The Brain-Computer Interface (BCI) concept dates back to the 1970s when cortical brain signals were first used to set up a brain-computer communication based on visual-evoked cortical potential (VEPs). BCI was classically defined as “a communication system in which messages or commands towards the external world bypass the brain normal output pathways of peripheral nerves and muscles”.
BCI technology has rapidly and considerably advanced in formulation and aims. In the last decade, the BCI field went through a gigantic revolution: its concept evolved from the “overt” detection of human intention to the “covert” assessment of human mental states. The latter form has been defined as “passive BCI”, since its innovative application consists in deriving its outputs from brain activity arising without the purpose of voluntary control, but implicitly related to human mental states. In practical terms, an example could be an Air Traffic Controller (ATCO) dealing with his/her interface. A passive BCI system would be able to recognize an eventual ATCO vigilance decrease under a certain threshold, as well as the increase of his/her level of mental workload, counteracting such degradations by enabling aids and solutions (e.g. if the workload is too high, the system will enable suggestions for the operator, or graphical solutions to highlight items, etc.) to support the ATCO in recovering and maintaining proper mental conditions. Passive BCI represents the implicit channel of information, enhancing a goal-oriented cooperation between humans and machines, the so-called Human–Machine Interaction.
Due to the need of monitoring human mental states with user-centered applications, the interest in passive BCIs has greatly increased, in particular in safety-critical operational environments. In this context, passive BCIs would be beneficial for improving human performance, health, and safety. Such a forefront application of BCIs is cross-disciplinary, embracing disciplines spanning from Neuroscience, to Computer Science, Psychology, Human Factor, and Data Science, converging in the Neuroergonomics novel field.
Abundant research data, investigating the possibility of developing effective passive BCI-based systems to enhance Human-Machine Interaction, exists. However, the majority is based on laboratory experiments and do not address the questions with a multidisciplinary approach, highlighting a lack of comprehensiveness. Despite the recent technological progress in enabling new solutions, considered inconceivable until a few years ago, it is still challenging to apply this technology to real-world purposes, because of cross-disciplinary lack.
This Research Topic aims to provide a collection of forefront works describing passive BCI -based systems employed outside the laboratory setting, i.e. in real-world applications. We welcome original contributions focusing on the use of human neurophysiological monitoring techniques (e.g. EEG, fNIRs, ECG, GSR, Eye Tracking) for the application of passive BCIs into the real world.
In addition to the application of the BCI-based system in real settings, works should tackle and provide original solutions to the issues reflecting typical real-world applications, such as:
• Sensors and wearable technology;
• Neurophysiological signal processing online algorithms (with attention to the intrinsically lower signal-to-noise ratio); and
• Machine-learning techniques to provide synthetic and reliable metrics of human mental states.
Editors are willing to consider all types of contributions, including reviews, that pertain to the topic. We require authors to submit their abstracts before submitting their manuscripts. Article submissions for this Research Topic are possible after acceptance of the abstract.
The Brain-Computer Interface (BCI) concept dates back to the 1970s when cortical brain signals were first used to set up a brain-computer communication based on visual-evoked cortical potential (VEPs). BCI was classically defined as “a communication system in which messages or commands towards the external world bypass the brain normal output pathways of peripheral nerves and muscles”.
BCI technology has rapidly and considerably advanced in formulation and aims. In the last decade, the BCI field went through a gigantic revolution: its concept evolved from the “overt” detection of human intention to the “covert” assessment of human mental states. The latter form has been defined as “passive BCI”, since its innovative application consists in deriving its outputs from brain activity arising without the purpose of voluntary control, but implicitly related to human mental states. In practical terms, an example could be an Air Traffic Controller (ATCO) dealing with his/her interface. A passive BCI system would be able to recognize an eventual ATCO vigilance decrease under a certain threshold, as well as the increase of his/her level of mental workload, counteracting such degradations by enabling aids and solutions (e.g. if the workload is too high, the system will enable suggestions for the operator, or graphical solutions to highlight items, etc.) to support the ATCO in recovering and maintaining proper mental conditions. Passive BCI represents the implicit channel of information, enhancing a goal-oriented cooperation between humans and machines, the so-called Human–Machine Interaction.
Due to the need of monitoring human mental states with user-centered applications, the interest in passive BCIs has greatly increased, in particular in safety-critical operational environments. In this context, passive BCIs would be beneficial for improving human performance, health, and safety. Such a forefront application of BCIs is cross-disciplinary, embracing disciplines spanning from Neuroscience, to Computer Science, Psychology, Human Factor, and Data Science, converging in the Neuroergonomics novel field.
Abundant research data, investigating the possibility of developing effective passive BCI-based systems to enhance Human-Machine Interaction, exists. However, the majority is based on laboratory experiments and do not address the questions with a multidisciplinary approach, highlighting a lack of comprehensiveness. Despite the recent technological progress in enabling new solutions, considered inconceivable until a few years ago, it is still challenging to apply this technology to real-world purposes, because of cross-disciplinary lack.
This Research Topic aims to provide a collection of forefront works describing passive BCI -based systems employed outside the laboratory setting, i.e. in real-world applications. We welcome original contributions focusing on the use of human neurophysiological monitoring techniques (e.g. EEG, fNIRs, ECG, GSR, Eye Tracking) for the application of passive BCIs into the real world.
In addition to the application of the BCI-based system in real settings, works should tackle and provide original solutions to the issues reflecting typical real-world applications, such as:
• Sensors and wearable technology;
• Neurophysiological signal processing online algorithms (with attention to the intrinsically lower signal-to-noise ratio); and
• Machine-learning techniques to provide synthetic and reliable metrics of human mental states.
Editors are willing to consider all types of contributions, including reviews, that pertain to the topic. We require authors to submit their abstracts before submitting their manuscripts. Article submissions for this Research Topic are possible after acceptance of the abstract.