A Review on EEG Signals Based Emotion Recognition

Emotion recognition has become a very controversial issue in brain-computer interfaces (BCIs). Moreover, numerous studies have been conducted in order to recognize emotions. Also, there are several important definitions and theories about human emotions. In this paper we try to cover important topics related to the field of emotion recognition. We review several studies which are based on analyzing electroencephalogram (EEG) signals as a biological marker in emotion changes. Considering low cost, good time and spatial resolution, EEG has become very common and is widely used in most BCI applications and studies. First, we state some theories and basic definitions related to emotions. Then some important steps of an emotion recognition system like different kinds of biologic measurements (EEG, electrocardiogram [EEG], respiration rate, etc), offline vs online recognition methods, emotion stimulation types and common emotion models are described. Finally, the recent and most important studies are reviewed

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Advanced EEG Signal Based Min to Mean Algorithm Approach For Human Emotion Taxonomy And Mental State Analysis

With electroencephalography (EEG) brain waves alone, it is full-scale phenomena in the field of computer-brain interface DNN, CNN, and SVM have improved detection and prediction accuracy in a number of researches during the last several years. But when it comes to recognizing global reliance, both deep learning and SVM have obvious limits. Pre-processing, extraction capabilities, and network design are the most common techniques used in deep learning models today, yet they are still unable to produce reliable results in noisy and sparse datasets. Any dataset, no matter how little or large, may suffer from poor SVM performance due to overlapping target instructions and boundaries. There are many different sorts of emotions that may be classified using the particular approach employed in this research. In order to get a whole picture of a person's mental state, it is best to use a "Min of mean” proposed technique. After comparison to the referential mean, a feeling is divided into one of four emotional quadrants. The MIN Max range is used to further split the emotion into 12 subcategories based on the amount of arousal. The proposed set of rules performed better than existing methods. Research on multi-class emotion reputation has shown that, compared to more recent studies, the proposed technique may be rather strong. It is possible to analyze a person's mental health by using the emotional spectrum, which has an accuracy rate of above 90%

Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network

Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion classification and feature extraction. Furthermore, we feed the selected features to a grouped ESN for recognizing emotions. Our approach is validated on the well-known DEAP benchmark, which includes the EEG data from 32 participants. The proposed model achieved 89.32% accuracy for H/L Arousal and 91.21% accuracy for H/L Valence on the DEAP dataset. The obtained results demonstrate the effectiveness of our approach, which yields good performance compared to existing models of emotion analysis based on EEG

Expressive and response dimensions of human emotion.

This thesis is about the neural mechanisms that underpin the expression of emotion in the human face and emotional modulation of behavioural responses. I designed 5 integrated studies and used functional magnetic resonance imaging (fMRI) to address specifically the neural mechanisms underlying human facial expression and emotional response. This work complements studies of emotion perception and subjective affective experience to provide a more comprehensive understanding of human emotions. I examined the neural underpinnings of emotional facial expression in three studies. I first demonstrated that emotional (compared to non-emotional) facial expression is not a purely motoric process but engages affective centres, including amygdala and rostral cingulate gyrus. In a second study I developed the concept of emotion contagion to demonstrate and verify a new interference effect (emotion expression interference, EEI). There is a cost (in reaction time and effort) to over-riding pre-potent tendency to mirror the emotional expressions of others. Several neural centres supporting EEI were identified (inferior frontal gyrus, superior temporal sulcus and insula), with their activity across subject predicting individual differences in personal empathy and emotion regulation. In a third study I examined an interesting phenomenon in our daily social life: how our own emotional facial expressions influence our judgment of the emotional signals of other people I explored this issue experimentally to examine the behavioural and neural consequences of posing positive (smiling) and negative (frowning) emotional expressions on judgments of perceived facial expressions. Reciprocal interactions between an emotion centre (amygdala) and a social signal processing region (superior temporal sulcus) were quantified. My analysis further revealed that the biasing of emotion judgments by one's own facial expression works through changes in connectivity between posterior brain regions (specifically from superior temporal sulcus to post-central cortex). I further developed two versions of an emotion GO/NOGO task to probe the impact of affective processing on behavioural responses. GO represents response execution and NOGO represents response inhibition. I therefore investigated how different emotions modulate both these complementary response dimensions (i.e. execution and inhibition). This research line is pertinent to a major theme within emotion theory, in which emotion is defined in terms of response patterns (e.g. approach and withdrawal). My results confirmed that both emotional processing and induced emotional states have robust modulatory effects on neural centres supporting response execution and response inhibition. Importantly, my results argue for emotion as a context for response control. My work extends our understanding of human emotion in terms of the nature and effect of its expression and its influence on response system