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This study investigates the use of electroencephalography (EEG) to characterize emotions and provides insights into the consistency between self-reported and machine learning outcomes. Thirty participants engaged in five virtual reality environments designed to elicit specific emotions, while their brain activity was recorded. The participants self-assessed their ground truth emotional state in terms of Arousal and Valence through a Self-Assessment Manikin. Gradient Boosted Decision Tree was adopted as a classification algorithm to test the EEG feasibility in the characterization of emotional states. Distinctive patterns of neural activation corresponding to different levels of Valence and Arousal emerged, and a noteworthy correspondence between the outcomes of the self-assessments and the classifier suggested that EEG-based affective indicators can be successfully applied in emotional characterization, shedding light on the possibility of using them as ground truth measurements. These findings provide compelling evidence for the validity of EEG as a tool for emotion characterization and its contribution to a better understanding of emotional activation.

Accurately measuring children’s emotion reactivity and regulation is important both for advancing theoretical understanding of child development and for identifying and monitoring children who have difficulties with emotional competence. Children have a uniquely important perspective on their own internal experiences of emotion reactivity and regulation that may differ from observers. However, there are few child-report measures of emotion reactivity and regulation. The goal of the present study was to develop and assess the psychometric properties of a child self-report measure of emotion reactivity and regulation (Youth Emotions Scale; YES) for elementary school-aged children. Examination of psychometric properties in a sample of 277 children (5 to 12 years old) oversampled for risk for emotional difficulties indicated good validity and reliability. Factor analyses indicated two theory-consistent factors (Reactivity and Emotion Regulation Strategies), both of which showed convergent and concurrent validity based on parent and child self-report of related constructs. This scale has both clinical and research utility, as having a brief measure of both emotion reactivity and regulation can aid clinicians and researchers in attaining relevant information without overtaxing children with multiple long measures. Future research should explore psychometric properties of the YES across different samples with different characteristics; if its validity continues to be supported, clinicians and researchers can utilize the YES to capture children’s internal experiences of emotion reactivity and regulation in treatment and research.

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assess students’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis, thereby continuously promoting the improvement of teaching quality. However, most existing multi-face expression recognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance, and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single face images, which are of low quality and lack specificity, also restricting the development of this research. This paper aims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable for smart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and apply the model to group emotion assessment to expand its application value. To this end, we propose an end-to-end multi-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide high-quality and highly targeted data support for model research, we constructed a multi-face expression dataset in real classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smart classrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG) block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability; combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention), it strengthens the ability to extract key information; adopts asymptotic feature pyramid network (AFPN) feature fusion tailored to classroom scenes and optimizes the head prediction output size; achieves high-performance end-to-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessment and provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED show that the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively, improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC model has obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-time multi-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.

Background: The assessment of emotion recognition holds growing significance in research on the brain–computer interface and human–computer interaction. Among diverse physiological signals, electroencephalography (EEG) occupies a pivotal position in affective computing due to its exceptional temporal resolution and non-invasive acquisition. However, EEG signals are inherently complex, characterized by substantial noise contamination and high variability, posing considerable challenges to accurate assessment. Methods: To tackle these challenges, we propose a Triple Attention Network (TANet), a triple-attention EEG emotion recognition framework that integrates Conformer, Convolutional Block Attention Module (CBAM), and Mutual Cross-Modal Attention (MCA). The Conformer component captures temporal feature dependencies, CBAM refines spatial channel representations, and MCA performs cross-modal fusion of differential entropy and power spectral density features. Results: We evaluated TANet on two benchmark EEG emotion datasets, DEAP and SEED. On SEED, using a subject-specific cross-validation protocol, the model reached an average accuracy of 98.51 ± 1.40%. On DEAP, we deliberately adopted a segment-level splitting paradigm—in line with influential state-of-the-art methods—to ensure a direct and fair comparison of model architecture under an identical evaluation protocol. This approach, designed specifically to assess fine-grained within-trial pattern discrimination rather than cross-subject generalization, yielded accuracies of 99.69 ± 0.15% and 99.67 ± 0.13% for the valence and arousal dimensions, respectively. Compared with existing benchmark approaches under similar evaluation protocols, TANet delivers substantially better results, underscoring the strong complementary effects of its attention mechanisms in improving EEG-based emotion recognition performance. Conclusions: This work provides both theoretical insights into multi-dimensional attention for physiological signal processing and practical guidance for developing high-performance, robust EEG emotion assessment systems.

In this paper, the development process and validation of a self-assessment emotion tool (SAET) is described, which establishes an emotion-assessment method to improve pictorial expression design. The tool is based on an emotion set of emotional-cognition-derived rules obtained from an OCC model proposed by Ortony, Clore, and Collins, and the emotion set and expression design are validated by numerical computation of the dimensional space pleasure–arousal–dominance (PAD) and the cognitive assessment of emotion words. The SAET consists of twenty images that display a cartoon figure expressing ten positive and ten negative emotions. The instrument can be used during interactions with visual interfaces such as websites, posters, cell phones, and vehicles, and allows participants to select interface elements that elicit specific emotions. Experimental results show the validity of this type of tool in terms of both semantic discrimination of emotions and quantitative numerical validation.

Background Schizophrenia is a neurological disease characterized by alterations to patients’ cognitive functions and emotional expressions. Relevant studies often use magnetic resonance imaging (MRI) of the brain to explore structural differences and responsiveness within brain regions. However, as this technique is expensive and commonly induces claustrophobia, it is frequently refused by patients. Thus, this study used non-contact infrared thermal facial images (ITFIs) to analyze facial temperature changes evoked by different emotions in moderately and markedly ill schizophrenia patients. Methods Schizophrenia is an emotion-related disorder, and images eliciting different types of emotions were selected from the international affective picture system (IAPS) and presented to subjects during ITFI collection. ITFIs were aligned using affine registration, and the changes induced by small irregular head movements were corrected. The average temperatures from the forehead, nose, mouth, left cheek, and right cheek were calculated, and continuous temperature changes were used as features. After performing dimensionality reduction and noise removal using the component analysis method, multivariate analysis of variance and the Support Vector Machine (SVM) classification algorithm were used to identify moderately and markedly ill schizophrenia patients. Results Analysis of five facial areas indicated significant temperature changes in the forehead and nose upon exposure to various emotional stimuli and in the right cheek upon evocation of high valence low arousal (HVLA) stimuli. The most significant P -value (lower than 0.001) was obtained in the forehead area upon evocation of disgust. Finally, when the features of forehead temperature changes in response to low valence high arousal (LVHA) were reduced to 9 using dimensionality reduction and noise removal, the identification rate was as high as 94.3%. Conclusions Our results show that features obtained in the forehead, nose, and right cheek significantly differed between moderately and markedly ill schizophrenia patients. We then chose the features that most effectively distinguish between moderately and markedly ill schizophrenia patients using the SVM. These results demonstrate that the ITFI analysis protocol proposed in this study can effectively provide reference information regarding the phase of the disease in patients with schizophrenia.

Abstract Background Complex regional pain syndrome (CRPS) is difficult to effectively treat with unimodal approaches. Objective To investigate whether CRPS can be effectively treated in a comprehensive interdisciplinary pain management program. Design Observational cohort study of 49 patients aged 18–89 who fulfilled ‘Budapest Criteria’ for CRPS and completed an interdisciplinary pain management program. Preprogram to postprogram changes in physical functioning, perceived disability, emotional functioning, acceptance, coping, and pain were assessed. The measures used included: Pain Disability Index, Six minute walk test, 2-minute sit-to-stand, Numerical Rating Scale, Center for Epidemiologic Studies Depression Scale, Pain Anxiety Symptoms Scale, Chronic Pain Acceptance Questionnaire, Coping Strategies Questionnaire-Revised, RIC- Multidimensional Patient Global Impression of Change (RIC-MPGIC), and Medication Quantification Scale. For worker's compensation patients, the rate of successful release to work at the end of the program was calculated. Results Results indicated significant improvements in physical functioning and perceived disability ( P 's<0.001). Patients reported increased usage of an adaptive coping strategy, distraction ( P = 0.010), and decreased usage of maladaptive and passive strategies ( P 's < 0.001). Patients showed greater chronic pain acceptance ( P 's ≤ 0.010) and reductions in emotional distress ( P 's < 0.001). Medication usage at 1-month follow-up was significantly reduced compared to program start ( P < 0.001) and discharge ( P = 0.004). Patients reported “much improvement” in overall functioning, physical functioning, mood, and their ability to cope with pain and flare-ups (RIC-MPGIC). Patient report of pain was not significantly reduced at discharge ( P =0.078). Fourteen (88%) of 16 total worker's compensation patients were successfully released to work at the end of the program. Conclusions This study demonstrates short-term improvements in physical and emotional functioning, pain coping, and medication usage. These findings are consistent with the rehabilitation philosophy of improving functioning and sense of well-being as of equal value and relevance to pain reduction.

The focus of this paper is to discuss the semiotic aspects of our findings from a project we conducted in the frame of Emotional Text Analysis paradigm. In the project, we intended to create a computer text classifier capable of effectively classifying texts into emotional categories. We agreed that we would need discrete data samples to input into it. For this, we asked 178 informants to give a verdict on the dominant emotion of 48 sample texts. Prior to their assessment of the texts, the informants responded to a questionnaire used to estimate their empathic tendency. A detailed analysis of the informants’ assessments and personal empathetic tendency scores showed a positive correlation. Subsequently, our interest was piqued by the issue of how emotions could be triggered by conventional signs (words). Our findings seem to suggest that words are only used as an expression form insofar as they embody another type of semiotic complexity, thus diverging from the traditional Pearcian triad. In order to develop on these findings, it is therefore the main objective of this paper to provide a biosemiotic model of representation/interpretation of emotions, with particular attention paid to the eliciting of emotions as sign types. In this endeavour, we draw upon K. Kull’s concept of emonic semiotic model realization. Our suggestion is that, when one processes a text that elicits an emotional response, two semiotic facets are relevant: indexicality and emonicity. We argue that it is a main empathetic function to enforce the emonic model of semiosis over the indexical in situations where the interpreter has a choice between the two. As such, the hypothesis of the study is that emotions facilitate a particular type of semiotic mechanism, relying on the mimesis principle.