patients exhibited reduced functional segregation and integration due to loss of both local and long-distance functional connections. These findings suggest that benign epileptic brain networks might be functionally disrupted due to their altered functional organization especially in the α and β frequency bands.

Hemodynamic (fNIRS) and EEG (N200) correlates of emotional inter-species interactions modulated by visual and auditory stimulation

The brain activity, considered in its hemodynamic (optical imaging: functional Near-Infrared Spectroscopy, fNIRS) and electrophysiological components (event-related potentials, ERPs, N200) was monitored when subjects observed (visual stimulation, V) or observed and heard (visual + auditory stimulation, VU) situations which represented inter-species (human-animal) interactions, with an emotional positive (cooperative) or negative (uncooperative) content. In addition, the cortical lateralization effect (more left or right dorsolateral prefrontal cortex, DLPFC) was explored. Both ERP and fNIRS showed significant effects due to emotional interactions which were discussed at light of cross-modal integration effects. The significance of inter-species effect for the emotional behavior was considered. In addition, hemodynamic and EEG consonant results and their value as integrated measures were discussed at light of valence effect.

Positive Emotional Experience: Induced by Vibroacoustic Stimulation Using a Body Monochord in Patients with Psychosomatic Disorders: Is Associated with an Increase in EEG-Theta and a Decrease in EEG-Alpha Power

Relaxation and meditation techniques are generally characterized by focusing attention, which is associated with an increase of frontal EEG Theta. Some studies on music perception suggest an activation of Frontal Midline Theta during emotionally positive attribution, others display a lateralization of electrocortical processes in the attribution of music induced emotion of different valence. The present study examined the effects of vibroacoustic stimulation using a Body Monochord and the conventional relaxation music from an audio CD on the spontaneous EEG of patients suffering from psychosomatic disorders (N = 60). Each treatment took about 20 min and was presented to the patients in random order. Subjective experience was recorded via self-rating scale. EEG power spectra of the Theta, Alpha-1 and Alpha-2 bands were analysed and compard between the two treatment conditions. There was no lateralization of electrocortical activity in terms of the emotional experience of the musical pieces. A reduction in Alpha-2 power occurred during both treatments. An emotionally positive attribution of the experience of the vibroacoustically induced relaxation state is characterized by a more pronounced release of control. In the context of focused attention this is interpreted as flow experience. The spontaneous EEG showed an increase in Theta power, particularly in the frontal medial and central medial area, and a greater reduction in Alpha-2 power. The intensity of positive emotional feelings during the CD music showed no significant effect on the increase in Theta power.