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About this Research Topic

Abstract Submission Deadline 22 February 2023
Manuscript Submission Deadline 22 June 2023

With the deepening of neuroscience research on the emotional neural mechanism and the emerging emergence of affect science, the diagnosis of mental disorders and affective computing has gradually received intense attention in recent years. It has been confirmed by many researchers that mental disorders could accelerate brain aging. However, how these mental disorders accelerate brain aging, the internal relationship between them, and the mechanism of the occurrence and evolution of such disorders are still unclear. Owing to the advantages of objectiveness, high time resolution, and non-invasion, electroencephalogram (EEG) is commonly employed as an essential neural signal analysis technology in the diagnosis of mental disorders, and the analysis of human emotions.

This Research Topic mainly focuses on the exploration of human affective processes and the relationship between mental disorders and age by using EEG technology. Specifically, it is emphasized to reveal the internal relationship between EEG changes and mental disorders. Noting that the mental disorders mentioned in this topic are not limited to anxiety disorder, obsessive-compulsive disorder, depression, bipolar disorder, schizophrenia, anorexia nervosa, bulimia nervosa, and acute stress disorder. In terms of affective computing, this special issue concentrates on recognizing and interpreting human emotions through EEG, and on this basis promoting the diagnosis and treatment of mental disorders.

This Research Topic contains age-related mental disorder diagnosis and affective computing. For this purpose, EEG technology is a major implementation method, and additionally, multimodal fusion, e.g., the combination or comparison of EEG technology with other technologies is encouraged. Moreover, the strategies involved in these technologies, including the excavation of EEG biomarkers of mental disorders, extraction of distinctive features, design, and development of pattern recognition and regression algorithms are also worth to be explored extensively.

Potential topics include, but are not limited to:
- Detection technology of age-related brain changes
- Extraction of EEG biomarkers for mental disorders
- Machine learning methods for EEG analysis
- Affective computing
- Applications of neuromodulation technologies

Keywords: Diagnosis of Mental Disorders, Brain Aging, EEG Technology, Machine Learning, Affective Computing, Emotional Regulation


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

With the deepening of neuroscience research on the emotional neural mechanism and the emerging emergence of affect science, the diagnosis of mental disorders and affective computing has gradually received intense attention in recent years. It has been confirmed by many researchers that mental disorders could accelerate brain aging. However, how these mental disorders accelerate brain aging, the internal relationship between them, and the mechanism of the occurrence and evolution of such disorders are still unclear. Owing to the advantages of objectiveness, high time resolution, and non-invasion, electroencephalogram (EEG) is commonly employed as an essential neural signal analysis technology in the diagnosis of mental disorders, and the analysis of human emotions.

This Research Topic mainly focuses on the exploration of human affective processes and the relationship between mental disorders and age by using EEG technology. Specifically, it is emphasized to reveal the internal relationship between EEG changes and mental disorders. Noting that the mental disorders mentioned in this topic are not limited to anxiety disorder, obsessive-compulsive disorder, depression, bipolar disorder, schizophrenia, anorexia nervosa, bulimia nervosa, and acute stress disorder. In terms of affective computing, this special issue concentrates on recognizing and interpreting human emotions through EEG, and on this basis promoting the diagnosis and treatment of mental disorders.

This Research Topic contains age-related mental disorder diagnosis and affective computing. For this purpose, EEG technology is a major implementation method, and additionally, multimodal fusion, e.g., the combination or comparison of EEG technology with other technologies is encouraged. Moreover, the strategies involved in these technologies, including the excavation of EEG biomarkers of mental disorders, extraction of distinctive features, design, and development of pattern recognition and regression algorithms are also worth to be explored extensively.

Potential topics include, but are not limited to:
- Detection technology of age-related brain changes
- Extraction of EEG biomarkers for mental disorders
- Machine learning methods for EEG analysis
- Affective computing
- Applications of neuromodulation technologies

Keywords: Diagnosis of Mental Disorders, Brain Aging, EEG Technology, Machine Learning, Affective Computing, Emotional Regulation


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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