Cognitive reappraisal is an important strategy for emotion regulation. Studies show that even healthy people may not be able to implement this strategy successfully, but the underlying neural mechanism behind the behavioral observation of success or failure of reappraisal is unclear. In this paper, 28 healthy college students participated in an experiment of emotional regulation with the cognitive reappraisal strategy. They were asked to complete the cognitive psychological questionnaires before the experiment. Their behavioral scores and scalp electroencephalogram (EEG) signals were collected simultaneously during the experiment. We divided all the subjects into two groups, according to the statistical test of valence scores. Then we analyzed their questionnaires, early event-related potential (ERP) components N200, P200, and late positive potential (LPP), and calculated the correlation between the valence score and the amplitude of LPP. The results showed that, in both groups, compared with negative-watching, the reappraisal induced larger N200 and P200 components and there were two modulation patterns (“increase” and “decrease”) of the reappraisal effect on the amplitude of early LPP (300−1 000 ms after stimulus onset). Moreover, correlation analysis showed that significant positive correlation between two differences in the successful group, i.e., the greater difference in the valence scoresin between reappraisal and negative-watching, the greater difference in the amplitude of early LPP between reappraisal and negative-watching; but no such effect was found in the failure group. These results indicated that, whether reappraisal was successful or not, no significant effect on early ERP components was found; and there were different patterns of the reappraisal effect on early LPP. The difference between successful and failure groups was mainly reflected in early LPP, that is, the EEG characteristics and behavioral scores of successful group were significantly positively correlated. Furthermore, the small sample analysis showed that this correlation only existed in the pattern of "increase". In the future, more research of this modulation mode is necessary in order to find more stable EEG characteristics under successful cognitive reappraisal in emotion regulation.
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.
ObjectiveTo investigate the negative emotions of patients before cardiac surgery in West China Hospital in order to analyze the related factors.MethodsThe Huaxi emotional-distress index (HEI), a screening tool for mood disorders developed by the Mental Health Center of West China Hospital, was used for preoperative psychological evaluation of 1 968 adult patients hospitalized in cardiac surgery from March 2016 to July 2014. There were 835 males and 1 133 females at age of 49±13 years.Results Fifty-one patients (2.6%) had negative emotions, among whom 6 patients were screened for suicide risk. After intervention, none of them had serious consequences caused by adverse emotions, such as automatic discharge from hospital, avoidance of surgery and suicide.ConclusionThis study found that most of the cardiac surgery patients in West China Hospital have good psychological status before surgery, and a few suffered from negative emotions. “Huaxi emotional-distress index” is simple, effective and worth promoting.
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.
ObjectiveTo examine the effect of preoperative adverse emotion on rehabilitation outcomes in lung cancer patients undergoing thoracoscopic major pulmonary resection.MethodsWe retrospectively analyzed the clinical data of 1 438 patients with lung cancer who underwent thoracoscopic lobectomy and segmentectomy in West China Hospital of Sichuan University from February 2017 to July 2018 including 555 males and 883 females. All patients were assessed by Huaxi emotional-distress index scoring, and were divided into three groups including a non-negative emotion group, a mild negative emotion group, and a moderate-severe negative emotion group. All patients underwent thoracoscopic lobectomy or segmentectomy plus systematic lymph node dissection or sampling. The volume of postoperative chest drainage, postoperative lung infection rate, time of chest tube intubation and postoperative duration of hospitalization were compared among these three groups.ResultsThere were different morbidities of adverse emotion in age, sex, education level and smoking among patients before operation (P<0.05). Univariate analysis showed that there was no statistical difference in the duration of indwelling drainage tube, drainage volume, postoperative pulmonary infection rate or the incidence of other complications among these three groups, but the duration of hospitalization in the latter two groups was less than that in the first group with a statistical difference (P<0.05). After correction of confounding factors by multiple regression analysis, there was no statistical difference among the three groups.ConclusionYoung patients are more likely to develop bad emotions, women are more likely to develop serious bad emotions, highly educated patients tend to develop bad emotions, and non-smoking patients tend to develop bad emotions. There is no effect of preoperative adverse emotions on the rapid recovery of lung cancer patients after minimally invasive thoracoscopic surgery.
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.
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.
Emotion can reflect the psychological and physiological health of human beings, and the main expression of human emotion is voice and facial expression. How to extract and effectively integrate the two modes of emotion information is one of the main challenges faced by emotion recognition. In this paper, a multi-branch bidirectional multi-scale time perception model is proposed, which can detect the forward and reverse speech Mel-frequency spectrum coefficients in the time dimension. At the same time, the model uses causal convolution to obtain temporal correlation information between different scale features, and assigns attention maps to them according to the information, so as to obtain multi-scale fusion of speech emotion features. Secondly, this paper proposes a two-modal feature dynamic fusion algorithm, which combines the advantages of AlexNet and uses overlapping maximum pooling layers to obtain richer fusion features from different modal feature mosaic matrices. Experimental results show that the accuracy of the multi-branch bidirectional multi-scale time sensing dual-modal emotion recognition model proposed in this paper reaches 97.67% and 90.14% respectively on the two public audio and video emotion data sets, which is superior to other common methods, indicating that the proposed emotion recognition model can effectively capture emotion feature information and improve the accuracy of emotion recognition.
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.
Analyzing the influence of mixed emotional factors on false memory through brain function network is helpful to further explore the nature of brain memory. In this study, Deese-Roediger-Mc-Dermott (DRM) paradigm electroencephalogram (EEG) experiment was designed with mixed emotional memory materials, and different kinds of music were used to induce positive, calm and negative emotions of three groups of subjects. For the obtained false memory EEG signals, standardized low resolution brain electromagnetic tomography algorithm (sLORETA) was applied in the source localization, and then the functional network of cerebral cortex was built and analyzed. The results show that the positive group has the most false memories [(83.3 ± 6.8)%], the prefrontal lobe and left temporal lobe are activated, and the degree of activation and the density of brain network are significantly larger than those of the calm group and the negative group. In the calm group, the posterior prefrontal lobe and temporal lobe are activated, and the collectivization degree and the information transmission rate of brain network are larger than those of the positive and negative groups. The negative group has the least false memories [(73.3 ± 2.2)%], and the prefrontal lobe and right temporal lobe are activated. The brain network is the sparsest in the negative group, the degree of centralization is significantly larger than that of the calm group, but the collectivization degree and the information transmission rate of brain network are smaller than the positive group. The results show that the brain is stimulated by positive emotions, so more brain resources are used to memorize and associate words, which increases false memory. The activity of the brain is inhibited by negative emotions, which hinders the brain’s memory and association of words and reduces false memory.