About this Research Topic
Real-world contexts are dynamic and often involve cooperation between people as part of a wider setting such as familiarization and interactions with more devices (e.g. phone, wristbands, laptop, automated systems) or interactions with other family members in their home, or interactions as part of a team in their work setting. The capability of gathering and analysing neurophysiological data, in the most ecological environment possible and without interfering with the users and execution of tasks, would be a great advantage for extracting genuine data and beneficial for forming meaningful knowledge related to mental states. Wearable sensors are going through a significant technological progress and they are proving useful in many practical application domains.
The capability to non-invasively gather neurophysiological data and assess the user’s mental and emotional states can support the investigation of individual human as well as team and group behaviors. Ultimately, a better understanding of these psycho- and physiological mechanisms can allow the design of interactive technologies tailored to working and living situations thus improving the conditions in social and professional contexts. For example, being able to monitor stressful and high mental workload while performing certain working tasks can be used in the short-term to trigger adaptive automation. In turn, this can support end-users to deal with extreme circumstances, and in the long-term this can prevent mental and physical impairments, fatigue, and depression, or assess when the familiarization with the considered working task has been accomplished.
The main objective of this Research Topic is to focus on innovative and promising approaches, methodologies, and technologies to collect, analyse, and assess individual user behaviours as well as users in teams/groups while performing real-world activities in their natural (home/work/social) settings. All research studies focused on brain learning processes, physiological mechanisms underpinning cognitive functions and social/professional interactions by employing neuroimaging, neurostimulation, neuropharmacology, and neurogenetics methods, are welcome. Specials reviews highlighting the most promising findings in this field are also encouraged.
Areas covered by this section include, but are not limited to:
● Modelling and assessment of mental states and physical/psychological impairments/disorders
● Social Interaction Understanding and Evaluation
● Group/Team Behavior/Performance Assessment
● Intra- and Inter-personal Brain and Social Behavior Synchrony
● Human-Machine Interaction (HMI)
● Human-Automation Teaming
● Adaptive Automation
● Trustworthiness in Automation
● Human Performance Envelope (HPE)
● Learning and Neuroplasticity
● Passive Brain Computer Interaction (pBCI)
● Hyper-scanning and Multi-brain Approaches
● Wearable technologies
● Applications of machine learning approaches for mental/affective state understanding
● Neuro-physiological Artificial Intelligence
● Multi-modality for neuro-physiological assessment
● Tools, datasets and testbeds
● Deployed and/or emerging applications
● Ethical considerations
All types of manuscripts are considered, including original basic science reports, translational research, clinical studies, review articles, opinion, and methodology papers.
The capability to non-invasively gather neurophysiological data and assess the user’s mental and emotional states can support the investigation of individual human as well as team and group behaviors. Ultimately, a better understanding of these psycho- and physiological mechanisms can allow the design of interactive technologies tailored to working and living situations thus improving the conditions in social and professional contexts. For example, being able to monitor stressful and high mental workload while performing certain working tasks can be used in the short-term to trigger adaptive automation. In turn, this can support end-users to deal with extreme circumstances, and in the long-term this can prevent mental and physical impairments, fatigue, and depression, or assess when the familiarization with the considered working task has been accomplished.
The main objective of this Research Topic is to focus on innovative and promising approaches, methodologies, and technologies to collect, analyse, and assess individual user behaviours as well as users in teams/groups while performing real-world activities in their natural (home/work/social) settings. All research studies focused on brain learning processes, physiological mechanisms underpinning cognitive functions and social/professional interactions by employing neuroimaging, neurostimulation, neuropharmacology, and neurogenetics methods, are welcome. Specials reviews highlighting the most promising findings in this field are also encouraged.
Areas covered by this section include, but are not limited to:
● Modelling and assessment of mental states and physical/psychological impairments/disorders
● Social Interaction Understanding and Evaluation
● Group/Team Behavior/Performance Assessment
● Intra- and Inter-personal Brain and Social Behavior Synchrony
● Human-Machine Interaction (HMI)
● Human-Automation Teaming
● Adaptive Automation
● Trustworthiness in Automation
● Human Performance Envelope (HPE)
● Learning and Neuroplasticity
● Passive Brain Computer Interaction (pBCI)
● Hyper-scanning and Multi-brain Approaches
● Wearable technologies
● Applications of machine learning approaches for mental/affective state understanding
● Neuro-physiological Artificial Intelligence
● Multi-modality for neuro-physiological assessment
● Tools, datasets and testbeds
● Deployed and/or emerging applications
● Ethical considerations
All types of manuscripts are considered, including original basic science reports, translational research, clinical studies, review articles, opinion, and methodology papers.
Keywords: Wearable Technology, Contactless Assessment, Automation, Neuroplasticity, Mental and Physical Impairment, Pain, Depression, Hyperscanning, Brain-Computer Interface, Artificial Intelligence, Machine Learning, Team/Group Behavior Understanding, Neurophysiological Correlates, Mental Workload
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.
