In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.
Sleep ; Sleep Stages ; Arousal ; Data Analysis ; Electroencephalography

There are two modes to display panoramic movies in virtual reality (VR) environment: non-stereoscopic mode (2D) and stereoscopic mode (3D). It has not been fully studied whether there are differences in the activation effect between these two continuous display modes on emotional arousal and what characteristics of the related neural activity are. In this paper, we designed a cognitive psychology experiment in order to compare the effects of VR-2D and VR-3D on emotional arousal by analyzing synchronously collected scalp electroencephalogram signals. We used support vector machine (SVM) to verify the neurophysiological differences between the two modes in VR environment. The results showed that compared with VR-2D films, VR-3D films evoked significantly higher electroencephalogram (EEG) power (mainly reflected in α and β activities). The significantly improved β wave power in VR-3D mode showed that 3D vision brought more intense cortical activity, which might lead to higher arousal. At the same time, the more intense α activity in the occipital region of the brain also suggested that VR-3D films might cause higher visual fatigue. By the means of neurocinematics, this paper demonstrates that EEG activity can well reflect the effects of different vision modes on the characteristics of the viewers' neural activities. The current study provides theoretical support not only for the future exploration of the image language under the VR perspective, but for future VR film shooting methods and human emotion research.
Arousal ; Electroencephalography ; Emotions/physiology* ; Humans ; Motion Pictures ; Virtual Reality

Objective: To explore the effect of emotional optimization of workplace employees in immersive virtual natural environment. Methods: In July 2020, 15 subjects were selected to complete two groups of treadmill walking training experiments in virtual natural environment and daily environment respectively. At the same time, the subjects' skin electrical (EDA) , pulse frequency (Pf) , respiratory frequency (Rf) physiological data and Self-Assessment Manikin (SAM) data before and after walking were collected; the mean value of three dimensions of SAM and the emotion difference before and after the experiment were calculated. The differences of physiological indexes and subjective mood changes of subjects were tested by paired sample t-test. Results: Compared with the daily environment, the ΔEDA, ΔPf and ΔRf of the subjects in the virtual natural environment were all decreased , and the differences were statistically significant (P<0.05). There were statistically significant differences in pleasure and arousal between subjects before and after using the virtual natural environment (P <0.05). Compared with the daily environment, the Δpleasure degree of subjects using the virtual natural environment increased, and the Δarousal degree and Δdominance degree decreased, and the differences were statistically significant (P <0.05). Conclusion: Walking in virtual natural environment can help subjects improve their mood, relax and improve the regulation ability of autonomic nervous system.
Arousal ; Emotions/physiology* ; Heart Rate ; Humans ; Virtual Reality ; Workplace

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
Arousal ; Electroencephalography ; Emotions ; Humans ; Memory, Short-Term ; Neural Networks, Computer

Insomnia is a subjective experience of difficulty in falling asleep and/or maintaining sleep accompanied by the impairment of daytime social functioning due to insufficient sleep quality or quantity to meet normal physiological needs.It has chronic damage to all the human body systems and is the most common sleep disorder.The main mechanism for the occurrence and maintenance of insomnia is the hyperarousal hypothesis,and microarousal,as a cortical arousal,is also involved in the formation of the hyperarousal mechanism.The mechanism and clinical significance of microarousal were reviewed and summarized in this paper in order to guide the clinical work.
Arousal ; Humans ; Sleep ; Sleep Initiation and Maintenance Disorders ; Sleep Quality

Sexual arousal is an important factor for the success of sexual behavior, and regulated by the central nervous system, its underlying mechanism is very complicated. Androgen is the most important endocrine hormone in men, which is deeply involved in the whole process of male sexual response, but how it regulates male sexual arousal has not been fully clarified and remains one of the hotspots in current andrological research. Therefore, this paper presents an overview of the advances in the studies of the related role and mechanism of androgen in male sexual arousal. In the central nervous system, androgen regulates the release of dopamine neurotransmitters by binding androgen receptors or metabolizing neurosteroids, thus activating the brain reward system. Besides, androgen regulates the neuronal plasticity and spinous process formation in the neural circuit of sexual arousal to ensure successful activation and conduction of the neural circuit. However, the specific regulating mechanism of sexual arousal remains to be further explored.
Androgens ; Humans ; Male ; Sexual Arousal

Adult ; Altitude Sickness ; Arousal ; Electroencephalography ; Emotions ; Humans ; Hypoxia ; Male ; Young Adult

Emotion is a series of reactions triggered by a specific object or situation that affects a person's physiological state and can, therefore, be identified by physiological signals. This paper proposes an emotion recognition model. Extracted the features of physiological signals such as photoplethysmography, galvanic skin response, respiration amplitude, and skin temperature. The SVM-RFE-CBR(Recursive Feature Elimination-Correlation Bias Reduction-Support Vector Machine) algorithm was performed to select features and support vector machines for classification. Finally, the model was implemented on the DEAP dataset for an emotion recognition experiment. In the rating scale of valence, arousal, and dominance, the accuracy rates of 73.5%, 81.3%, and 76.1% were obtained respectively. The result shows that emotional recognition can be effectively performed by combining a variety of physiological signals.
Arousal ; Emotions ; Galvanic Skin Response ; Humans ; Photoplethysmography ; Support Vector Machine

To investigate psychological characteristics in different clinical subgroups of insomniacs, and to provide the basis for the accurate simplification of cognitive behavioral therapy for insomnia.
 Methods: A total of 212 insomniacs from November 2014 to June 2017 in Clinical Psychology Department or Sleep Department of 2 general hospitals in Hunan Province were included in convenient and classified into sleep onset insomnia (SOI), difficulty maintaining insomnia (DMI), early morning awakening insomnia (EMAI), and combined insomnia (CI) subgroups. Ford Insomnia Response to Stress Test (FIRST), Simplified Coping Style Questionnaire (SCSQ), Dysfunctional Beliefs and Attitudes about Sleep Scale 16 version (DBAS-16), Sleep-Related Behavior Questionnaire (SRBQ), Pre-sleep Arousal Scale (PSAS), Center for Epidemiological Studies Depression Scale (CES-D), Beck Anxiety Inventory (BAI) were used to investigate the psychological characteristics.
 Results: SOI and CI insomniacs had a higher frequency in use of sleep-related behavior than those with DMI; CI had a higher frequency in use of sleep-related behavior than those with EMAI (all P<0.05). Both SOI and CI insomniacs had a higher level of pre-sleep cognitive arousal than DMI and EMAI (all P<0.05). CI insomniacs noticed more consequences of insomnia and had more worries on insomnia than DMI, and CI insomniacs had more expectations of sleep than SOI (all P<0.05).
 Conclusion: Insomniacs with different clinical subgroups have different features of psychological characteristics. Both the insomnia subgroups and the psychological characteristics should be taken into account when we simplify cognitive behavioral therapy for insomnia (CBT-I) precisely.
Anxiety ; Arousal ; Cognitive Behavioral Therapy ; Humans ; Sleep ; Sleep Initiation and Maintenance Disorders ; Surveys and Questionnaires

OBJECTIVES: Insomnia is one of the most prevalent sleep disorders. Recent studies suggest that cognitive and physical arousal play an important role in the generation of primary insomnia. Studies have also shown that information processing disorders due to cortical hyperactivity might interfere with normal sleep onset and sleep continuity. Therefore, focusing on central nervous system arousal and normalizing the information process have become current topics of interest. It has been well known that neurofeedback can reduce the brain hyperarousal by modulating patients' brain waves during a sequence of behavior therapy. The purpose of this study was to investigate effects of neurofeedback therapy on electroencephalography (EEG) characteristics in patients with primary insomnia. METHODS: Thirteen subjects who met the criteria for an insomnia diagnosis and 14 control subjects who were matched on sex and age were included. Neurofeedback and sham treatments were performed in a random order for 30 minutes, respectively. EEG spectral power analyses were performed to quantify effects of the neurofeedback therapy on brain wave forms. RESULTS: In patients with primary insomnia, relative spectral theta and sigma power during a therapeutic neurofeedback session were significantly lower than during a sham session (13.9 ± 2.6 vs. 12.2 ± 3.8 and 3.6 ± 0.9 vs. 3.2 ± 1.0 in %, respectively; p < 0.05). There were no statistically significant changes in other EEG spectral bands. CONCLUSION: For the first time in Korea, EEG spectral power in the theta band was found to increase when a neurofeedback session was applied to patients with insomnia. This outcome might provide some insight into new interventions for improving sleep onset. However, the treatment response of insomniacs was not precisely evaluated due to limitations of the current pilot study, which requires follow-up studies with larger samples in the future.
Arousal ; Automatic Data Processing ; Behavior Therapy ; Brain ; Brain Waves ; Central Nervous System ; Diagnosis ; Electroencephalography ; Follow-Up Studies ; Humans ; Korea ; Neurofeedback ; Pilot Projects ; Sleep Initiation and Maintenance Disorders ; Sleep Wake Disorders