In recent years the interdisciplinary field of neuroergonomics, pioneered by Raja Parasuraman et al., has come to the fore with the advent of new noninvasive technologies for monitoring brain function and studying human behavior in relation to systems and machines. Neuroergonomics is defined as the study of brain and behavior at work by combining two disciplines, cognitive neuroscience and ergonomics. The goal of the neuroergonomics field is to use the discoveries and increased knowledge of human brain and physiological functioning to improve human performance or expand/augment human cognitive capabilities and also to optimize the fit between humans and technology.
Significant progress has been made in methodologies and tools for investigating human brain and behavior in relation to performance at work and everyday settings. The availability of ambulatory hardware, wearable sensors and advanced data modeling and analysis techniques allow for neuroimaging in real-world environments. The EEG, functional near-infrared spectroscopy (fNIRS), and stimulation approaches have been utilized to measure and even alter brain activities while allowing full-body movements in applied environments. The application of neuroimaging techniques in real-world scenarios is highly relevant in the burgeoning field of neuroergonomics. Traditionally, neuroimaging experiments tend to avoid active movement of the subject for fear of artifacts. To overcome this problem, more sophisticated data analysis approaches have to be developed to remove artifacts and motion-related activities. Understanding how human operators perform in complex tasks has implications for safety, efficiency, productivity, and design in a range of complex and safety-critical systems from the sectors of public transportation, nuclear industry and healthcare. Adaptive automation (AA) refers to a human-machine system that makes use of real-time assessment of the operator’s workload to enhance performance. To implement AA, the system must include an estimator or classifier for real-time and accurate assessment of the Operator Functional State (OFS). Passive BCIs allow for online assessment of various aspects of OFS such that systems can automatically adapt to their operator. This neuro-adaptive technology may lead to continuous monitoring and assessment of cognitive and affective aspects of the OFS. Hence, deployment of portable neuroimaging devices in online settings enables us to assess cognitive and emotional states of a human operator performing safety-critical tasks.
With an aim to comprehensively review the recent progresses in both neuroergonomics and AA, this Research Topic calls for submissions that cover new theory, approaches, algorithms and technologies in the following areas (but not limited to): - Human-automation interaction - Human-system integration - Adaptive automation - OFS analysis - Human performance modeling and assessment - Quantitative assessment of mental workload/stress - Work memory - Multimodal neuroimaging - Affective computing and emotion recognition - Brain/neural signal processing - Advanced data analysis techniques: computational intelligence, machine learning, statistical modeling, data fusion/integration, etc. - Practical applications: driving safety, BCI for rehabilitation of physically disabled person, etc.
The Topic Editors would like to recognize Dr. Zhong Yin for his contribution to the development of this Research Topic proposal.
Important Note: All submissions/contributions to this Research Topic must be in line with the scope of the journal and section they are submitted to. While authors are encouraged to draw from other disciplines to enrich their papers where relevant, they must ensure papers fall within the section, as expressed in its Scope.
In recent years the interdisciplinary field of neuroergonomics, pioneered by Raja Parasuraman et al., has come to the fore with the advent of new noninvasive technologies for monitoring brain function and studying human behavior in relation to systems and machines. Neuroergonomics is defined as the study of brain and behavior at work by combining two disciplines, cognitive neuroscience and ergonomics. The goal of the neuroergonomics field is to use the discoveries and increased knowledge of human brain and physiological functioning to improve human performance or expand/augment human cognitive capabilities and also to optimize the fit between humans and technology.
Significant progress has been made in methodologies and tools for investigating human brain and behavior in relation to performance at work and everyday settings. The availability of ambulatory hardware, wearable sensors and advanced data modeling and analysis techniques allow for neuroimaging in real-world environments. The EEG, functional near-infrared spectroscopy (fNIRS), and stimulation approaches have been utilized to measure and even alter brain activities while allowing full-body movements in applied environments. The application of neuroimaging techniques in real-world scenarios is highly relevant in the burgeoning field of neuroergonomics. Traditionally, neuroimaging experiments tend to avoid active movement of the subject for fear of artifacts. To overcome this problem, more sophisticated data analysis approaches have to be developed to remove artifacts and motion-related activities. Understanding how human operators perform in complex tasks has implications for safety, efficiency, productivity, and design in a range of complex and safety-critical systems from the sectors of public transportation, nuclear industry and healthcare. Adaptive automation (AA) refers to a human-machine system that makes use of real-time assessment of the operator’s workload to enhance performance. To implement AA, the system must include an estimator or classifier for real-time and accurate assessment of the Operator Functional State (OFS). Passive BCIs allow for online assessment of various aspects of OFS such that systems can automatically adapt to their operator. This neuro-adaptive technology may lead to continuous monitoring and assessment of cognitive and affective aspects of the OFS. Hence, deployment of portable neuroimaging devices in online settings enables us to assess cognitive and emotional states of a human operator performing safety-critical tasks.
With an aim to comprehensively review the recent progresses in both neuroergonomics and AA, this Research Topic calls for submissions that cover new theory, approaches, algorithms and technologies in the following areas (but not limited to): - Human-automation interaction - Human-system integration - Adaptive automation - OFS analysis - Human performance modeling and assessment - Quantitative assessment of mental workload/stress - Work memory - Multimodal neuroimaging - Affective computing and emotion recognition - Brain/neural signal processing - Advanced data analysis techniques: computational intelligence, machine learning, statistical modeling, data fusion/integration, etc. - Practical applications: driving safety, BCI for rehabilitation of physically disabled person, etc.
The Topic Editors would like to recognize Dr. Zhong Yin for his contribution to the development of this Research Topic proposal.
Important Note: All submissions/contributions to this Research Topic must be in line with the scope of the journal and section they are submitted to. While authors are encouraged to draw from other disciplines to enrich their papers where relevant, they must ensure papers fall within the section, as expressed in its Scope.