Distinct Emotional States Research Articles

Suicidal Ideation Among Older Adults in Prison: A Qualitative Study.

Suicide is a serious public health concern, and people who are incarcerated represent a particularly high-risk group. Although research on the suicidality of persons in prison has gained interest in recent decades, the issue of suicide among older adults in prison has been understudied. Therefore, the aim of the present study was to explore the ways in which older adults in prison understood their experiences of suicidal ideation. Interpretive phenomenological analysis was utilized to analyze interviews with 16 incarcerated older adults. The analysis of the findings revealed four themes: (1) Suicide intentions as a control strategy; (2) Suicide intentions as an act of resistance; (3) Suicide intentions as a means of self-redemption, and (4) Suicide intentions in the absence of other choices. Guided by the life course perspective, the findings suggest that suicidal ideation serves as a means of expressing distinct emotional states that may be unique to older adults in carceral environments.

Customers’ emotional impact on star rating and thumbs-up behavior towards food delivery service Apps

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

Analysis of EEG Fluctuation Patterns Using Nonlinear Phase-Based Functional Connectivity Measures for Emotion Recognition

Automated emotion recognition is crucial in identifying and monitoring psychological disorders. Although several electroencephalography (EEG)-based methods have been explored for emotion recognition, capturing the subtle oscillations within EEG signals associated with distinct emotional states remains a persistent challenge. Nonlinear phase-based functional connectivity (FC) can capture the intricate time-varying patterns of brain activity during the processing of emotions. In this work, an attempt has been made to characterize the EEG-based emotional states using nonlinear phase-based FC techniques. For this, the EEG signals are obtained from the publicly available DEAP database and decomposed into four frequency bands: Theta (4–7[Formula: see text]Hz), alpha (8–12[Formula: see text]Hz), beta (13–30[Formula: see text]Hz) and gamma (30–45[Formula: see text]Hz). Three nonlinear phase-based FC measures, namely phase lag index (PLI), phase locking value (PLV) and Rho index, are extracted from individual frequency bands. Two types of features, namely network features and FC indices, are fed to three classifiers, namely random forest (RF), extreme gradient boosting (XGB) and K-Nearest Neighbors (KNN). The results reveal that the proposed approach can capture EEG dynamics to characterize emotional states. The gamma band-based Rho index demonstrated prominence in discriminating arousal and valence emotional states. The utilization of the Rho index-based FC feature effectively reveals interactions among cortical brain regions in response to audio-visual stimuli. Thus, the proposed approach could be extended to classifying various emotional states in clinical settings.

Unveiling Emotional Stress: Analyzing EEG Signals With Deep Learning Insights

This study focuses on a comprehensive exploration of EEG-based emotion recognition, with a particular focus on leveraging recurrent neural network (RNN) architectures, including long short-term memory (LSTM) and gated recurrent unit (GRU). The investigation is rooted in a publicly available EEG Brainwave Dataset, meticulously curated to capture human responses to emotional stimuli. The dataset features signals obtained from frontal and temporal brain lobes, categorized into distinct emotional states encompassing positive, neutral, and negative emotions. The analysis spans four distinct scenarios, each representing different combinations of features and preprocessing strategies. Notably primary emphasis is on LSTM and GRU architectures due to their proven effectiveness in sequential data processing tasks. In addition to evaluating the performance of LSTM and GRU models a thorough examination of all the four cases to assess their efficacy in accurately predicting emotional states from EEG signals. The study also emphasis on the importance of meticulous feature selection and preprocessing in optimizing model accuracy and robustness, highlighting the intricate interplay between data representation, model architecture, and task-specific requirements. Ultimately, the research findings contribute to advanced understanding of EEG-based emotion recognition and pave the way for further research in this exciting interdisciplinary field. Key Words: Electroencephalography(EEG), Long short-term memory (LSTM), Gated recurrent unit (GRU), Feature extraction, Preprocessing techniques, Statistical metrics, Frequency domain analysis.

Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study.

The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification. The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs). Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states. In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.

ELINA: Emotion low-resources INference algorithm based on 1D ConvNets

In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven distinct emotional states. Electrocardiogram (ECG) and galvanic skin response (GSR) biosignals were collected and classified using two deep learning models, BEC-1D and ELINA, along with two different preprocessing techniques, a classical fourier-based filtering and an Empirical Mode Decomposition (EMD) approach. For the single-modal set, this proposal achieved an accuracy of 84.43±30.03, precision of 85.16±28.91, and F1-score of 84.06±29.97. Moreover, in the extended configuration the model maintained strong performance, yielding scores of 80.95±22.55, 82.44±24.34, and 79.91±24.55, respectively. Notably, for the multi-modal set (B-12sbj), the best results were obtained with EMD preprocessing and the ELINA model. This proposal achieved an improved accuracy, precision, and F1-score scores of 98.02±3.78, 98.31±3.31, and 97.98±3.83, respectively, demonstrating the effectiveness of this approach in discerning emotional states from biosignals.

Development of an advanced ai-based model for human psychoemotional state analysis

The research focuses on developing a novel method for the automatic recognition of human psychoemotional states (PES) using deep learning technology. This method is centered on analyzing speech signals to classify distinct emotional states. The primary challenge addressed by this research is to accurately perform multiclass classification of seven human psychoemotional states, namely joy, fear, anger, sadness, disgust, surprise, and a neutral state. Traditional methods have struggled to accurately distinguish these complex emotional nuances in speech. The study successfully developed a model capable of extracting informative features from audio recordings, specifically mel spectrograms and mel-frequency cepstral coefficients. These features were then used to train two deep convolutional neural networks, resulting in a classifier model. The uniqueness of this research lies in its use of a dual-feature approach and the employment of deep convolutional neural networks for classification. This approach has demonstrated high accuracy in emotion recognition, with an accuracy rate of 0.93 in the validation subset. The high accuracy and effectiveness of the model can be attributed to the comprehensive and synergistic use of mel spectrograms and mel-frequency cepstral coefficients, which provide a more nuanced analysis of emotional expressions in speech. The method presented in this research has broad applicability in various domains, including enhancing human-machine interface interactions, implementation in the aviation industry, healthcare, marketing, and other fields where understanding human emotions through speech is crucial

A simplified PPG based approach for automated recognition of five distinct emotional states

A simplified PPG based approach for automated recognition of five distinct emotional states

Machine learning-based model for customer emotion detection in hotel booking services

PurposeThe purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods, and build a model to identify customer emotions.Design/methodology/approachThe study proposed a customer emotion detection model and data mining method based on the collected dataset, including 80,593 online reviews on agoda.com and booking.com from 2009 to 2022.FindingsBy discerning specific emotions expressed in customers' comments, emotion detection, which refers to the process of identifying users' emotional states, assumes a crucial role in evaluating the brand value of a product. The research capitalizes on the vast and diverse data sources available on hotel booking websites, which, despite their richness, remain largely unexplored and unanalyzed. The outcomes of the model, pertaining to the detection and classification of customer emotions based on ratings and reviews into four distinct emotional states, offer a means to address the challenge of determining customer satisfaction regarding their actual service experiences. These findings hold substantial value for businesses operating in this domain, as the findings facilitate the evaluation and formulation of improvement strategies within their business models. The experimental study reveals that the proposed model attains an exact match ratio, precision, and recall rates of up to 81%, 90% and 90%, respectively.Research limitations/implicationsThe study has yet to mine real-time data. Prediction results may be influenced because the amount of data collected from the web is insufficient and preprocessing is not completely suppressed. Furthermore, the model in the study was not tested using all algorithms and multi-label classifiers. Future research should build databases to mine data in real-time and collect more data and enhance the current model.Practical implicationsThe study's results suggest that the emotion detection models can be applied to the real world to quickly analyze customer feedback. The proposed models enable the identification of customers' emotions, the discovery of customer demand, the enhancement of service, and the general customer experience. The established models can be used by many service sectors to learn more about customer satisfaction with the offered goods and services from customer reviews.Social implicationsThe research paper helps businesses in the hospitality area analyze customer emotions in each specific aspect to ensure customer satisfaction. In addition, managers can come up with appropriate strategies to bring better products and services to society and people. Subsequently, fostering the growth of the hotel tourism sector within the nation, thereby facilitating sustainable economic development on a national scale.Originality/valueThis study developed a customer emotions detection model for detecting and classifying customer ratings and reviews as 4 specific emotions: happy, angry, depressed and hopeful based on online booking hotel websites agoda.com and booking.com that contains 80,593 reviews in Vietnamese. The research results help businesses check and evaluate the quality of their services, thereby offering appropriate improvement strategies to increase customers' satisfaction and demand more effectively.

Deep Learning-Based Emotion Recognition through Facial Expressions

Nowadays, humans can communicate easily with others by recognizing speech and text characters, particularly facial expressions. In human communication, it is critical to comprehend their emotion or implicit expression. Indeed, facial expression recognition is vital for analyzing the emotions of conversation partners, which can contribute to a series of matters, including mental health consulting. This technique enables psychiatrists to select appropriate questions based on their patients’ current emotional state. The purpose of this study was to develop a deep learningbased model for detecting and recognizing emotions on human faces. We divided the experiment into two parts: Faster R-CNN and mini-Xception architecture. We concentrated on four distinct emotional states: angry, sad, happy, and neutral. Both models implemented using the Faster R-CNN and the mini-Xception architectures were compared during the evaluation process. The findings indicate that the mini-Xception architecture model produced a better result than the Faster R-CNN. This study will be expanded in the future to include the detection of complex emotions such as sadness.

Affective Computing Based on Morphological Features of Photoplethysmography for Patients with Hypertension.

Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with essential hypertension were measured for blood pressure and PPG signals under baseline and four emotional conditions (neutral, anger, happiness, and sadness), and the PPG signals were transformed into the mean standard deviation of five PPG waveform indices. A support vector machine was used as a classifier. The performance of the classifier was verified by using resubstitution and six-fold cross-validation (CV) methods. Feature selectors, including full search and genetic algorithm (GA), were used to select effective feature combinations. Traditional statistical analyses only differentiated between the emotional states and baseline, whereas AC achieved 100% accuracy in distinguishing between the emotional states and baseline by using the resubstitution method. AC showed high accuracy rates when used with 10 waveform features in distinguishing the records into two, three, and four classes by applying a six-fold CV. The GA feature selector further boosted the accuracy to 78.97%, 74.22%, and 67.35% in two-, three-, and four-class differentiation, respectively. The proposed AC achieved high accuracy in categorizing PPG records into distinct emotional states with features extracted from only five waveform indices. The results demonstrated the effectiveness of the five indices and the proposed AC in patients with hypertension.

Measuring distinct emotional states implicitly: The role of response speed.

Affective science offers many self-report measures, but implicit measures of multiple distinct emotional states are lacking. Prior research (Bartoszek & Cervone, 2017) initiated the development of such an assessment method by examining whether ratings of the emotional content of abstract images reveal raters' emotional states. The current studies were designed to determine whether the speed of these ratings is key to the validity of an implicit emotion measure. To this end, Study 1 exploited naturally occurring variations in response times, whereas Study 2 used time pressure in responding to the implicit measure. Both studies featured a fear-induction and revealed that implicitly assessed fear correlated with psychophysiological (Study 1) and behavioral (Studies 1 and 2) responses even when controlling for self-reported fear. Importantly, results supported the construct, criterion, and incremental validity of the implicit measure only among participants who responded quickly. Study 3 employed a sadness-induction and an experimental manipulation of response times using fast- and slow-paced conditions. The emotion induction affected fast, but not slow, responses to the implicit measure. Overall, findings highlight the importance of response speed in implicit emotion assessment and suggest that the Implicit Measure of Distinct Emotional States can validly differentiate among emotions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Normal Inverse Gaussian Features for EEG-Based Automatic Emotion Recognition

Electroencephalography (EEG)-based emotion recognition is crucial in the domain of Human-Computer Interaction (HCI), which gained significant attention in recent years. However, the non-stationarity and chaotic nature of the EEG signals pose challenges and restrict the state-of-the-art techniques from precisely identifying distinct emotional states from the EEG data and hence offer limited emotion recognition performance. To capture the underlying non-linear characteristics of EEG, this study employed a novel local mean decomposition (LMD) algorithm which decomposes EEG signals into product functions (PFs). Further PFs are modeled by Normal Inverse Gaussian (NIG) probability density function (PDF) parameters. Thus these PDF features are fed to an optimized Adaboost classifier developed with the help of a cross-validation approach. The novelty of the work lies in the NIG modeling of LMD domain PFs to identify specific emotions from the EEG signals. The significance of the NIG parameters is illustrated by qualitative, pictorial, and statistical analyses. To assess the efficiency of the proposed approach, intensive experiments are conducted on open-source datasets, SJTU Emotion EEG Dataset (SEED), SEED-IV, and Database for Emotion Analysis of Physiological Signals (DEAP). The emotion recognition performance is evaluated in terms of heat maps, receiver operating characteristics (ROC), and accuracy. The proposed emotion recognition system outperformed the state-of-art methods and achieved a maximum accuracy of 97.3%, 98%, and 98.6% with the cross-validation approach and 93.23%, 94.87%, and 95.58% for the cross-subject validation approach using DEAP and SEED, SEED-IV datasets, respectively.

Spectral Characteristics of EEG during Active Emotional Musical Performance.

The research on neural correlates of intentional emotion communication by the music performer is still limited. In this study, we attempted to evaluate EEG patterns recorded from musicians who were instructed to perform a simple piano score while manipulating their manner of play to express specific contrasting emotions and self-rate the emotion they reflected on the scales of arousal and valence. In the emotional playing task, participants were instructed to improvise variations in a manner by which the targeted emotion is communicated. In contrast, in the neutral playing task, participants were asked to play the same piece precisely as written to obtain data for control over general patterns of motor and sensory activation during playing. The spectral analysis of the signal was applied as an initial step to be able to connect findings to the wider field of music-emotion research. The experimental contrast of emotional playing vs. neutral playing was employed to probe brain activity patterns differentially involved in distinct emotional states. The tasks of emotional and neutral playing differed considerably with respect to the state of intended-to-transfer emotion arousal and valence levels. The EEG activity differences were observed between distressed/excited and neutral/depressed/relaxed playing.

Spectrum‐dependent photonic synapses based on 2D imine polymers for power‐efficient neuromorphic computing

Abstract2D polymers (2DPs) have attracted increasing interests in sensors, catalysis, and gas storage applications. Furthermore, 2DPs with unique band structure and tunable photophysical properties also have immense potential for application in photonic neuromorphic computing. Here, photonic synaptic transistors based on 2DPs as the light‐tunable charge‐trapping medium are developed for the first time. The resulted organic transistors can successfully emulate common synaptic functions, including excitatory postsynaptic current, pair‐pulse facilitation, the transition of short‐term memory to long‐term memory, and dynamic filtering. Benefitting from the high photosensitivity of the 2DP, the devices can be operated under a low operating voltage of −0.1 V, and achieve an ultralow energy consumption of ~0.29 pJ per event. In addition, the heterostructure formed between the 2DP and organic semiconductor enables spectrum‐dependent synaptic responses, which facilitates the simulation of visual learning and memory processes in distinct emotional states. The underlying mechanism of spectrum‐dependent synaptic‐like behaviors is systematically validated with in situ atomic force microscopy based electrical techniques. The spectrum‐enabled tunability of synaptic behaviors further promotes the realization of optical logic functions and associative learning. This work inspires the new application of 2DPs in photonic synapses for future neuromorphic computing.image

A Novel Method for Gender and Age Detection Based on EEG Brain Signals

This paper presents a new gender and age classification system based on Electroencephalography (EEG) brain signals. First, Continuous Wavelet Transform (CWT) technique is used to get the time-frequency information of only one EEG electrode for eight distinct emotional states instead of the ordinary neutral or relax states. Then, sequential steps are implemented to extract the improved grayscale image feature. For system evaluation, a three-fold-cross validation strategy is applied to construct four different classifiers. The experimental test shows that the proposed extracted feature with Convolutional Neural Network (CNN) classifier improves the performance of both gender and age classification, and achieves an average accuracy of 96.3% and 89% for gender and age classification, respectively. Moreover, the ability to predict human gender and age during the mood of different emotional states is practically approved.

Modeling adaptive E-Learning environment using facial expressions and fuzzy logic

The integration of two or more intelligent systems, such as deep neural networks and fuzzy techniques, results in so-called hybrid intelligent systems. These have recently attracted considerable attention owing to their broad success in several complex real-world applications. An interesting example is the development of adaptive computer-based learning environments, in which learner responses to certain questions are considered so that the next level of the learning process may be determined. However, these methods fail to capture the behavior and emotional expressions of the learner during the learning process. Capturing these could make the learning flow more adaptive, and could allow the learning environment to redirect learners to different learning paths based on their capabilities and interaction levels. In this regard, the contribution of the present study is threefold. First, it proposes an approach for modeling an intelligent adaptive e-learning environment by considering the integration of the learner responses to questions and their emotional states. In the proposed approach, a loosely coupled integration between a convolutional neural network (CNN) and a fuzzy system is adopted. The CNN is used to detect a learner’s facial expressions, and outperforms other CNN models on the same training benchmark. The fuzzy system is used to determine the next learning level based on the extracted facial expression states from the CNN and several response factors by the learner. Second, the study introduces methods whereby a group of facial expressions is aggregated into a single representative. Third, it introduces corpora for evaluating the performance of the proposed approach. The corpora of 12 learners contain 72 learning activities and 1735 data points of distinct emotional states. The experimental results using these corpora demonstrate that the proposed approach provides adaptive learning flows that match the learning capabilities of all learners in a group. Moreover, the approach allows decision makers to monitor the learning performance for each learner.

Distinct affective responses to second- and third-party norm violations

Social norm violations provoke strong emotional reactions that often culminate in punishment of the wrongdoer. This is true not only when we are the victims of the norm violation (second-party), but also when witnessing a complete stranger being victimized (third-party). What remains unclear, however, is whether second- and third-party punishments are associated with different emotions. To address this question, here we examine how subjects respond affectively to both second- and third-party norm violations in an economic game. Our results indicate that while second- and third-parties respond to norm violations by punishing wrongdoers similarly, they report experiencing distinct emotional states as a result of the violation. Specifically, we observed a cross-over interaction between anger and moral outrage depending on the party's context: while anger was more frequently reported for second- than for third-party violations, moral outrage was more evoked by third-party than second-party violations. Disgust and sadness were the most prevalently reported emotions, but their prevalence were unaffected by party contexts. These results indicate that while responses to second- and third-party violations result in similar punishment, they are associated with the expression of distinct affective palettes. Further, our results provide additional evidence that moral outrage is a critical experience in the evaluation of third-party wrongdoings.

Final Ritardandi and the Expression of Musical Emotion

According to Juslin (2001), final ritardandi constitute part of the acoustic code employed by musicians to communicate two distinct emotional states: sadness and tenderness. To test this proposition, in two experiments, participants were exposed to a set of hymns that were modified in mode and/or tempo to primarily express either happiness, sadness, or tenderness. In addition, the inclusion of final ritardandi was experimentally manipulated such that these timing variations were either present or absent in the hymn stimuli. Participants were then asked to rate the emotions expressed by each variant of the hymns. In line with Juslin (2001), results revealed that when final ritardandi were included, expressively sad music was perceived as conveying more sadness, whereas expressively tender music was perceived as conveying more tenderness. Inclusion of ritardandi did not heighten the expression of happiness in music that was in a major key nor promote the expression of tenderness in music that was in a minor key. This suggests that final ritardandi do not generally heighten emotional expressivity and only amplify the emotional message already established by other cues, particularly those based on mode and overall tempo.

The influence of color on impulsiveness and arousal: Part 1 – Hue

AbstractImpulsiveness and arousal are two similar yet distinct emotional and behavioral states that have been shown to be influenced by color in many fields, such as clothing and environment design. This series of studies investigates the fundamental theory of how color impacts on people's impulsiveness and arousal states. To achieve this goal, psychophysical methods were used to examine the impact of color environment on impulsiveness and arousal. Two main factors were used to quantify them: response time and error rate in problem solving. The psychophysical experiment was designed to examine whether, in a particular color environment, the response time and error rate were different. Participants sat in front of a large TV screen showing different colored backgrounds against which a range of psychometric tests were completed. The color backgrounds varied in hue. During the experiment, participants were required to give their responses to each of the psychometric tests as quickly and accurately as possible. The results showed that different colors can significantly influence response time and error rate, which suggests that color has a distinct influence on impulsiveness and arousal. Gender difference has also been investigated. This article mainly discusses hue influence on impulsiveness and arousal. The influence of chroma along with hue will be explored in the next article.