Brain-computer interfaces (BCIs) provide a direct communication pathway between brain activity and an external device. They are of great value and can strength or replace human peripheral working capabilities, with potential applications in different fields, including clinical and biomedical domains, rehabilitation, robotics, entertainment and many others. During the past few years, many approaches have been explored in terms of types of neurological sources of information, feature extraction, and intention prediction for BCI applications and significant research efforts have delivered common platforms for technology standardization. In particular, new research directions aim at combining this technology with brain connectivity modeling. The goal is to describe the interactions among different brain regions as connectivity patterns that reflect the dynamics of information flow either at rest or while performing a task, holding a great potential to further improve the performance of BCI systems.
This Research Topic aims at presenting rigorous neuroscientific studies and engineering applications investigating all aspects of BCI systems relying on brain connectivity. Functional and effective connectivity represent a complex system considering the transient nature of the interactions, such as synchronizations and desynchronizations between different brain regions. Therefore, comparing the cortical connections within different brain rhythms and summarizing the related information is challenging. Although BCI studies exploring advanced brain connectivity have become more popular in the last years, there still exist many unsolved fundamental problems, such as how to interpret the interactions, especially when causal interactions are estimated, and testing usability for online operations.
This Research Topic seeks articles on the design, development, and implementation of BCIs based on brain connectivity. Topics of interest include, but are not limited to:
• Non-invasive BCIs using different technologies: electroencephalography (EEG), magnetoencephalography (MEG), functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS) and others
• Invasive BCIs
• Exogenous and endogenous BCIs
• Assistive, adaptive, and rehabilitative BCIs for health and well-being in older age
• Multidimensional BCIs and multifunctional hybrid BCIs
• Passive BCIs: mental state detection for psychological well-being
• Advanced algorithms for assisted healthy living
• Developments of BCIs based on connectivity for different purposes (neurorehabilitation, immersive virtual reality, gaming and so on...)
• Transfer Learning and BCI
• Deep learning (CNN, RNN, GAN, etc.) and BCI
Brain-computer interfaces (BCIs) provide a direct communication pathway between brain activity and an external device. They are of great value and can strength or replace human peripheral working capabilities, with potential applications in different fields, including clinical and biomedical domains, rehabilitation, robotics, entertainment and many others. During the past few years, many approaches have been explored in terms of types of neurological sources of information, feature extraction, and intention prediction for BCI applications and significant research efforts have delivered common platforms for technology standardization. In particular, new research directions aim at combining this technology with brain connectivity modeling. The goal is to describe the interactions among different brain regions as connectivity patterns that reflect the dynamics of information flow either at rest or while performing a task, holding a great potential to further improve the performance of BCI systems.
This Research Topic aims at presenting rigorous neuroscientific studies and engineering applications investigating all aspects of BCI systems relying on brain connectivity. Functional and effective connectivity represent a complex system considering the transient nature of the interactions, such as synchronizations and desynchronizations between different brain regions. Therefore, comparing the cortical connections within different brain rhythms and summarizing the related information is challenging. Although BCI studies exploring advanced brain connectivity have become more popular in the last years, there still exist many unsolved fundamental problems, such as how to interpret the interactions, especially when causal interactions are estimated, and testing usability for online operations.
This Research Topic seeks articles on the design, development, and implementation of BCIs based on brain connectivity. Topics of interest include, but are not limited to:
• Non-invasive BCIs using different technologies: electroencephalography (EEG), magnetoencephalography (MEG), functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS) and others
• Invasive BCIs
• Exogenous and endogenous BCIs
• Assistive, adaptive, and rehabilitative BCIs for health and well-being in older age
• Multidimensional BCIs and multifunctional hybrid BCIs
• Passive BCIs: mental state detection for psychological well-being
• Advanced algorithms for assisted healthy living
• Developments of BCIs based on connectivity for different purposes (neurorehabilitation, immersive virtual reality, gaming and so on...)
• Transfer Learning and BCI
• Deep learning (CNN, RNN, GAN, etc.) and BCI
