Existing emotion recognition research is typically limited to static laboratory settings and has not fully handle the changes in emotional states in dynamic scenarios. To address this problem, this paper proposes a method for dynamic continuous emotion recognition based on electroencephalography (EEG) and eye movement signals. Firstly, an experimental paradigm was designed to cover six dynamic emotion transition scenarios including happy to calm, calm to happy, sad to calm, calm to sad, nervous to calm, and calm to nervous. EEG and eye movement data were collected simultaneously from 20 subjects to fill the gap in current multimodal dynamic continuous emotion datasets. In the valence-arousal two-dimensional space, emotion ratings for stimulus videos were performed every five seconds on a scale of 1 to 9, and dynamic continuous emotion labels were normalized. Subsequently, frequency band features were extracted from the preprocessed EEG and eye movement data. A cascade feature fusion approach was used to effectively combine EEG and eye movement features, generating an information-rich multimodal feature vector. This feature vector was input into four regression models including support vector regression with radial basis function kernel, decision tree, random forest, and K-nearest neighbors, to develop the dynamic continuous emotion recognition model. The results showed that the proposed method achieved the lowest mean square error for valence and arousal across the six dynamic continuous emotions. This approach can accurately recognize various emotion transitions in dynamic situations, offering higher accuracy and robustness compared to using either EEG or eye movement signals alone, making it well-suited for practical applications.
ObjectiveTo investigate the effect of positive family behavior support on emotional and behavioral problems in preschool children with epilepsy. Methods A total of 80 preschool epileptic children and their parents who were admitted to the Department of Neurology of our hospital from October 2022 to February 2023 were selected as the research objects, and were divided into experimental group and control group with 40 cases each by random number table method. The control group received neurology routine nursing, and the experimental group received positive family behavior support intervention based on the control group. The scores of family intimacy and adaptability scale, strengths and difficulties questionnaire, medication compliance and quality of life of epilepsy children were compared before and after intervention between the two groups. ResultsAfter intervention, the scores of strength and difficulty questionnaire in experimental group were lower than those in control group (P<0.05), and the scores of family intimacy and adaptability scale, quality of life and medication compliance in experimental group were higher than those in control group (all P<0.05). ConclusionThe application of positive family behavior support program can reduce the occurrence of emotional behavior problems, improve family closeness and adaptability, improve medication compliance, and improve the quality of life of preschool children with epilepsy.
Objective To explore the association between behavioral, emotional problems and life events among adolescents, and to determine which factors of life events correlate most highly with the behavioral, emotional problems. Method A total of 1 325 adolescents were investigated with Youth Self-Report (YSR) of Achenbach’s behavior checklist and Adolescent Self-Rating Life Events Checklist (ASLEC), and the data were analyzed with canonical correlation analysis. Results Canonical correlation was statistically significant. The correlation coefficients of the first pair of canonical variables in the male and female group were 0.631 3 and 0.621 1, respectively, and the cumulative proportion of the first two pairs of canonical variables was above 0.95. In the first pair of canonical variables, the loadings of anxious/depressed, interpersonal sensitivity and study pressure were higher, while in the second pair, withdrawal and punishment were the most important factors. Conclusions The effects of life events on emotional problems mainly contributed to interpersonal sensitivity and study pressure.
ObjectiveTo identify the effects of transition to siblinghood (TTS) on the firstborn children’s emotions and behaviors, and to define the time of TTS.MethodsCBM, VIP, CNKI, WanFang Data, PubMed, Web of Science and EBSCO were electronically searched to collect studies on the emotional and behavioral characteristics of firstborn children in TTS from inception to December 31st, 2019. Two reviewers independently screened literature, extracted data and assessed the risk bias of included studies. Then, qualitative methods were used to analyze the studies.ResultsA total of 13 studies involving 980 children were included. 12 behavioral related studies explored self-behavior of the firstborn children during TTS, 3 studies focused on the interaction behavior between the firstborn children and their parents, the firstborn children and the second children. The systematic reviews found that TTS showed both positive and negative effects on the behavioral characteristics of firstborn children, primarily the negative effects. Firstborn children’s anxiety, confrontation and attachment showed 3 different patterns over time, respectively. Two studies showed the increase of negative emotions of firstborn children during TTS. The time range of TTS was mainly concentrated in the third trimester to 12 months after the birth of the second child.ConclusionsThe current evidence shows that TTS primarily increases the negative emotions and behaviors of firstborn children, and the behaviors of firstborn children changes over time. Due to limited quality and quantity of the included studies, more high quality studies are required to verify above conclusions.
Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.
Emotion recognition refers to the process of determining and identifying an individual's current emotional state by analyzing various signals such as voice, facial expressions, and physiological indicators etc. Using electroencephalogram (EEG) signals and virtual reality (VR) technology for emotion recognition research helps to better understand human emotional changes, enabling applications in areas such as psychological therapy, education, and training to enhance people’s quality of life. However, there is a lack of comprehensive review literature summarizing the combined researches of EEG signals and VR environments for emotion recognition. Therefore, this paper summarizes and synthesizes relevant research from the past five years. Firstly, it introduces the relevant theories of VR and EEG signal emotion recognition. Secondly, it focuses on the analysis of emotion induction, feature extraction, and classification methods in emotion recognition using EEG signals within VR environments. The article concludes by summarizing the research’s application directions and providing an outlook on future development trends, aiming to serve as a reference for researchers in related fields.
ObjectiveTo systematically review the relationship between polycyclic aromatic hydrocarbons (PAHs) and emotion and behaviors in children and adolescents. MethodsThe PubMed, EBSCO, Web of Science, CBM, VIP, WanFang Data, OVFT, Proquest Psychological database and CNKI databases were electronically searched to collect studies on the relationship between PAHs and emotion and behaviors in children and adolescents from inception to October 20, 2022. Two reviewers independently screened the literature, extracted data and assessed the risk of bias of the included studies. A qualitative systematic review was then performed. ResultsA total of six cohort studies were included, five studies involving maternal exposure during pregnancy, found that maternal exposure to PAHs during pregnancy was associated with an increase in childhood anxiety/depression syndrome, attention problems, social withdrawal, social competence, social problems, orientation/regulation, withdrawal behaviors, and autism-related behaviors. Another study of exposure in school-age children found that PAHs exposure was associated with poorer attention performance in school. Results of other emotional behaviors were inconsistent, or no association was found. ConclusionCurrent evidence shows that PAHs have certain effects on emotional behaviors of children and adolescents. Due to the limited quality and quantity of the included studies, more high-quality cohort studies are required to verify above conclusion.
ObjectiveTo explore and clarify the relationship between epileptic seizure and inducing factors. Avoid inducing factors and reduce epileptic seizure, so as to improve the quality of life in patients with epilepsy.MethodsClinical data of 604 patients diagnosed with epilepsy in Xijing Hospital of Air Force Military Medical University from January 2018 to January 2019 were collected. The clinical data of patients with epilepsy were followed up 6 months.ResultsAmong the 604 patients, 318 (52.6%) were seizure-free in the last 6 months, 286 (47.4%) had seizures. 169 (59.1%) had seizures with at least one inducing factor. Common inducing factors: 123 cases of sleep disorder (72.8%), 114 cases of emotion changes (67.5%), 87 cases of irregular medication (51.5%), 97 cases of diet related (57.4%), 33 cases of menstruation and pregnancy (19.5%), etc. Using the χ2 test, seizures with age, gender differences had no statistical significance (P > 0.05), but seizure type was statistically different between inducing factors. In generalized seizures, tonic-clonic seizures associated with sleep deprivation (χ2= 0.189), absence seizures and anger (χ2= 0.237), pressure (χ2= 0.203), irregular life (χ2= 0.214). In the focal seizures, focal motor seizures was correlated with coffee consumption (χ2=0.145), focal sensory seizures with cold (χ2=0.235), electronic equipment use (χ2 =0.153), satiety (χ2 =0.257). Complex partial seizures was correlated with anger (χ2 =0.229), stress (χ2 =0.187), and cold (χ2 =0.198). The secondarily generalized seizures was correlated with drug missing (χ2 =0.231), sleep deprivation (χ2 =0.158), stress (χ2 =0.161), cold (χ2 =0.263), satiety (χ2 =0.182). Among the inducing factors, sleep deprivation was correlated with anger (χ2 =0.167), fatigue (χ2 =0.283), and stress (χ2 =0.230).ConclusionsEpileptic seizure were usually induced by a variety of factors. Generalized seizures were associated with sleep disorders, emotional changes, stress, irregular life, etc. While focal seizures were associated with stress, emotional changes, sleep disorders, cold, satiety, etc. An analysis of the triggers found that sleep deprivation was associated with anger, fatigue, and stress. Therefore, to clarify the inducing factors of epileptic seizure, avoid the inducing factors as much as possible, reduce the harm caused by seizures, and improve the quality of life of patients.
Emotion classification and recognition is a crucial area in emotional computing. Physiological signals, such as electroencephalogram (EEG), provide an accurate reflection of emotions and are difficult to disguise. However, emotion recognition still faces challenges in single-modal signal feature extraction and multi-modal signal integration. This study collected EEG, electromyogram (EMG), and electrodermal activity (EDA) signals from participants under three emotional states: happiness, sadness, and fear. A feature-weighted fusion method was applied for integrating the signals, and both support vector machine (SVM) and extreme learning machine (ELM) were used for classification. The results showed that the classification accuracy was highest when the fusion weights were set to EEG 0.7, EMG 0.15, and EDA 0.15, achieving accuracy rates of 80.19% and 82.48% for SVM and ELM, respectively. These rates represented an improvement of 5.81% and 2.95% compared to using EEG alone. This study offers methodological support for emotion classification and recognition using multi-modal physiological signals.
ObjectiveTo explore the influence factors of therapeutic compliance and emotional expression of first-degree relatives in acute schizophrenic patients with psychotic symptoms. MethodsThe Brief Psychiatric Rating Scale (BPRS) was used to measure the severity of psychotic symptoms in sixty schizophrenic patients from June to September 2014 in West China Hospital and the Toronto Alexithymia Scale (TAS) was used to survey the emotional expression in their family members. The homemade treatment adherence scale was used to survey the treatment adherence in patients for one week. ResultsThere was a poor therapeutic compliance in nineteen patients with acute schizophrenia (32%) and the other 41(68%) had good therapeutic compliance; the relatives of schizophrenic patients had high TAS scores (male: 67.61±10.03; female: 69.68±11.46) than the normal models did (P ＜ 0.05) . The differences between the patients with different therapeutic compliance in BPRS total score, reactivator, hostile and suspicion factor (P ＜ 0.05) . The therapeutic compliance was related to the severity of the psychotic symptoms (P ＜ 0.05) . Conclusions There is a bad emotional expression in the relatives of acute schizophrenic patients. The psychotic symptoms can influence the therapeutic compliance. The milder the psychotic symptoms, the better the therapeutic dependence.