Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise K fold cross validation. Moreover, we compare the accuracy of emotion recognition with different time window length. The experimental results indicate that 4 s time window is appropriate for sampling. Filter-bank long short-term memory networks (FBLSTM) using differential entropy features as input was proposed. The average accuracy of low and high in valance dimension, arousal dimension and combination of the four in valance-arousal plane is 78.8%, 78.4% and 70.3%, respectively. These results demonstrate the advantage of our emotion recognition model over the current studies in terms of classification accuracy. Our model might provide a novel method for emotion recognition in affective brain-computer interactions.
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.
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 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.
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 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.
As an important component of the event related potential (ERP), late positive potential (LPP) is an ideal component for studying emotion regulation. This study was focused on processing and analysing the LPP component of the emotional cognitive reappraisal electroencephalogram (EEG) signal. Firstly, we used independent component analysis (ICA) algorithm to remove electrooculogram, electromyogram and some other artifacts based on 16 subjects' EEG data by using EGI 64-channal EEG acquisition system. Secondly, we processed feature extraction of the EEG signal at Pz electrode by using one versus the rest common spatial patterns (OVR-CSP) algorithm. Finally, the extracted LPP component was analysed both in time domain and spatial domain. The results indicated that ① From the perspective of amplitude comparison, the LPP amplitude, which was induced by cognitive reappraisal, was much higher than the amplitude under the condition of watching neural stimuli, but lower than the amplitude under condition of watching negative stimuli; ② from the perspective of time process, the difference between cognitive reappraisal and watching after processing with OVR-CSP algorithm was in the process of range between 0.3 s and 1.5 s; but the difference between cognitive reappraisal and watching after processing with averaging method was during the process between 0.3 s and 1.25 s. The results suggested that OVR-CSP algorithm could not only accurately extract the LPP component with fewer trials compared with averaging method so that it provided a better method for the follow-up study of cognitive reappraisal strategy, but also provide neurophysiological basis for cognitive reappraisal in emotional regulation.
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.
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.
Objective To investigate the status quo and influencing factors of depression and anxiety in postoperative patients with thoracic neoplasms. Methods The general information questionnaire and Huaxi emotional-distress index scale (HEI) were adopted to survey 70 patients after surgery of thoracic neoplasms at the thoracic nursing outpatients from September to November 2016. There were 43 males and 27 females with age of 18-78 (56.20±11.34) years. Results The prevalence rate of depression and anxiety among postoperative patients with thoracic neoplasms was 50.0%, and moderate to severe negative emotions predominated. There was significant difference in educational levels, postoperative hospitalization and postoperative complications (P<0.05), while no significant difference in age, gender, disease types, complicated diseases, surgical procedures, pathological stages and hospitalization expenditures between patients with unhealthy emotions and normal emotions (P>0.05). Conclusion There is a high prevalence rate of negative emotion among postoperative patients with thoracic neoplasms. Educational levels, postoperative hospitalization and postoperative complications are important factors for negative emotion.