The acquisition and processing of physiological and behavioral signals is an important means of psychological and cognitive evaluation. It is a way to realize human-machine interaction. Traditional physiological and cognitive research based on physiological and behavioral computing faces various challenges, including: 1) The relatively simple task in paradigm using traditional computer media, such as pictures and texts, is not effective; 2) The performance of machine learning models based on physiological and behavioral data is relatively poor in some complex scenarios; and 3) Psychological and cognitive assessment and intervention by physiological and behavioral computing lack corresponding clinical validation.
With technology advancement, now it is possible to design effective tasks in a paradigm using virtual reality, new scales, mobile gaming, etc. And it is possible to monitor multi-source physiological and behavioral signals by wearable or non-contact technologies. Meanwhile, we may establish psychological and cognitive evaluation models with good generalization performance in complex scenarios such as cross-subjects and cross-devices. Therefore, we can conduct psychological and cognitive clinical evaluation and verify the interventions, with physiological and behavioral computing in tasks. This forms a comprehensive system of giving tasks, monitoring physiology and behavior, evaluating the psychological and cognitive function, and regulating intervention.
The research on psychological and cognitive evaluation and intervention using physiological and behavioral computing aims to improve the effectiveness of the tasks, to improve the accuracy and generalization of models based on physiological and behavioral data, and to explore clinical implications. Psychological and cognitive evaluation and intervention tasks are the core of these tasks. Physiological and behavioral data include measurements from EEG, ECG, skin electrical conductivity, eye movement, expression, voice, limb movement etc. Deep learning model is mainly established based on wearable or non-contact physiological and behavioral perception data. It can be used to perform psychological calculation and cognitive evaluation, monitoring, and intervention training for psychological or cognitive disorders.
This Research Topic is therefore to summarize the foundations and technologies of physiological and behavioral computing for psychological and cognitive evaluation and intervention, including but not limited to the topics below. This Research Topic welcomes submissions of the following article types: Brief Research Report, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Systematic Review and Technology and Code.
Topics of Interest:
- Task design of specific mental states and cognitive assessment using virtual reality, gaming, language, new scales, and so on
- Acquisition and processing of physiological and behavioral data induced by virtual reality stimulation
- Psychological calculation based on physiological signals such as EEG, ECG, and eye movement and behavioral data
- Cognitive evaluation methods with multimodal signal fusion
- Models based on physiological and behavioral data in complex scenarios such as cross-subject and cross-device
- Cognitive function evaluation and training using virtual reality
- Applications of virtual reality in psychology, neuroscience, and brain science
- Application of artificial intelligence in mental health monitoring
- Evaluation and application of virtual reality or neurofeedback in anxiety and depression
- Application of wearable devices in mental computing, cognitive assessment, and interventions
The acquisition and processing of physiological and behavioral signals is an important means of psychological and cognitive evaluation. It is a way to realize human-machine interaction. Traditional physiological and cognitive research based on physiological and behavioral computing faces various challenges, including: 1) The relatively simple task in paradigm using traditional computer media, such as pictures and texts, is not effective; 2) The performance of machine learning models based on physiological and behavioral data is relatively poor in some complex scenarios; and 3) Psychological and cognitive assessment and intervention by physiological and behavioral computing lack corresponding clinical validation.
With technology advancement, now it is possible to design effective tasks in a paradigm using virtual reality, new scales, mobile gaming, etc. And it is possible to monitor multi-source physiological and behavioral signals by wearable or non-contact technologies. Meanwhile, we may establish psychological and cognitive evaluation models with good generalization performance in complex scenarios such as cross-subjects and cross-devices. Therefore, we can conduct psychological and cognitive clinical evaluation and verify the interventions, with physiological and behavioral computing in tasks. This forms a comprehensive system of giving tasks, monitoring physiology and behavior, evaluating the psychological and cognitive function, and regulating intervention.
The research on psychological and cognitive evaluation and intervention using physiological and behavioral computing aims to improve the effectiveness of the tasks, to improve the accuracy and generalization of models based on physiological and behavioral data, and to explore clinical implications. Psychological and cognitive evaluation and intervention tasks are the core of these tasks. Physiological and behavioral data include measurements from EEG, ECG, skin electrical conductivity, eye movement, expression, voice, limb movement etc. Deep learning model is mainly established based on wearable or non-contact physiological and behavioral perception data. It can be used to perform psychological calculation and cognitive evaluation, monitoring, and intervention training for psychological or cognitive disorders.
This Research Topic is therefore to summarize the foundations and technologies of physiological and behavioral computing for psychological and cognitive evaluation and intervention, including but not limited to the topics below. This Research Topic welcomes submissions of the following article types: Brief Research Report, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Systematic Review and Technology and Code.
Topics of Interest:
- Task design of specific mental states and cognitive assessment using virtual reality, gaming, language, new scales, and so on
- Acquisition and processing of physiological and behavioral data induced by virtual reality stimulation
- Psychological calculation based on physiological signals such as EEG, ECG, and eye movement and behavioral data
- Cognitive evaluation methods with multimodal signal fusion
- Models based on physiological and behavioral data in complex scenarios such as cross-subject and cross-device
- Cognitive function evaluation and training using virtual reality
- Applications of virtual reality in psychology, neuroscience, and brain science
- Application of artificial intelligence in mental health monitoring
- Evaluation and application of virtual reality or neurofeedback in anxiety and depression
- Application of wearable devices in mental computing, cognitive assessment, and interventions
