Time-dependent cortical asymmetries induced by emotional arousal: EEG analysis of event-related synchronization and desynchronization in individually defined frequency bands

Oscillatory brain dynamics underlying affective face processing

Facial expressions are ubiquitous and highly reliable social cues. Decades of research has shown that affective faces undergo facilitated processing across a distributed brain network. However, few studies have examined the multispectral brain dynamics underlying affective face processing, which is surprising given the multiple brain regions and rapid temporal dynamics thought to be involved. Herein, we used magnetoencephalography to derive dynamic functional maps of angry, neutral, and happy face processing in healthy adults. We found stronger theta oscillations shortly after the onset of affective relative to neutral faces (0-250 ms), within distributed ventral visual and parietal cortices, and the anterior hippocampus. Early gamma oscillations (100-275 ms) were strongest for angry faces in the inferior parietal lobule, temporoparietal junction, and presupplementary motor cortex. Finally, beta oscillations (175-575 ms) were stronger for neutral relative to affective expressions in the middle occipital and fusiform cortex. These results are consistent with the literature in regard to the critical brain regions, and delineate a distributed network where multispectral oscillatory dynamics support affective face processing through the rapid merging of low-level visual inputs to interpret the emotional meaning of each facial expression.

Can guilt enhance sensitivity to other's suffering? An EEG investigation into moral emotions and pain empathy

As a unique form of empathy, pain empathy often has a close relationship with society and morality. Research has revealed that moral emotions can influence pain empathy. The underlying physiological mechanism still needs to be further examined to understand how moral emotions affect pain empathy. This study employs EEG and Machine Learning techniques, using a painful image induction paradigm to explore the impact of moral emotion (guilt)-on pain empathy and its neural mechanisms. Participants without pain sensation were instructed to observe and evaluate pictures of an anonymous hand in painful or non-painful pictures under feelings of guilt or neutral emotion. Results found slower reaction times and higher pain ratings for painful pictures. Furthermore, guilt led to higher pain ratings. Under conditions of painful pictures, guilt-induced greater P3(350-450ms) amplitudes and higher α oscillations and enhanced the functional connectivity between the prefrontal cortex, the central frontal region, and the parieto-occipital lobe. K-nearest neighbor can effectively classify high and low-pain empathy under guilt emotion. The result showed that guilt promotes the brain's processing of painful picture, causing individuals to pay high attention and engage in deep cognitive processing. This study provides insights into enhancing empathy and fostering interpersonal relationships.

Investigating the Spatio-Temporal Signatures of Language Control-Related Brain Synchronization Processes

Language control processes allow for the flexible manipulation and access to context-appropriate verbal representations. Functional magnetic resonance imaging (fMRI) studies have localized the brain regions involved in language control processes usually by comparing high vs. low lexical-semantic control conditions during verbal tasks. Yet, the spectro-temporal dynamics of associated brain processes remain unexplored, preventing a proper understanding of the neural bases of language control mechanisms. To do so, we recorded functional brain activity using magnetoencephalography (MEG) and fMRI, while 30 healthy participants performed a silent verb generation (VGEN) and a picture naming (PN) task upon confrontation with pictures requiring low or high lexical-semantic control processes. fMRI confirmed the association between stronger language control processes and increased left inferior frontal gyrus (IFG) perfusion, while MEG revealed these controlled mechanisms to be associated with a specific sequence of early (< 500 ms) and late (> 500 ms) beta-band (de)synchronization processes within fronto-temporo-parietal areas. Particularly, beta-band modulations of event-related (de)synchronization mechanisms were first observed in the right IFG, followed by bilateral IFG and temporo-parietal brain regions. Altogether, these results suggest that beyond a specific recruitment of inferior frontal brain regions, language control mechanisms rely on a complex temporal sequence of beta-band oscillatory mechanisms over antero-posterior areas.

Stable construction and analysis of MDD modular networks based on multi-center EEG data

BACKGROUND

The modular structure can reflect the activity pattern of the brain, and exploring it may help us understand the pathogenesis of major depressive disorder (MDD). However, little is known about how to build a stable modular structure in MDD patients and how modules are separated and integrated.

METHOD

We used four independent resting state Electroencephalography (EEG) datasets. Different coupling methods, window lengths, and optimized community detection algorithms were used to find a reliable and robust modular structure, and the module differences of MDD were analyzed from the perspectives of global module attributes and local topology in multiple frequency bands.

RESULTS

The combination of the Phase Lag Index (PLI) and the Louvain algorithm can achieve better results and can achieve stability at smaller window lengths. Compared with Healthy Controls (HC), MDD had higher Modularity (Q) values and the number of modules in low-frequency bands. In addition, MDD showed significant structural changes in the frontal and parietal-occipital lobes, which were confirmed by further correlation analysis.

CONCLUSION

Our results provided a reliable validation of the modular structure construction method in MDD patients and contributed strong evidence for the changes in emotional cognition and visual system function in MDD patients from a new perspective. These results would afford valuable insights for further exploration of the pathogenesis of MDD.

Anxious/depressed individuals exhibit disrupted frontotemporal synchrony during cognitive conflict encoding

Anxiety and depressive disorders are associated with cognitive control deficits, yet their underlying neural mechanisms remain poorly understood. Here, we used high-resolution stereotactic EEG (sEEG) to determine how anxiety and/or depression modulates neural and behavioral responses when cognitive control is engaged in individuals with medically refractory epilepsy undergoing sEEG monitoring for surgical evaluation. We analyzed sEEG data recorded from frontotemporal regions of 29 participants (age range: 19-55, mean age: 35.5, female: 16/29) while they performed a Multi-Source Interference Task (MSIT) designed to elicit cognitive conflict. Neurobehavioral interviews, symptom rating scales, and clinical documentation were used to categorize participants as demonstrating anxiety and/or depression symptoms (A/D, n=13) or as epilepsy controls (EC, n=16). Generalized linear mixed-effects (GLME) models were used to analyze behavioral and neural data. Models of oscillatory power were used to identify brain regions within conflict-encoding networks in which coherence and phase locking values (PLV) were examined in A/D and EC. A/D participants demonstrated a greater conflict effect (response time slowing with higher cognitive load), without impairment in response time (RT) or accuracy compared to EC. A/D participants also showed significantly enhanced conflict-evoked theta (4-8Hz) and alpha (8-15Hz) power in the dorsolateral prefrontal cortex (dlPFC) and amygdala as well as widespread broadband activity in the lateral temporal lobe (LTL) compared to EC. Additionally, theta coherence and PLV between dlPFC-LTL and dlPFC-amygdala were reduced by conflict in A/D. Our findings suggest individuals with anxiety/depression symptoms exhibit heightened frontotemporal oscillatory activity and disrupted frontotemporal synchrony during cognitive conflict encoding, which may indicate a greater need for cognitive resources due to ineffective cognitive processing. These results highlight a potential role of frontotemporal circuits in conflict encoding that are altered in anxiety/depression, and may further inform future therapeutic interventions aimed at enhancing cognitive control in these populations.

The role of the somatosensory system in the feeling of emotions: a neurostimulation study

Emotional experiences deeply impact our bodily states, such as when we feel 'anger', our fists close and our face burns. Recent studies have shown that emotions can be mapped onto specific body areas, suggesting a possible role of the primary somatosensory system (S1) in emotion processing. To date, however, the causal role of S1 in emotion generation remains unclear. To address this question, we applied transcranial alternating current stimulation (tACS) on the S1 at different frequencies (beta, theta, and sham) while participants saw emotional stimuli with different degrees of pleasantness and levels of arousal. Results showed that modulation of S1 influenced subjective emotional ratings as a function of the frequency applied. While theta and beta-tACS made participants rate the emotional images as more pleasant (higher valence), only theta-tACS lowered the subjective arousal ratings (more calming). Skin conductance responses recorded throughout the experiment confirmed a different arousal for pleasant versus unpleasant stimuli. Our study revealed that S1 has a causal role in the feeling of emotions, adding new insight into the embodied nature of emotions. Importantly, we provided causal evidence that beta and theta frequencies contribute differently to the modulation of two dimensions of emotions-arousal and valence-corroborating the view of a dissociation between these two dimensions of emotions.

Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.

Active suicidal ideation associated with dysfunction in default mode network using resting-state EEG and functional MRI - Findings from the T-RAD Study

Suicide in youth and young adults is a serious public health problem. However, the biological mechanisms of suicidal ideation (SI) remain poorly understood. The primary goal of these analyses was to identify the connectome profile of suicidal ideation using resting state electroencephalography (EEG). We evaluated the neurocircuitry of SI in a sample of youths and young adults (aged 10-26 years, n = 111) with current or past diagnoses of either a depressive disorder or bipolar disorder who were enrolled in the Texas Resilience Against Depression Study (T-RAD). Neurocircuitry was analyzed using orthogonalized power envelope connectivity computed from resting state EEG. Suicidal ideation was assessed with the 3-item Suicidal Thoughts factor of the Concise Health Risk Tracking self-report scale. The statistical pipeline involved dimension reduction using principal component analysis, and the association of neuroimaging data with SI using regularized canonical correlation analysis. From the original 111 participants and the correlation matrix of 4950 EEG connectivity pairs in each band (alpha, beta, theta), dimension reduction generated 1305 EEG connectivity pairs in the theta band, 2337 EEG pairs in the alpha band, and 914 EEG connectivity pairs in the beta band. Overall, SI was consistently involved with dysfunction of the default mode network (DMN). This report provides preliminary evidence of DMN dysfunction associated with active suicidal ideation in adolescents. Using EEG using power envelopes to compute connectivity moves us closer to using neurocircuit dysfunction in the clinical setting to identify suicidal ideation.

Theta oscillations underlie the interplay between emotional processing and empathy

Emotional reactions to salient stimuli are well documented in psychophysiological research. However, some individual variables that can influence how people process emotions (i.e., empathy traits) have received little consideration. The present study investigated the relationship between emotions and empathy. Forty participants completed the Interpersonal Reactivity Index, a questionnaire that measure general and specific empathy dimensions. Then, emotional (erotic and mutilation) and non-emotional pictures were presented, during electroencephalographic recording. Valence and arousal were evaluated for each stimulus. Behavioral results revealed a positive correlation between the arousal induced by mutilation pictures and personal distress (i.e., feeling discomfort in emergency situations). At the electrophysiological level, theta activity elicited by positive and negative emotion processing in the superior frontal gyrus was associated with personal distress. Moreover, erotic-related theta in the middle frontal gyrus was associated with subjective judgement of erotic stimulus valence. Overall, theta activity modulated the interplay between emotions and empathy.

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB)

BACKGROUND

The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear.

METHODS

In this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB).

RESULTS

We found that the MDD group showed significantly higher power in the high alpha range (10.5-11.5 Hz) and lower power in the low alpha range (7-8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone.

CONCLUSIONS

Our results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

The relationship between alpha power and heart rate variability commonly seen in various mental states

The extensive exploration of the correlation between electroencephalogram (EEG) and heart rate variability (HRV) has yielded inconsistent outcomes, largely attributable to variations in the tasks employed in the studies. The direct relationship between EEG and HRV is further complicated by alpha power, which is susceptible to influences such as mental fatigue and sleepiness. This research endeavors to examine the brain-heart interplay typically observed during periods of music listening and rest. In an effort to mitigate the indirect effects of mental states on alpha power, subjective fatigue and sleepiness were measured during rest, while emotional valence and arousal were evaluated during music listening. Partial correlation analyses unveiled positive associations between occipital alpha2 power (10-12 Hz) and nHF, an indicator of parasympathetic activity, under both music and rest conditions. These findings underscore brain-heart interactions that persist even after the effects of other variables have been accounted for.

Understanding the role of emotion in decision making process: using machine learning to analyze physiological responses to visual, auditory, and combined stimulation

Emotions significantly shape decision-making, and targeted emotional elicitations represent an important factor in neuromarketing, where they impact advertising effectiveness by capturing potential customers' attention intricately associated with emotional triggers. Analyzing biometric parameters after stimulus exposure may help in understanding emotional states. This study investigates autonomic and central nervous system responses to emotional stimuli, including images, auditory cues, and their combination while recording physiological signals, namely the electrocardiogram, blood volume pulse, galvanic skin response, pupillometry, respiration, and the electroencephalogram. The primary goal of the proposed analysis is to compare emotional stimulation methods and to identify the most effective approach for distinct physiological patterns. A novel feature selection technique is applied to further optimize the separation of four emotional states. Basic machine learning approaches are used in order to discern emotions as elicited by different kinds of stimulation. Electroencephalographic signals, Galvanic skin response and cardio-respiratory coupling-derived features provided the most significant features in distinguishing the four emotional states. Further findings highlight how auditory stimuli play a crucial role in creating distinct physiological patterns that enhance classification within a four-class problem. When combining all three types of stimulation, a validation accuracy of 49% was achieved. The sound-only and the image-only phases resulted in 52% and 44% accuracy respectively, whereas the combined stimulation of images and sounds led to 51% accuracy. Isolated visual stimuli yield less distinct patterns, necessitating more signals for relatively inferior performance compared to other types of stimuli. This surprising significance arises from limited auditory exploration in emotional recognition literature, particularly contrasted with the pleathora of studies performed using visual stimulation. In marketing, auditory components might hold a more relevant potential to significantly influence consumer choices.

Alpha and theta oscillations during the cognitive reappraisal of aversive pictures: A spatio-temporal qEEG investigation

CONTEXT

Emotion regulation is a set of processes responsible for controlling, evaluating and adjusting reactions to achieve a goal. Results derived from magnetic resonance imaging agreed on the involvement of frontal and limbic structures in this process. Findings using cognition and physiology interactions are still scarce but suggest a role of alpha rhythm in emotional induction and for theta in regulation.

OBJECTIVES AND HYPOTHESES

Our goal was to investigate alpha and theta rhythm during the reappraisal of aversive stimuli. We hypothesized that an implication of alpha rhythm in emotional induction only and an increase in prefrontal theta rhythm positively correlated with successful regulation.

METHOD

Twenty-four healthy participants were recorded with 64 EEG electrodes while asked to watch or reappraise negative pictures passively. Theta and alpha rhythms were compared across maintain, decrease and increase regulation conditions, and a source localization estimated the generators.

RESULTS

Theta activity was consistently higher in the upregulation than in the maintenance condition (p = .04) for the entire control period, but mainly at the beginning of regulation (1-3 s) for low-theta and later (5-7 s) for high-theta. Moreover, our results confirm that a low-theta generator correlated with mainly the middle frontal gyrus and the anterior dorsal cingulate cortex during upregulation. Theta was sensitive to emotion upregulation, whereas the alpha oscillation was non-sensitive to emotion induction and regulation.

CONCLUSION

Theta rhythm was involved explicitly in emotion upregulation processes that occur at a definite time during reappraisal, whereas the alpha rhythm was not altered by emotion induction and regulation.

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.

Early threat perception is independent of later cognitive and behavioral control. A virtual reality-EEG-ECG study

Research on social threat has shown influences of various factors, such as agent characteristics, proximity, and social interaction on social threat perception. An important, yet understudied aspect of threat exposure concerns the ability to exert control over the threat and its implications for threat perception. In this study, we used a virtual reality (VR) environment showing an approaching avatar that was either angry (threatening body expression) or neutral (neutral body expression) and informed participants to stop avatars from coming closer under five levels of control success (0, 25, 50, 75, or 100%) when they felt uncomfortable. Behavioral results revealed that social threat triggered faster reactions at a greater virtual distance from the participant than the neutral avatar. Event-related potentials (ERPs) revealed that the angry avatar elicited a larger N170/vertex positive potential (VPP) and a smaller N3 than the neutral avatar. The 100% control condition elicited a larger late positive potential (LPP) than the 75% control condition. In addition, we observed enhanced theta power and accelerated heart rate for the angry avatar vs. neutral avatar, suggesting that these measures index threat perception. Our results indicate that perception of social threat takes place in early to middle cortical processing stages, and control ability is associated with cognitive evaluation in middle to late stages.

EEG rhythm based emotion recognition using multivariate decomposition and ensemble machine learning classifier

Recently, electroencephalogram (EEG) signals have shown great potential to recognize human emotions. The goal of effective computing is to assist computers in understanding various types of emotions via human-computer interaction (HCI). Multichannel EEG signals are used to measure the electrical activity of the brain in space and time. Automated emotion recognition using multichannel EEG signals is an interesting area of cognitive neuroscience and affective computing research. This research proposes EEG multichannel rhythmic features and ensemble machine learning (EML) classifiers with leave-one-subject-out cross-validation (LOSOCV) for automatic emotion classification from multichannel EEG recordings. Multivariate fast iterative filtering (MvFIF) is used to assess the EEG rhythm sequences. EEG rhythms delta(δ), theta(θ), alpha(α), beta(β), and gamma(γ) are separated based on the mean frequency of the EEG rhythm sequence. Three Hjorth parameters and nine entropy features were extracted from multichannel EEG rhythms. Extracted features are selected using the minimum redundancy maximum relevance (mRMR) approach. The experimental design was performed on two emotional datasets (GAMEEMO and DREAMER). The validation showed that gamma rhythm multichannel features with EML-based subspace K-nearest neighbor (SS KNN) were as high as 93.5%-99.8%, achieving high classification accuracy. The comparisons of δ, θ, α, β, and γ rhythms with EML, support vector machine (SVM), and artificial neural network (ANN) were performed. we also analyzed multi-class emotions (HVHA, HVLA, LVHA, LVLA) with an ensemble-based bagging tree on gamma rhythm. It provides a novel solution for multichannel rhythm-specific features in EEG data analysis.

Testing EEG functional connectivity between sensorimotor and face processing visual regions in individuals with congenital facial palsy

Moebius syndrome (MBS) is characterized by the congenital absence or underdevelopment of cranial nerves VII and VI, leading to facial palsy and impaired lateral eye movements. As a result, MBS individuals cannot produce facial expressions and did not develop motor programs for facial expressions. In the latest model of sensorimotor simulation, an iterative communication between somatosensory, motor/premotor cortices, and visual regions has been proposed, which should allow more efficient discriminations among subtle facial expressions. Accordingly, individuals with congenital facial motor disability, specifically with MBS, should exhibit atypical communication within this network. Here, we aimed to test this facet of the sensorimotor simulation models. We estimated the functional connectivity between the visual cortices for face processing and the sensorimotor cortices in healthy and MBS individuals. To this aim, we studied the strength of beta band functional connectivity between these two systems using high-density EEG, combined with a change detection task with facial expressions (and a control condition involving non-face stimuli). The results supported our hypothesis such that when discriminating subtle facial expressions, participants affected by congenital facial palsy (compared to healthy controls) showed reduced connectivity strength between sensorimotor regions and visual regions for face processing. This effect was absent for the condition with non-face stimuli. These findings support sensorimotor simulation models and the communication between sensorimotor and visual areas during subtle facial expression processing.

Cortical-brainstem interplay during speech perception in older adults with and without hearing loss

Introduction

Real time modulation of brainstem frequency-following responses (FFRs) by online changes in cortical arousal state via the corticofugal (top-down) pathway has been demonstrated previously in young adults and is more prominent in the presence of background noise. FFRs during high cortical arousal states also have a stronger relationship with speech perception. Aging is associated with increased auditory brain responses, which might reflect degraded inhibitory processing within the peripheral and ascending pathways, or changes in attentional control regulation via descending auditory pathways. Here, we tested the hypothesis that online corticofugal interplay is impacted by age-related hearing loss.

Methods

We measured EEG in older adults with normal-hearing (NH) and mild to moderate hearing-loss (HL) while they performed speech identification tasks in different noise backgrounds. We measured α power to index online cortical arousal states during task engagement. Subsequently, we split brainstem speech-FFRs, on a trial-by-trial basis, according to fluctuations in concomitant cortical α power into low or high α FFRs to index cortical-brainstem modulation.

Results

We found cortical α power was smaller in the HL than the NH group. In NH listeners, α-FFRs modulation for clear speech (i.e., without noise) also resembled that previously observed in younger adults for speech in noise. Cortical-brainstem modulation was further diminished in HL older adults in the clear condition and by noise in NH older adults. Machine learning classification showed low α FFR frequency spectra yielded higher accuracy for classifying listeners' perceptual performance in both NH and HL participants. Moreover, low α FFRs decreased with increased hearing thresholds at 0.5-2 kHz for clear speech but noise generally reduced low α FFRs in the HL group.

Discussion

Collectively, our study reveals cortical arousal state actively shapes brainstem speech representations and provides a potential new mechanism for older listeners' difficulties perceiving speech in cocktail party-like listening situations in the form of a miss-coordination between cortical and subcortical levels of auditory processing.

Oscillatory brain responses to emotional stimuli are effects related to events rather than states

Emotional cues draw attention, thereby enabling enhanced processing. Electrophysiological brain research in humans suggests that increased gamma band activity and decreased alpha band activity over posterior brain areas is associated with the allocation of attention. However, emotional events can alternate quickly, like rapidly changing news items and it remains unknown whether the modulation of brain oscillations happens in a stimulus induced manner, changing with each individual stimulus, or whether the events lead to prolonged, state-like changes. To investigate this, we measured the electroencephalogram (EEG) during a passive viewing task (N = 32) while emotional pictures International Affective Picture System (IAPS) were presented in blocks containing either pleasant and neutral or unpleasant and neutral pictures. As predicted, we found decreased alpha and increased gamma power over posterior areas in response to unpleasant compared to pleasant pictures (and also compared to neutral pictures for gamma power). When testing the neutral pictures of the unpleasant and pleasant block against each other, we found no significant difference, which speaks to a stimulus induced effect of alpha and gamma power rather than a state effect. In addition, the inter-trial interval (ITI) between the pictures did not differ between the unpleasant and pleasant block either, corroborating this conclusion. Since emotional pictures can at the same time elicit a freezing-like response and we were interested in whether this freezing-like response co-occurs with enhanced attention, we also collected postural sway data. However, within this EEG-setup, postural analyses indicated no stimulus-related effects nor a correlation with EEG-data. We interpret the alpha and gamma band results as reflecting event-related attention toward unpleasant compared to pleasant (and neutral) pictures and discuss this finding in light of previous EEG research and in combination with behavioral research on threat-induced reductions in body sway (freezing-like response).

Brainstem speech encoding is dynamically shaped online by fluctuations in cortical α state

Experimental evidence in animals demonstrates cortical neurons innervate subcortex bilaterally to tune brainstem auditory coding. Yet, the role of the descending (corticofugal) auditory system in modulating earlier sound processing in humans during speech perception remains unclear. Here, we measured EEG activity as listeners performed speech identification tasks in different noise backgrounds designed to tax perceptual and attentional processing. We hypothesized brainstem speech coding might be tied to attention and arousal states (indexed by cortical α power) that actively modulate the interplay of brainstem-cortical signal processing. When speech-evoked brainstem frequency-following responses (FFRs) were categorized according to cortical α states, we found low α FFRs in noise were weaker, correlated positively with behavioral response times, and were more "decodable" via neural classifiers. Our data provide new evidence for online corticofugal interplay in humans and establish that brainstem sensory representations are continuously yoked to (i.e., modulated by) the ebb and flow of cortical states to dynamically update perceptual processing.

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.

Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review

Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.

A Combination of Green Tea, Rhodiola, Magnesium, and B Vitamins Increases Electroencephalogram Theta Activity During Attentional Task Performance Under Conditions of Induced Social Stress

Background

A combination of green tea, rhodiola and magnesium with B vitamins has previously been reported to significantly increase EEG resting state theta, attenuate subjective stress, anxiety and mood disturbance, and heighten subjective and autonomic arousal under acute psychosocial laboratory stress. Here we examine the capacity of green tea and rhodiola extract administered in combination or in isolation with magnesium and B vitamins to moderate spectral brain activity during attentional task performance under stress.

Materials and Methods

One-hundred moderately stressed adults received oral supplementation of (i) Mg + B vitamins + green tea + rhodiola; (ii) Mg + B vitamins + rhodiola; (iii) Mg + B vitamins + green tea; or (iv) placebo, in a double-blind, randomised, placebo-controlled, parallel-group design (Clinicaltrials.gov: NCT03262376; 25/0817). Participants completed an attention switching and emotionally threatening attentional bias task after stress induction (Trier Social Stress Test). Spectral alpha and theta brain activity and event related potentials (ERPs) were recorded during cognitive task performance by electroencephalogram (EEG; BioSemi ActiveTwo 64 channel).

Results

The combined treatment of Mg + B vitamins + green tea + rhodiola significantly increased frontal midline theta vs. placebo and rhodiola in isolation during the attention switching task, specifically in anticipation of a change in task performance parameter. The combined treatment also significantly increased contralateral theta activation in relation to viewing emotionally threatening images in the left (vs. placebo and rhodiola in isolation) and right parietal (vs. placebo) regions. Further, this treatment demonstrated significantly heightened ipsilateral left parietal theta activation in relation to viewing emotionally threatening images. The combined treatment attenuated a decrease in alpha power during the attentional bias task evident in comparator treatments, but this did not reach significance. No significant effects of treatments on behavioural performance or ERP were found.

Conclusion

The combination of Mg + B vitamins + green tea + rhodiola increased spectral theta brain activity during the execution of two attentional tasks suggestive of a potential to increase attentional capacity under conditions of stress. Further examination of these ingredients in relation to attentional performance under stress is warranted to ascertain if functional benefits suggested by theta activation can be shown behaviourally.

Comparison of different emotion stimulation modalities: an EEG signal analysis

Emotions processing is a complex mechanism that involves different physiological systems. In particular, the Central Nervous System (CNS) is considered to play a key role in this mechanism and one of the main modalities to study the CNS activity is the Electroencephalographic signal (EEG). To elicit emotions, different kinds of stimuli can be used e.g.: audio, visual or a combination of the two. Literature studies focus more on the correct classification of the different types of emotions or which kind of stimulation gives the best performance in terms of classification accuracy. However, it is still unclear how the different stimuli elicit the emotions and which are the results in terms of brain activity. In this paper, we analysed and compared EEG signals given by eliciting emotions using audio and visual stimuli or a combination of the latter two. Data were collected during experiments conducted in our laboratories using IAPS and IADS dataset. Our study confirmed literature physiological studies about emotions highlighting higher brain activity in the frontal and central regions and in the δ and θ bands for each kind of stimulus. However, audio stimulation was found to have higher responses when compared to the other two modalities of stimulation in almost all the comparisons performed. Higher values of the δ/β ratios, an index related to negative emotions, have been achieved when using only sounds as stimuli. Moreover, the same type of stimuli, resulted in higher δ-β coupling, suggesting a better attention control. We concluded that stimulating subjects without letting them know (seeing) what is actually happening may give a higher perception of emotions, even if this mechanism remains highly subjective. Clinical Relevance- This paper suggests that audio stimuli may give higher perception of the elicited emotion resulting in higher brain activity in the physiological areas and more focused subjects. Thus using only audio in emotion related studies may give more reliable and consistent results.

Comparison of Slides and Video Clips as Different Methods for Inducing Emotions: An Electroencephalographic Alpha Modulation Study

Films, compared with emotional static pictures, represent true-to-life dynamic stimuli that are both ecological and effective in inducing an emotional response given the involvement of multimodal stimulation (i.e., visual and auditory systems). We hypothesized that a direct comparison between the two methods would have shown greater efficacy of movies, compared to standardized slides, in eliciting emotions at both subjective and neurophysiological levels. To this end, we compared these two methods of emotional stimulation in a group of 40 young adults (20 females). Electroencephalographic (EEG) Alpha rhythm (8–12 Hz) was recorded from 64 scalp sites while participants watched (in counterbalanced order across participants) two separate blocks of 45 slides and 45 clips. Each block included three groups of 15 validated stimuli classified as Erotic, Neutral and Fear content. Greater self-perceived arousal was found after the presentation of Fear and Erotic video clips compared with the same slide categories. sLORETA analysis showed a different lateralization pattern: slides induced decreased Alpha power (greater activation) in the left secondary visual area (Brodmann Area, BA, 18) to Erotic and Fear compared with the Neutral stimuli. Instead, video clips elicited reduced Alpha in the homologous right secondary visual area (BA 18) again to both Erotic and Fear contents compared with Neutral ones. Comparison of emotional stimuli showed smaller Alpha power to Erotic than to Fear stimuli in the left precuneus/posterior cingulate cortex (BA 7/31) for the slide condition, and in the left superior parietal lobule (BA 7) for the clip condition. This result matched the parallel analysis of the overlapped Mu rhythm (corresponding to the upper Alpha band) and can be interpreted as Mu/Alpha EEG suppression elicited by greater motor action tendency to Erotic (approach motivation) compared to Fear (withdrawal motivation) stimuli. Correlation analysis found lower Alpha in the left middle temporal gyrus (BA 21) associated with greater pleasantness to Erotic slides (r38 = –0.62, p = 0.009), whereas lower Alpha in the right supramarginal/angular gyrus (BA 40/39) was associated with greater pleasantness to Neutral clips (r38 = –0.69, p = 0.012). Results point to stronger emotion elicitation of movies vs. slides, but also to a specific involvement of the two hemispheres during emotional processing of slides vs. video clips, with a shift from the left to the right associative visual areas.

Cardiac sympathetic-vagal activity initiates a functional brain-body response to emotional arousal

We investigate the temporal dynamics of brain and cardiac activities in healthy subjects who underwent an emotional elicitation through videos. We demonstrate that, within the first few seconds, emotional stimuli modulate heartbeat activity, which in turn stimulates an emotion intensity (arousal)–specific cortical response. The emotional processing is then sustained by a bidirectional brain–heart interplay, where the perceived arousal level modulates the amplitude of ascending heart-to-brain neural information flow. These findings may constitute fundamental knowledge linking neurophysiology and psychiatric disorders, including the link between depressive symptoms and cardiovascular disorders.

A century-long debate on bodily states and emotions persists. While the involvement of bodily activity in emotion physiology is widely recognized, the specificity and causal role of such activity related to brain dynamics has not yet been demonstrated. We hypothesize that the peripheral neural control on cardiovascular activity prompts and sustains brain dynamics during an emotional experience, so these afferent inputs are processed by the brain by triggering a concurrent efferent information transfer to the body. To this end, we investigated the functional brain–heart interplay under emotion elicitation in publicly available data from 62 healthy subjects using a computational model based on synthetic data generation of electroencephalography and electrocardiography signals. Our findings show that sympathovagal activity plays a leading and causal role in initiating the emotional response, in which ascending modulations from vagal activity precede neural dynamics and correlate to the reported level of arousal. The subsequent dynamic interplay observed between the central and autonomic nervous systems sustains the processing of emotional arousal. These findings should be particularly revealing for the psychophysiology and neuroscience of emotions.

Enhancing Emotion Recognition Using Region-Specific Electroencephalogram Data and Dynamic Functional Connectivity

Recognizing the emotional states of humans through EEG signals are of great significance to the progress of human-computer interaction. The present study aimed to perform automatic recognition of music-evoked emotions through region-specific information and dynamic functional connectivity of EEG signals and a deep learning neural network. EEG signals of 15 healthy volunteers were collected when different emotions (high-valence-arousal vs. low-valence-arousal) were induced by a musical experimental paradigm. Then a sequential backward selection algorithm combining with deep neural network called Xception was proposed to evaluate the effect of different channel combinations on emotion recognition. In addition, we also assessed whether dynamic functional network of frontal cortex, constructed through different trial number, may affect the performance of emotion cognition. Results showed that the binary classification accuracy based on all 30 channels was 70.19%, the accuracy based on all channels located in the frontal region was 71.05%, and the accuracy based on the best channel combination in the frontal region was 76.84%. In addition, we found that the classification performance increased as longer temporal functional network of frontal cortex was constructed as input features. In sum, emotions induced by different musical stimuli can be recognized by our proposed approach though region-specific EEG signals and time-varying functional network of frontal cortex. Our findings could provide a new perspective for the development of EEG-based emotional recognition systems and advance our understanding of the neural mechanism underlying emotion processing.

An electroencephalography connectome predictive model of major depressive disorder severity

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.

Theta oscillations: A rhythm difference comparison between major depressive disorder and anxiety disorder

BACKGROUND

Due to substantial comorbidities of major depressive disorder (MDD) and anxiety disorder (AN), these two disorders must be distinguished. Accurate identification and diagnosis facilitate effective and prompt treatment. EEG biomarkers are a potential research hotspot for neuropsychiatric diseases. The purpose of this study was to investigate the differences in EEG power spectrum at theta oscillations between patients with MDD and patients with AN.

METHODS

Spectral analysis was used to study 66 patients with MDD and 43 patients with AN. Participants wore 16-lead EEG caps to measure resting EEG signals. The EEG power spectrum was measured using the fast Fourier transform. Independent samples t-test was used to analyze the EEG power values of the two groups, and p < 0.05 was statistically significant.

RESULTS

EEG power spectrum of the MDD group significantly differed from the AN group in the theta oscillation on 4-7 Hz at eight electrode points at F3, O2, T3, P3, P4, FP1, FP2, and F8.

CONCLUSION

Participants with anxiety demonstrated reduced power in the prefrontal cortex, left temporal lobe, and right occipital regions. Confirmed by further studies, theta oscillations could be another biomarker that distinguishes MDD from AN.

A Novel Online Action Observation-Based Brain-Computer Interface That Enhances Event-Related Desynchronization

Brain-computer interface (BCI)-based stroke rehabilitation is an emerging field in which different studies have reported variable outcomes. Among the BCI paradigms, motor imagery (MI)-based closed-loop BCI is still the main pattern in rehabilitation training. It can estimate a patient' motor intention and provide corresponding feedback. However, the individual difference in the ability to generate event-related desynchronization (ERD) and the low classification accuracy of the multi-class scenario restrict the application of MI-based BCI. In the current study, a novel online action observation (AO)-based BCI was proposed. The visual stimuli of four types of hand movements were designed to simultaneously induce steady-state motion visual evoked potential (SSMVEP) in the occipital region and to activate the sensorimotor region. Task-related component analysis was performed to identify the SSMVEP. Results showed that the amplitude of the induced frequency in the SSMVEP had a negative relationship with the stimulus frequency. The classification accuracy in the four-class scenario reached 72.81 ± 13.55% within 2.5s. Importantly, the AO-based closed-loop BCI, which provided visual feedback based on the SSMVEP, could enhance ERD compared with AO-alone. The increased attentiveness might be one key factor for the enhancement of the ERD in the designed AO-based BCI. In summary, the proposed AO-based BCI provides a new insight for BCI-based rehabilitation.

Alpha-to-beta- and gamma-band activity reflect predictive coding in affective visual processing

Processing of negative affective pictures typically leads to desynchronization of alpha-to-beta frequencies (ERD) and synchronization of gamma frequencies (ERS). Given that in predictive coding higher frequencies have been associated with prediction errors, while lower frequencies have been linked to expectations, we tested the hypothesis that alpha-to-beta ERD and gamma ERS induced by aversive pictures are associated with expectations and prediction errors, respectively. We recorded EEG while volunteers were involved in a probabilistically cued affective picture task using three different negative valences to produce expectations and prediction errors. Our data show that alpha-to-beta band activity after stimulus presentation was related to the expected valence of the stimulus as predicted by a cue. The absolute mismatch of the expected and actual valence, which denotes an absolute prediction error was related to increases in alpha, beta and gamma band activity. This demonstrates that top-down predictions and bottom-up prediction errors are represented in typical spectral patterns associated with affective picture processing. This study provides direct experimental evidence that negative affective picture processing can be described by neuronal predictive coding computations.

Investigation of the interaction between emotion and working memory load using spatiotemporal pattern similarity analysis

Objective.Accumulating evidence has revealed that emotions can be provided with the modulatory effect on working memory (WM) and WM load is an important factor for the interaction between emotion and WM. However, it remains controversial whether emotions inhibit or facilitate WM and the interaction between cognitive task, processing load and emotional processing remains unclear.Approach.In this study, we used a change detection paradigm wherein memory items have four different load sizes and emotion videos to induce three emotions (negative, neutral, and positive). We performed an event-related spectral perturbation (ERSP) analysis and a spatiotemporal pattern similarity (STPS) analysis on the electroencephalography data.Main results.The ERSP results indicated that alpha and beta oscillations can reflect the difference among WM load sizes and also can reflect the difference among emotions under middle high WM load over posterior brain region in the maintenance stage. Moreover, the STPS results demonstrated a significant interaction between emotion and WM load size in the posterior region and found significantly higher similarity indexes for the negative emotion to the neutral emotion under the middle high WM load during WM maintenance. In addition, The STPS results also revealed that both positive emotion and negative emotion could interfere with the distinction of load sizes.Significance.The consistence of the behavioral, ERSP and STPS results suggested that when the memory load approaches the limit of WM capacity, negative emotion could facilitate WM through the top-down attention modulation promoting the most relevant information storage during WM maintenance.

Effects of the calorie content of visual food stimuli and simulated situations on event-related frontal alpha asymmetry and event-related potentials in the context of food choices

Approach and avoidance tendencies play an important role in everyday food choices when choosing between high-caloric, rather unhealthy, and low-caloric, rather healthy options. On a neuronal level, approach and avoidance motivation have been associated with asymmetrical activity of the frontal cortex, often quantified by alpha power averaged over several seconds of resting electroencephalogram (EEG). Going beyond the analysis of resting EEG, the present study aimed to investigate asymmetrical frontal activity in direct response to food stimuli in an event-related design and in combination with event-related potentials (ERPs). Therefore, a sample of 56 young and healthy participants completed a food choice task. They were asked to choose from a selection of high-caloric and low-caloric foods which they would want to eat on a normal day (baseline), when being on a diet, and in a reward situation. On the behavioural level, there was a clear preference for low-caloric foods. Well in line with that, time-frequency analyses of alpha asymmetry revealed relatively stronger temporary (950-1175 ms) left-hemispheric frontal activity, that is, a stronger approach tendency, in response to low-caloric as compared to high-caloric foods. Furthermore, larger P300 for low-caloric foods indicated an increased task relevance of low-caloric foods in the baseline and the reward situation. In contrast, the late positive potential (LPP), an index of subjective value, was larger for high-as compared to low-caloric foods, reflecting the intrinsic rewarding properties of high-caloric foods. ERPs, but not frontal alpha asymmetry, were influenced by the situational context.

Non-phase-locked alpha oscillations are involved in spontaneous racial categorization of faces

Racial categorization of faces has a notable impact on human behavior, but its neural underpinnings remain unresolved. Previous electroencephalography (EEG) research focused on contributions of phase-locked neural activities to racial categorization of faces. We investigated functional roles of non-phase-locked neural oscillations in spontaneous racial categorization of faces by recording EEG from Chinese adults who performed an individuation task on Asian/White faces in Experiment 1 and on Asian/Black faces in Experiment 2. We quantified neural processes involved in spontaneous racial categorization of faces by examining repetition suppression of non-phase-locked neural oscillations when participants viewed faces of one race presented repeatedly in the same block of trials (repetition condition), or faces of two races presented alternately in the same block of trials (alternating condition). We found decreased power of alpha (9-13 Hz) oscillations in the repetition than alternating conditions at 80-240 ms over frontal-central electrodes induced by White/Black (but not Asian) faces. Moreover, larger repetition suppression of alpha oscillations in response to White/Black (vs. Asian) faces predicted greater implicit negative attitudes toward White/Black faces across individuals. Our findings suggest that non-phase-locked alpha oscillations are engaged in spontaneous racial categorization of faces and are associated with implicit negative attitudes toward other-race faces.

Emotional arousal due to video stimuli reduces local and inter-regional synchronization of oscillatory cortical activities in alpha- and beta-bands

The purpose of current study is to reveal spatiotemporal features of oscillatory EEG activities in response to emotional arousal induced by emotional video stimuli, and to find the characteristics of cortical activities showing significant difference according to arousal levels. The EEGs recorded during watching affective video clips were transformed to cortical current density time-series, and then, cluster-based permutation test was applied to determine the spatiotemporal origins of alpha- and beta-band activities showing significant difference between high and low arousal levels. We found stronger desynchronization of alpha-band activities due to higher arousal in visual areas, which may be due to stronger activation for sensory information processing for the highly arousing video stimuli. In precentral and superior parietal regions, the stronger desynchronization in alpha-and low beta-bands was observed for the high arousal stimuli. This is expected to reflect enhanced mirror neuron system activities, which is involved in understanding the intention of other’s action. Similar changes according to arousal level were found also in inter-regional phase synchronization in alpha- and beta-bands.

Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals

Emotion is interpreted as a psycho-physiological process, and it is associated with personality, behavior, motivation, and character of a person. The objective of affective computing is to recognize different types of emotions for human-computer interaction (HCI) applications. The spatiotemporal brain electrical activity is measured using multi-channel electroencephalogram (EEG) signals. Automated emotion recognition using multi-channel EEG signals is an exciting research topic in cognitive neuroscience and affective computing. This paper proposes the rhythm-specific multi-channel convolutional neural network (CNN) based approach for automated emotion recognition using multi-channel EEG signals. The delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) rhythms of EEG signal for each channel are evaluated using band-pass filters. The EEG rhythms from the selected channels coupled with deep CNN are used for emotion classification tasks such as low-valence (LV) vs. high valence (HV), low-arousal (LA) vs. high-arousal (HA), and low-dominance (LD) vs. high dominance (HD) respectively. The deep CNN architecture considered in the proposed work has eight convolutions, three average pooling, four batch-normalization, three spatial drop-outs, two drop-outs, one global average pooling and, three dense layers. We have validated our developed model using three publicly available databases: DEAP, DREAMER, and DASPS. The results reveal that the proposed multivariate deep CNN approach coupled with β-rhythm has obtained the accuracy values of 98.91%, 98.45%, and 98.69% for LV vs. HV, LA vs. HA, and LD vs. HD emotion classification strategies, respectively using DEAP database with 10-fold cross-validation (CV) scheme. Similarly, the accuracy values of 98.56%, 98.82%, and 98.99% are obtained for LV vs. HV, LA vs. HA, and LD vs. HD classification schemes, respectively, using deep CNN and θ-rhythm. The proposed multi-channel rhythm-specific deep CNN classification model has obtained the average accuracy value of 57.14% using α-rhythm and trial-specific CV using DASPS database. Moreover, for 8-quadrant based emotion classification strategy, the deep CNN based classifier has obtained an overall accuracy value of 24.37% using γ-rhythms of multi-channel EEG signals. Our developed deep CNN model can be used for real-time automated emotion recognition applications.

A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis

BACKGROUND

Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed through questionnaire-based approaches; however, these methods may not lead to an accurate diagnosis. In this regard, many studies have focused on using electroencephalogram (EEG) signals and machine learning techniques to diagnose MDD.

NEW METHOD

This paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework.

RESULTS

The proposed method is validated with a public EEG dataset, including the EEG data of 34 MDD patients and 30 healthy subjects. The evaluation of the proposed framework is conducted using 10-fold cross-validation, providing the metrics such as accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR). The best performance of the proposed method has provided an average AC of 99%, SE of 98.4%, SP of 99.6%, F1 of 98.9%, and FDR of 0.4% using the support vector machine with RBF kernel (RBFSVM) classifier.

COMPARISON WITH EXISTING METHODS

The obtained results demonstrate that the proposed method outperforms other approaches for MDD classification based on EEG signals.

CONCLUSIONS

According to the obtained results, a highly accurate MDD diagnosis would be provided using the proposed method, while it can be utilized to develop a computer-aided diagnosis (CAD) tool for clinical purposes.

Event-related EEG oscillatory responses elicited by dynamic facial expression

BACKGROUND

Recognition of facial expressions (FEs) plays a crucial role in social interactions. Most studies on FE recognition use static (image) stimuli, even though real-life FEs are dynamic. FE processing is complex and multifaceted, and its neural correlates remain unclear. Transitioning from static to dynamic FE stimuli might help disentangle the neural oscillatory mechanisms underlying face processing and recognition of emotion expression. To our knowledge, we here present the first time-frequency exploration of oscillatory brain mechanisms underlying the processing of dynamic FEs.

RESULTS

Videos of joyful, fearful, and neutral dynamic facial expressions were presented to 18 included healthy young adults. We analyzed event-related activity in electroencephalography (EEG) data, focusing on the delta, theta, and alpha-band oscillations. Since the videos involved a transition from neutral to emotional expressions (onset around 500 ms), we identified time windows that might correspond to face perception initially (time window 1; first TW), and emotion expression recognition subsequently (around 1000 ms; second TW). First TW showed increased power and phase-locking values for all frequency bands. In the first TW, power and phase-locking values were higher in the delta and theta bands for emotional FEs as compared to neutral FEs, thus potentially serving as a marker for emotion recognition in dynamic face processing.

CONCLUSIONS

Our time-frequency exploration revealed consistent oscillatory responses to complex, dynamic, ecologically meaningful FE stimuli. We conclude that while dynamic FE processing involves complex network dynamics, dynamic FEs were successfully used to reveal temporally separate oscillation responses related to face processing and subsequently emotion expression recognition.

The Effects of Dynamic and Static Emotional Facial Expressions of Humans and Their Avatars on the EEG: An ERP and ERD/ERS Study

This study aimed to examine whether the cortical processing of emotional faces is modulated by the computerization of face stimuli ("avatars") in a group of 25 healthy participants. Subjects were passively viewing 128 static and dynamic facial expressions of female and male actors and their respective avatars in neutral or fearful conditions. Event-related potentials (ERPs), as well as alpha and theta event-related synchronization and desynchronization (ERD/ERS), were derived from the EEG that was recorded during the task. All ERP features, except for the very early N100, differed in their response to avatar and actor faces. Whereas the N170 showed differences only for the neutral avatar condition, later potentials (N300 and LPP) differed in both emotional conditions (neutral and fear) and the presented agents (actor and avatar). In addition, we found that the avatar faces elicited significantly stronger reactions than the actor face for theta and alpha oscillations. Especially theta EEG frequencies responded specifically to visual emotional stimulation and were revealed to be sensitive to the emotional content of the face, whereas alpha frequency was modulated by all the stimulus types. We can conclude that the computerized avatar faces affect both, ERP components and ERD/ERS and evoke neural effects that are different from the ones elicited by real faces. This was true, although the avatars were replicas of the human faces and contained similar characteristics in their expression.

Face Recognition Brain Functional Connectivity in Patients With Major Depression: A Brain Source Localization Study by ERP

Objective: Patients with major depressive disorder (MDD) presents with face recognition defects. These defects negatively affect their social interactions. However, the cause of these defects is not clear. This study sought to explore whether MDD patients develop facial perceptual processing disorders with characteristics of brain functional connectivity (FC). Methods: Event-related potential (ERP) was used to explore differences between 20 MDD patients and 20 healthy participants with face and non-face recognition tasks based on 64 EEG parameters. After pre-processing of EEG data and source reconstruction using the minimum-norm estimate (MNE), data were converted to AAL90 template to obtain a time series of 90 brain regions. EEG power spectra were determined using Fieldtrip incorporating a Fast Fourier transform. FC was determined for all pairs of brain signals for theta band using debiased estimate of weighted phase-lag index (wPLI) in Fieldtrip. To explore group differences in wPLI, independent t-tests were performed with p < 0.05 to indicate statistical significance. False discovery rate (FDR) correction was used to adjust p-values. Results: The findings showed that amplitude induction by face pictures was higher compared with that of non-face pictures both in MDD and healthy control (HC) groups. Face recognition amplitude in MDD group was lower compared with that in the HC group. Two time periods with significant differences were then selected for further analysis. Analysis showed that FC was stronger in the MDD group compared with that in the HC group in most brain regions in both periods. However, only one FC between two brain regions in HC group was stronger compared with that in the MDD group. Conclusion: Dysfunction in brain FC among MDD patients is a relatively complex phenomenon, exhibiting stronger and multiple connectivity with several brain regions of emotions. The findings of the current study indicate that the brain FC of MDD patients is more complex and less efficient in the initial stage of face recognition.

The Role of Features Types and Personalized Assessment in Detecting Affective State Using Dry Electrode EEG

Assessing the human affective state using electroencephalography (EEG) have shown good potential but failed to demonstrate reliable performance in real-life applications. Especially if one applies a setup that might impact affective processing and relies on generalized models of affect. Additionally, using subjective assessment of ones affect as ground truth has often been disputed. To shed the light on the former challenge we explored the use of a convenient EEG system with 20 participants to capture their reaction to affective movie clips in a naturalistic setting. Employing state-of-the-art machine learning approach demonstrated that the highest performance is reached when combining linear features, namely symmetry features and single-channel features, with nonlinear ones derived by a multiscale entropy approach. Nevertheless, the best performance, reflected in the highest F1-score achieved in a binary classification task for valence was 0.71 and for arousal 0.62. The performance was 10–20% better compared to using ratings provided by 13 independent raters. We argue that affective self-assessment might be underrated and it is crucial to account for personal differences in both perception and physiological response to affective cues.

Emotion and Brain Oscillations: High Arousal is Associated with Decreases in Alpha- and Lower Beta-Band Power

The study of brain oscillations associated with emotional picture processing has revealed conflicting findings. Although many studies observed a decrease in power in the alpha- and lower beta band, some studies observed an increase. Accordingly, the main aim of the present research series was to further elucidate whether emotional stimulus processing is related to an increase or decrease in alpha/beta power. In Study 1, participants (N = 16) viewed briefly presented (150 ms) high-arousing erotic and low-arousing people pictures. Picture presentation included a passive viewing condition and an active picture categorization task. Study 2 (N = 16) replicated Study 1 with negative valence stimuli (mutilations). In Study 3 (N = 18), stimulus materials of Study 1 and 2 were used. The main finding is that high-arousing pictures (erotica and mutilations) are associated with a decrease of power in the alpha/beta band across studies and task conditions. The effect peaked in occipitoparietal sensors between 400 and 800 ms after stimulus onset. Furthermore, a late (>1000 ms) alpha/beta power increase to mutilation pictures was observed, possibly reflecting top-down inhibitory control processes. Overall, these findings suggest that brain oscillations in the alpha/beta-band may serve as a useful measure of emotional stimulus processing.

Improving performance in motor imagery BCI-based control applications via virtually embodied feedback

OBJECTIVE

Brain-computer interfaces (BCIs) based on motor imagery (MI) are commonly used for control applications. However, these applications require strong and discriminant neural patterns for which extensive experience in MI may be necessary. Inspired by the field of rehabilitation where embodiment is a key element for improving cortical activity, our study proposes a novel control scheme in which virtually embodiable feedback is provided during control to enhance performance.

METHODS

Subjects underwent two immersive virtual reality control scenarios in which they controlled the two-dimensional movement of a device using electroencephalography (EEG). The two scenarios only differ on whether embodiable feedback, which mirrors the movement of the classified intention, is provided. After undergoing each scenario, subjects also answered a questionnaire in which they rated how immersive the scenario and embodiable the feedback were.

RESULTS

Subjects exhibited higher control performance, greater discriminability in brain activity patterns, and enhanced cortical activation when using our control scheme compared to the standard control scheme in which embodiable feedback is absent. Moreover, the self-rated embodiment and presence scores showed significantly positive linear relationships with performance.

SIGNIFICANCE

The findings in our study provide evidence that providing embodiable feedback as guidance on how intention is classified may be effective for control applications by inducing enhanced neural activity and patterns with greater discriminability. By applying embodiable feedback to immersive virtual reality, our study also serves as another instance in which virtual reality is shown to be a promising tool for improving MI.

Perceived pleasantness of gentle touch in healthy individuals is related to salivary oxytocin response and EEG markers of arousal

Affective touch plays an important role in human social bonding, affiliative behavior, and in general emotional well-being. A system of unmyelinated low-threshold mechanosensitive C-type afferents innervating hairy skin (C-tactile or CT system) is postulated to provide the neurophysiological background of affective touch perception. C-tactile afferents respond optimally to soft and slow strokes, and this response correlates positively with pleasure ratings of tactile stimuli. As gentle touch is consistently associated with oxytocin release further promoting prosocial behavior, it has been suggested that this effect is mediated by the response of C-tactile afferents. This study assesses a possible link between CT-optimal touch, its subjective pleasantness, EEG indices of cortical arousal, and peripheral oxytocin response. EEG was recorded in 28 healthy volunteers during resting state and tactile stimulation[gentle slow brush strokes on forearm (CT-targeted touch) and palm (non-CT-targeted touch)]. Saliva samples were collected before and after the touch stimulation. Oxytocin concentration increase was significantly associated with greater subjective ratings of CT-targeted touch but not of non-CT-targeted touch, and with lower peak alpha frequency values indicating decreased cortical arousal. The findings suggest that CT-targeted stimulation triggers oxytocin release but only when the touch is perceived at an individual level as having clearly positive affective salience. This corresponds to previous studies reporting that oxytocin response to touch can be related to different personality factors, and bears important implications for planning touch-based interventions in social and medical care.

Neural dynamics of pain expression processing: Alpha-band synchronization to same-race pain but desynchronization to other-race pain

Both electroencephalography and functional magnetic resonance imaging studies have revealed enhanced neural responses to perceived pain in same-race than other-race individuals. However, it remains unclear how neural responses in the sensorimotor, cognitive, and affective subsystems vary dynamically in the first few hundreds of milliseconds to generate racial ingroup favoritism in empathy for pain. We recorded magnetoencephalography signals to pain and neutral expressions of Asian and white faces from Chinese adults during judgments of racial identity of each face. We found that pain compared to neutral expressions of same-race faces induced early increased alpha oscillations in the precuneus/parietal cortices followed by increased alpha-band oscillations in the left anterior insula and temporoparietal junction. Pain compared to neutral expressions of other-race faces, however, induced early suppression of alpha-band oscillations in the bilateral sensorimotor cortices and left insular cortex. Moreover, decreased functional connectivity between the left sensorimotor cortex and left anterior insula predicted reduced subjective feelings of other-race suffering. Our results unraveled distinct patterns of modulations of neural dynamics of sensorimotor, affective, and cognitive components of empathy by interracial relationships between an observer and a target person, which provide possible brain mechanisms for understanding racial ingroup favoritism in social behavior.

Affective Cortical Asymmetry at the Early Developmental Emergence of Emotional Expression

Emotions have an important survival function. Vast amounts of research have demonstrated how affect-related changes in physiology promote survival by effecting short-term and long-term changes in adaptive behavior. However, if emotions truly serve such an inherent function, they should be pervasive across species and be established early in life. Here, using electroencephalographic (EEG) brain activity we sought to characterize core neurophysiological features underlying affective function at the emergence of emotional expression [i.e., at the developmental age when human infants start to show reliable stimulus-elicited emotional states (4–6 months)]. Using an approach that eschews traditional EEG frequency band delineations (like theta, alpha), we demonstrate that negative emotional states induce a strong right hemispheric increase in the prominence of the resonant frequency (∼5–6 Hz) in the infant frontal EEG. Increased rightward asymmetry was strongly correlated with increased heart rate responses to emotionally negative states compared with neutral states. We conclude that functional frontal asymmetry is a key component of emotional processing and suggest that the rightward asymmetry in prominence of the resonant frequency during negative emotional states might reflect functional asymmetry in the central representation of anatomically driven asymmetry in the autonomic nervous system. Our findings indicate that the specific mode hallmarking emotional processing in the frontal cortex is established in parallel with the emergence of stable emotional states very early during development, despite the well known protracted maturation of frontal cortex.

Measuring Neural Arousal for Advertisements and Its Relationship With Advertising Success

Abundant research has established the important role of ad-evoked feelings on consumers' reaction to advertising. However, measurement of feelings through explicit self-report is not without its limitations. The current study adds to previous work by showing a sophisticated way of first estimating how arousal is represented in the brain via an independent task (using EEG), and thereafter using this representation to measure arousal in response to advertisements. We then estimate the relationship between the identified process (arousal) and external measures of ad effectiveness (as measured by notability and attitude toward the ad). The results show that the neural measure of arousal is positively associated with notability of ads in the population at large, but may be negatively associated with attitude toward these ads. The implications for the application of EEG in ad testing and for understanding the relationship between arousal and effective advertising are discussed.

Effectiveness of the level of personal relevance of visual autobiographical stimuli in the induction of positive emotions in young and older adults: pilot study protocol for a randomized controlled trial

BACKGROUND

The ability to retrieve specific memories is a cognitive and emotional protective factor. Among the most effective techniques to generate autobiographical memories is the use of audio-visual stimuli, particularly images. Developing and improving techniques that facilitate the generation of such memories could be highly effective in the prevention of depressive symptoms, especially in the elderly population. The aim of the present study is to examine how the level of personal relevance of pictures as autobiographical memory cues to induce positive emotions may affect an individual's emotion regulation.

METHODS

The participants, 120 older adults aged 65 and over and 120 young adults aged between 18 and 35, of both sexes and without depressive symptoms, will be induced to a negative mood state by means of viewing a film clip. Following the negative mood induction, the participants will be shown positive images according to experimental group to which they were randomly assigned (high personal relevance: personal autobiographical photographs; medium personal relevance: pictures of favourite locations associated with specific positive autobiographical memories; and low personal relevance: positive images from the International Affective Picture System). We will analyse the differences in subjective (responses to questionnaires) and objectives measures (EEG signal, heart rate variability and electrodermal activity) between the groups before and after the induction of negative affect and following the recall of positive memories.

DISCUSSION

The use of images associated with specific positive autobiographical memories may be an effective input for inducing positive mood states, which has potentially important implications for their use as a cognitive behavioural technique to treat emotional disorders, such as depression, which are highly prevalent among older adults.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04251104 . Registered on 30 January 2020.