This chapter reviews cross-cultural studies The study of emotions expressed by facial expression cannot be simply studied as isolated case studies through isolated instances of physiognomy, morphology, or cultural analytics For some theorists, universality is deeply revealing about the nature of emotion; for others, universality is a finding that must be accounted for by any viable theory of emotion The universality thesis is an idea that the Western psychologists find plausible, especially given randomness as the alternative Any studies of the universality thesis and other alternative theories like cultural relativism must aid in the design of more useful studies Rather than ask whether or not a given culture agrees with one preformulated hypothesis, it would be more useful to question how members of that culture conceptualize emotions and facial behavior

Is there universal recognition of emotion from facial expression? a review of the cross-cultural studies

Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

Human Emotion Recognition: Review of Sensors and Methods.

Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants’ emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

This paper reports on how emotional states elicited by affective sounds can be effectively recognized by means of estimates of Autonomic Nervous System (ANS) dynamics. Specifically, emotional states are modeled as a combination of arousal and valence dimensions according to the well-known circumplex model of affect, whereas the ANS dynamics is estimated through standard and nonlinear analysis of Heart rate variability (HRV) exclusively, which is derived from the electrocardiogram (ECG). In addition, Lagged Poincare Plots of the HRV series were also taken into account. The affective sounds were gathered from the International Affective Digitized Sound System and grouped into four different levels of arousal (intensity) and two levels of valence (unpleasant and pleasant). A group of 27 healthy volunteers were administered with these standardized stimuli while ECG signals were continuously recorded. Then, those HRV features showing significant changes (p $ 0.05 from statistical tests) between the arousal and valence dimensions were used as input of an automatic classification system for the recognition of the four classes of arousal and two classes of valence. Experimental results demonstrated that a quadratic discriminant classifier, tested through Leave-One-Subject-Out procedure, was able to achieve a recognition accuracy of 84.72 percent on the valence dimension, and 84.26 percent on the arousal dimension.

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Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability

Changes in EEG and autonomic nervous activity during meditation and their association with personality traits

Background The purpose of the current study was to examine the effectiveness of Matching Pursuit (MP) algorithm in emotion recognition. Methods Electrocardiogram (ECG) and galvanic skin responses (GSR) of 11 healthy students were collected while subjects were listening to emotional music clips. Applying three dictionaries, including two wavelet packet dictionaries (Coiflet, and Daubechies) and discrete cosine transform, MP coefficients were extracted from ECG and GSR signals. Next, some statistical indices were calculated from the MP coefficients. Then, three dimensionality reduction methods, including Principal Component Analysis (PCA), Linear Discriminant Analysis, and Kernel PCA were applied. The dimensionality reduced features were fed into the Probabilistic Neural Network in subject-dependent and subject-independent modes. Emotion classes were described by a two-dimensional emotion space, including four quadrants of valence and arousal plane, valence based, and arousal based emotional states. Results Using PCA, the highest recognition rate of 100% was achieved for sigma = 0.01 in all classification schemes. In addition, the classification performance of ECG features was evidently better than that of GSR features. Similar results were obtained for subject-dependent emotion classification mode. Conclusions An accurate emotion recognition system was proposed using MP algorithm and wavelet dictionaries.

An accurate emotion recognition system using ECG and GSR signals and matching pursuit method.

Analysis of lagged Poincaré plots in heart rate signals during meditation

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

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

Designing an efficient automatic emotion recognition system based on physiological signals has attracted great interests within the research of human–machine interactions. This study was aimed to classify emotional responses by means of a simple dynamic signal processing technique and fusion frameworks. The electrocardiogram and finger pulse activity of 35 participants were recorded during rest condition and when subjects were listening to music intended to stimulate certain emotions. Four emotion categories, including happiness, sadness, peacefulness, and fear were chosen. Estimating heart rate variability (HRV) and pulse rate variability (PRV), 4 Poincare indices in 10 lags were extracted. The support vector machine classifier was used for emotion classification. Both feature level (FL) and decision level (DL) fusion schemes were examined. Significant differences have been observed between lag 1 Poincare plot indices and the other lagged measures. The mean accuracies of 84.1, 82.9, 79.68, and 76.05% were obtained for PRV, DL, FL, and HRV measures, respectively. However, DL outperformed others in discriminating sadness and peacefulness, using SD1 and total features, correspondingly. In both cases, the classification rates improved up to 92% (with the sensitivity of 95% and specificity of 83.33%). Totally, DL resulted in better performances compared to FL. In addition, the impact of the fusion rules on the classification performances has been confirmed.

Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged poincare plots

Given the importance and abundant use of emotional recognition in the human–computer interfaces and multimedia applications, several methods for designing such systems have been reported in the literature. However, the performance of the photoplethysmography (PPG) signal in these devices has not been sufficiently studied. In addition, details of the phase space geometry of this signal have not been investigated in the emotional states up to now. In connection with this issue, Poincare's sections can quantify the geometric patterns of the trajectory in the high-dimensional phase space. Therefore, this work attempted to propose an automatic emotion recognizer which can characterize the PPG signals during the exposure of emotional music-video. Our focus was to detect and classify dynamical behaviors of the PPG trajectories in three emotional classes of love, hate, and fun using Poincare's section measures. To this effect, the 2D phase space of PPG was firstly reconstructed. Then, forming the Poincare's sections in different angles, some geometric indices were extracted. Finally, using support vector machine, the PPG measures were classified into emotional states. Our results showed that basin geometry of the PPG phase states was significantly different in different emotional states. The maximum accuracy rates of 96.67% and 91.11% were achieved for a binary and multi-class classification scheme, respectively. The proposed framework was fast and operated using the single-sensor signal. In conclusion, the dynamical Poincare's section indices of PPG signals during three emotional states paved the way for designing an online emotion recognition system.

Atefeh Goshvarpour

Papers

An accurate emotion recognition system using ECG and GSR signals and matching pursuit method.

Analysis of lagged Poincaré plots in heart rate signals during meditation

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

Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged poincare plots

Poincaré's section analysis for PPG-based automatic emotion recognition