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.
Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band. Combination of them gives a better reflect of the energy characteristics of EEG signals. Feature extraction and fusion are implemented based on kernel principal component analysis. Six emotional states from a public multimodal database for emotion analysis using physiological signals (DEAP) are recognized. The results show that the recognition accuracy of the proposed algorithm is more than 90%, and the highest recognition accuracy is 99.33%. It indicates that this algorithm can extract the feature of EEG emotion well, and it is a kind of effective emotion feature extraction algorithm, providing support to emotion recognition.
Fear emotion is a typical negative emotion that is commonly present in daily life and significantly influences human behavior. A deeper understanding of the mechanisms underlying negative emotions contributes to the improvement of diagnosing and treating disorders related to negative emotions. However, the neural mechanisms of the brain when faced with fearful emotional stimuli remain unclear. To this end, this study further combined electroencephalogram (EEG) source analysis and cortical brain network construction based on early posterior negativity (EPN) analysis to explore the differences in brain information processing mechanisms under fearful and neutral emotional picture stimuli from a spatiotemporal perspective. The results revealed that neutral emotional stimuli could elicit higher EPN amplitudes compared to fearful stimuli. Further source analysis of EEG data containing EPN components revealed significant differences in brain cortical activation areas between fearful and neutral emotional stimuli. Subsequently, more functional connections were observed in the brain network in the alpha frequency band for fearful emotions compared to neutral emotions. By quantifying brain network properties, we found that the average node degree and average clustering coefficient under fearful emotional stimuli were significantly larger compared to neutral emotions. These results indicate that combining EPN analysis with EEG source component and brain network analysis helps to explore brain functional modulation in the processing of fearful emotions with higher spatiotemporal resolution, providing a new perspective on the neural mechanisms of negative emotions.
ObjectiveTo explore the effect of family-school-hospital application in continuous nursing care for children with epilepsy. Methods120 children with epilepsy admitted to Children's Hospital Affiliated to Jiangnan University from January 2021 to October 2022 were randomly divided into two groups, each with 60 cases. The control group received routine care, while the experimental group received family-school-hospital continuous care. Compare the awareness of epilepsy knowledge, disease control effectiveness, medication compliance, negative emotions, physical and mental status, and quality of life before and after nursing between the families of two groups of children with epilepsy. ResultsAfter 2 months of nursing care, the scores of family members' knowledge of epilepsy in the experimental group were higher than the control group (P<0.05). The effect of disease control in the experimental group was better the control group (P<0.05). The drug compliance of the experimental group was higher than the control group (P<0.05). The quality of life score in the intervention group was higher than the control group (P<0.05). ConclusionThe application of family-school-hospital in the continuous care of children with epilepsy can improve their family members' awareness of epilepsy knowledge, effectively control the disease, improve medication compliance, improve negative emotions and physical and mental conditions, and thus improve the quality of life of children.
Childhood is the key period of psychological and behavioral development of children. The changes of children's psychological behavior during this period have an impact on the psychological and behavioral patterns of adolescents and even adults. Epilepsy is a chronic and recurrent disease, which affect the development emotional behavior problem of children with epilepsy seriously. This paper reviewed the influencing factors, measuring methods and intervention of emotional behavior problems in children with epilepsy so as to alleviate the negative emotion and behavior problems and provide quality of life in children with epilepsy.
ObjectiveTo investigate the job satisfaction, emotional state and related factors of medical staff participating in online consultation of West China Internet Hospital during the COVID-19 epidemic.MethodsThrough literature review and expert consultation (Delphi method), the questionnaire was developed, and the online consulting medical staff of West China Hospital of Sichuan University were invited to conduct the questionnaire survey from 26 January to 19 June 2020, and finally the statistical analysis was summarized.ResultsA total of 132 valid questionnaires were retrieved. Of the 132 subjects, 127 people (96.2%) expressed satisfaction or special satisfaction with the online consulting office format; 103 respondents (78.0%) said that online consulting did not affect or completely did not affect the work and life; 81 people (61.4%) consulted online more than 5 days a week, and 108 people (81.8%) worked within 2 hours a day; the vast majority (97.7%) of the research subjects were satisfied with the content of the training materials and the related support work of the coordination group. Only 29 (22.0%) of the study participants believed that the epidemic caused negative emotions, mainly due to the severity of the epidemic.ConclusionThe online consulting medical staff are satisfied with the office form, training materials and coordination work group of the COVID-19 epidemic, and think that it does not affect their work and life. 22.0% of medical staff have negative emotions, and the severity of the epidemic is the main reason.
Emotion recognition will be prosperious in multifarious applications, like distance education, healthcare, and human-computer interactions, etc. Emotions can be recognized from the behavior signals such as speech, facial expressions, gestures or the physiological signals such as electroencephalogram and electrocardiogram. Contrast to other methods, the physiological signals based emotion recognition can achieve more objective and effective results because it is almost impossible to be disguised. This paper introduces recent advancements in emotion research using physiological signals, specified to its emotion model, elicitation stimuli, feature extraction and classification methods. Finally the paper also discusses some research challenges and future developments.
Evolutionary psychology holds such an opinion that negative situation may threaten survival, trigger avoidance motive and have poor effects on the human body function and the psychological quality. Both disgusted and sad situations can induce negative emotions. However, differences between the two situations on attention capture and emotion cognition during the emotion induction are still not well known. Typical disgusted and sad situation images were used in the present study to induce two negative emotions, and 15 young students (7 males and 8 females, aged 27±3) were recruited in the experiments. Electroencephalogram of 32 leads was recorded when the subjects were viewing situation images, and event-related potentials (ERP) of all leads were obtained for future analysis. Paired sample t tests were carried out on two ERP signals separately induced by disgusted and sad situation images to get time quantum with significant statistical differences between the two ERP signals. Root-mean-square deviations of two ERP signals during each time quantum were calculated and the brain topographic map based on root-mean-square deviations was drawn to display differences of two ERP signals in spatial. Results showed that differences of ERP signals induced by disgusted and sad situation images were mainly manifested in T1 (120-450 ms) early and T2 (800-1 000 ms) later. During the period of T1, the occipital lobe reflecting attention capture was activated by both disgusted and sad situation images, but the prefrontal cortex reflecting emotion sense was activated only by disgusted situation images. During the period of T2, the prefrontal cortex was activated by both disgusted and sad situation images. However, the parietal lobe was activated only by disgusted situation images, which showed stronger emotional perception. The research results would have enlightenment to deepen understanding of negative emotions and to explore deep cognitive neuroscience mechanisms of negative emotion induction.
Objective To understand the status quo of depression and anxiety emotion in perioperative patients with thoracic neoplasms under the concept of enhanced recovery aftersurgery. Methods Huaxi emotional-distress index scale (HEI) was adopted to investigate the mental status of 195 patients with thoracic neoplasms in Department of Thoracic Surgery, West China Hospital, and the nursing outpatients between September and November in 2016. There were 118 males and 77 females at age of 17–80 (55.72±12.66) years. Results There was significant difference in mental health level between the preoperative patients and the postoperative patients (3.70±3.41vs. 11.01±9.78,P<0.001). The incidence of depression and anxiety emotion in the postoperative patients was significantly higher than that in the preoperative patients (50.00%vs. 9.60%, P<0.001). Besides, there was significant difference of depression and anxiety degree between the preoperative patients and postoperative patients (P<0.001). Moderate to severe depression and anxiety were mostly found in the postoperative patients while mild to moderate depression and anxiety in the preoperative patients. Conclusion Patients with thoracic neoplasms have much emotional obstacle in perioperative period. The incidence and severity degree of depression and anxiety emotion in postoperative patients are higher than those in preoperative patients.
Objective To analyze and explore positive emotional experiences of patients, in order to provide reference for improving the medical services. Methods Using NVivo software, praise letters from a tertiary hospital in Guangdong in 2020 and 2021 were used as the research object for three-level coding. The positive emotional experiences of patients were explored through tools such as analytic hierarchy process and node item map. Results A total of 8601 patient praise letters were received, and after screening, a total of 8128 valid texts were obtained. In 2020, there were 2570 patient praise letters, including 69 from the emergency department, 638 from the outpatient department, and 1863 from the inpatient department. In 2021, there were 5558 patient praise letters, including 203 from the emergency department, 2071 from the outpatient department, and 3284 from the inpatient department. The most praise letters were from the inpatient department, with a total of 5147 letters (63.3%). There were 2709 letters (33.3%) from outpatient department, and 272 letters (3.3%) from emergency department. The classification of patient praise letters showed that patient praise for the process and individuals were most common (77.4%). After step-by-step encoding, the valid text formed 36 third level nodes, 8 second level nodes, and 3 first level nodes. Patient praise letters mainly focused on emotional evaluation at the first level node, followed by emotional expression and emotional response. Word frequency analysis showed that in terms of positive emotional experiences, the word “thank you” had the highest frequency among patients. In terms of patient perception of service, the term “patience” had the highest frequency. Conclusions When patients express praise for medical services, they pay more attention to the personal performance of medical staff and the experience of the service process compared to the final result. In the process of hospital management, the emotions of patients should be fully considered.