Query: EEG music  Social cognition emotion

title: Frontal EEG alpha asymmetry and emotion: From neural underpinnings and methodological considerations to psychopathology and social cognition.
abstract: Forty years ago, in September of 1978, at the annual meeting of the Society for Psychophysiological Research inMadison,Wisconsin, the early career scientist Richie Davidson presented a paper suggesting that the experience of positive affect and of negative affect were associated with differently lateralized patterns of frontal brain electrical activity. Following the publication of the abstract of this presentation on frontal EEG asymmetry the next year (Davidson, Schwartz, Saron, Bennett, Goleman, 1979) there were almost no publications in the following decade, with only 15 empirical articles examining frontal EEG asymmetry and emotion by 1990. It might have been hard to predict at that time how popular this measure of frontal brain asymmetry would become, with now hundreds of articles published using frontal EEG asymmetry to examine emotion related and motivation related trait individual differences and staterelated changes. Among other topics, frontal EEG asymmetry has been used to investigate risk for depression, anxiety, and internalizing psychopathology, as well as externalizing disorders such as mania, addiction, and attention deficit hyperactivity disorder (ADHD) It has also been used extensively to examine individual differences in temperament and motivational style, and responses to emotional stimuli and social provocation. An advantage of frontal EEG asymmetry is its association with a highly successful conceptual model of emotion and motivation. The approach withdrawal model of frontal asymmetry posits that increased relative left frontal activity indicates a propensity to approach or engage a stimulus, whereas decreased relative left frontal activity indicates a propensity toward reduced approach motivation or increased withdrawal motivation (e.g. Coan Allen, 2004; Davidson, 1998; HarmonJones, 2003) Thus, frontal EEG asymmetry involves a unidimensional metric capturing large variations in motivation, emotion, and behavior. This metric is not only useful in understanding normative variation in motivation and emotion, but also abnormal variation, including mania and depression. Additionally, frontal asymmetry can be assessed as both a traitlike individual differences variable and as a measure of staterelated variation to particular stimuli or experimental paradigms (Coan Allen, 2004; Coan, Allen, McKnight, 2006)

title: EEG fMRI reveals rapid representation of personal relevance of faces in social cognition and reward related brain regions
abstract: Faces are a primary source of social information, but little is known about the sequence of neural processes that extract information from highly personally relevant faces, such as those of our loved ones. We applied representational similarity analyses to EEG fMRI measurement of neural responses to faces of personal relevance to participants their romantic partner and a friend compared to a stranger. Faces expressed fear, happiness or no emotion. Shared EEG fMRI representations started 100ms after stimulus onset not only in visual cortex, but also regions involved in social cognition, value representation and autobiographical memory, including ventromedial prefrontal cortex, temporoparietal junction and posterior cingulate. Representations in fusiform gyrus, amygdala, insular cortex and N. accumbens were evident after 200 ms. Representations related to romantic love emerged after 400ms in subcortical brain regions associated with reward. The results point to the prioritized processing of personal relevance with rapid and extensive cortical representation.

title: EEG Analysis of the Contribution of Music Therapy and Virtual Reality to the Improvement of Cognition in Alzheimer's Disease
abstract: Alzheimer's disease is the most common form of dementia, affecting nearly 9.9 million new people every year. The disease provokes important memory and cognitive impairment, eventually causing individuals to forget their loved ones and rendering them completely dependent on their caretakers. Alzheimer's patients typically experience more negative emotions, such as frustration and apathy, than healthy older adults. There is currently no cure for the disease. Our research group explores how the integration of virtual reality (VR) and an EEG based intelligent agent in music therapy can alleviate psychological and cognitive symptoms of the disease. We propose a theory explaining how, through activation of the brain reward system, music can reduce negative emotions, increase positive emotions and as a result increase performance on cognitive tasks. The results of our experimental study concord with our theory: emotional states of participants are improved, as per recorded through EEG, and performances on memory tasks show improvement following the intervention. We believe that the combination of EEG brain assessment, VR and music therapy is a promising method for emotional states and cognitive symptoms of Alzheimer's disease.

title: EEG based emotion recognition using minimum spanning tree
abstract: Emotion is a fundamental factor that influences human cognition, motivation, decision making and social interactions. This psychological state arises spontaneously and goes with physiological changes that can be recognized by computational methods. In this study, changes in minimum spanning tree (MST) structure of brain functional connectome were used for emotion classification based on EEG data and the obtained results were employed for interpretation about the most informative frequency content of emotional states. For estimation of interaction between different brain regions, several connectivity metrics were applied and interactions were calculated in different frequency bands. Subsequently, the MST graph was extracted from the functional connectivity matrix and its features were used for emotion recognition. The results showed that the accuracy of the proposed method for separating emotions with different arousal levels was 88.28% while for different valence levels it was 81.25% Interestingly, the system performance for binary classification of emotions based on quadrants of arousal valence space was also higher than 80% The MST approach allowed us to study the change of brain complexity and dynamics in various emotional states. This capability provided us enough knowledge to claim lower alpha and gamma bands contain the main information for discrimination of emotional states.

title: A double blind HD tDCS/EEG study examining right temporoparietal junction involvement in facial emotion processing
abstract: Prior studies have demonstrated that aspects of social cognition can be modulated via temporoparietal junction (TPJ) transcranial direct current stimulation (tDCS) However, this technique lacks focality and electrophysiological effects or correlates are rarely examined. The present study investigated whether anodal and/or cathodal high definition tDCS (HD tDCS) would influence facial emotion processing performance relative to sham stimulation, and whether task performance changes were related to neurophysiological changes. Participants completed a facial emotion attribution tasks before and after rTPJ HD tDCS, with event related potentials (ERP) recorded during task performance. Anodal rTPJ HD tDCS improved facial emotion processing performance for static depictions of fear (but not surprise) Stimulation condition influenced P300 latency, and also influenced the relationship between behavioural and electrophysiological (ERP) outcomes in several circumstances, findings which both support and challenge anodal excitation/cathodal inhibition accounts of tDCS effects. Results suggest that rTPJ anodal HD tDCS can influence facial emotion recognition (i.e. affective mentalizing) and elucidate the nature and distribution of underlying neurophysiological processes. Stimulation effects, however, might depend on the intensity and salience/valence (negativity/threat) of the emotion, and these behavioural effects may not relate directly or simply to the ERPs assessed here.

title: Electroencephalogram of Happy Emotional Cognition Based on Complex System of Music and Image Visual and Auditory
abstract: People are ingesting various information from different sense organs all the time to complete different cognitive tasks. The brain integrates and regulates this information. The two significant sensory channels for receiving external information are sight and hearing that have received extensive attention. This paper mainly studies the effect of music and visual auditory stimulation on electroencephalogram (EEG) of happy emotion recognition based on a complex system. In the experiment, the presentation was used to prepare the experimental stimulation program, and the cognitive neuroscience experimental paradigm of EEG evoked by happy emotion pictures was established. Using 93 videos as natural stimuli, fMRI data were collected. Finally, the collected EEG signals were removed with the eye artifact and baseline drift, and the t test was used to analyze the significant differences of different lead EEG data. Experimental data shows that, by adjusting the parameters of the convolutional neural network, the highest accuracy of the two classification algorithm can reach 98.8% and the average accuracy can reach 83.45% The results show that the brain source under the combined visual and auditory stimulus is not a simple superposition of the brain source of the single visual and auditory stimulation, but a new interactive source is generated.

title: A neuroimaging point of view on the diversity of social cognition: evidence for extended influence of experience and emotion related factors on face processing
abstract: Faces are key social stimuli that convey a wealth of information essential for person perception and adaptive interpersonal behaviour. Studies in the domain of cognitive, affective, and social neuroscience have put in light that the processing of faces recruits specific visual regions and activates a distributed set of brain regions related to attentional, emotional, social, and memory processes associated with the perception of faces and the extraction of the numerous information attached to them. Studies using neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have allowed localizing these brain regions and characterizing their functional properties. Magnetoencephalography (MEG) and electroencephalography (EEG) techniques are complementary to fMRI in that they offer a unique insight into the temporal dynamics of mental processes. In this article, I review the contribution of neuroimaging techniques to the knowledge on face processing and person perception with the aim of putting in light the extended influence of experience related factors, particularly in relation with emotions, on the face processing system. Although the face processing network has evolved under evolutionary selection pressure related to sociality related needs and is therefore highly conserved throughout the human species, neuroimaging studies put in light both the extension and the flexibility of the brain network involved in face processing. MEG and EEG allow in particular to reveal that the human brain integrates emotion and experience related information from the earliest stage of face processing. Altogether, this emphasizes the diversity of social cognitive processes associated with face perception.

title: EEG Based Emotion Recognition Using Hierarchical Network With Subnetwork Nodes
abstract: Emotions play a crucial role in decision making, brain activity, human cognition, and social intercourse. This paper proposes a hierarchical network structure with subnetwork nodes to discriminate three human emotions: 1) positive; 2) neutral; and 3) negative. Each subnetwork node embedded in the network that are formed by hundreds of hidden nodes, could be functional as an independent hidden layer for feature representation. The top layer of the hierarchical network, like the mammal cortex in the brain, combine such features generated from subnetwork nodes, but simultaneously, recast these features into a mapping space so that the network can be performed to produce more reliable cognition. The proposed method is compared with other state of the art methods. The experimental results from two different EEG datasets show that a promising result is obtained when using the proposed method with both single and multiple modality.

title: Deep Learning Based on CNN for Emotion Recognition Using EEG Signal
abstract: Emotion recognition based on brain computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13% The results showed excellent classification ability of the model and can improve emotion recognition.

title: Deep Learning Based on CNN for Emotion Recognition Using EEG Signal
abstract: Emotion recognition based on brain computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13% The results showed excellent classification ability of the model and can improve emotion recognition.  BCI, EEG, Deep learning (DL) CNN, ResNet50, Emotion recognition, Adam optimizer,