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.
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.
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.
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.
In order to monitor the emotional state changes of audience on real-time and to adjust the music playlist, we proposed an algorithm framework of an electroencephalogram (EEG) driven personalized affective music recommendation system based on the portable dry electrode shown in this paper. We also further finished a preliminary implementation on the Android platform. We used a two-dimensional emotional model of arousal and valence as the reference, and mapped the EEG data and the corresponding seed songs to the emotional coordinate quadrant in order to establish the matching relationship. Then, Mel frequency cepstrum coefficients were applied to evaluate the similarity between the seed songs and the songs in music library. In the end, during the music playing state, we used the EEG data to identify the audience’s emotional state, and played and adjusted the corresponding song playlist based on the established matching relationship.
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.
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.
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.
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.