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.
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.
Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network (SBN) has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band and alpha band from emotion electroencephalogram (EEG) of 22 subjects, constructed effective networks of different arousal, and analyzed measurements of complex network including degree, average clustering coefficient and characteristic path length. We found that: ① compared with EEG signal of low arousal, left middle temporal extensively interacted with other regions in high arousal, while right superior frontal interacted less; ② average clustering coefficient was higher in high arousal and characteristic path length was shorter in low arousal.
Objective To analyze the clinical intervention effect of multi-disciplinary team (MDT) nursing mode on patients after transcatheter aortic valve implantation (TAVI). Methods A total of 89 patients who were admitted to our hospital and underwent TAVI surgery from April to December 2021 were selected, including 64 males and 25 females, with an average age of 64.7±11.8 years. The subjects were divided into a MDT intervention group (n=42) and a control group (n=47) according to different postoperative nursing intervention methods. Clinical effectivenesses were compared between the two groups. Results The left ventricular ejection fraction in the two groups significantly increased on the 7th day after the operation, and the increase in the MDT intervention group was more obvious, with no statistical difference between the two groups (P=0.14). On the 7th day after surgery, forced vital capacity/predicated value and forced expiratory volume in one second/predicated value significantly decreased, and decreased more significantly in the control group than those in the MDT intervention group with statistical differences (P=0.01). The ICU stay time (P=0.01), hospital stay time (P<0.01) and total postoperative pulmonary complications rate (P=0.03) in the MDT intervention group were significantly shorter or lower than those in the control group The evaluation results of the anxiety and depression status of the patients before and after nursing intervention showed that the scores of anxiety and depression in the two groups were significantly lower than before, and the scores of each scale in the MDT intervention group were lower. The score of quality of life of the two groups significantly improved at the end of 6 months after surgery, and in the MDT intervention group it was significantly higher than that in the control group (P=0.02). Conclusion MDT intervention mode can promote the rapid recovery of patients after TAVI, effectively reduce the risk of postoperative pulmonary complications, and improve the postoperative quality of life.