Closed-Loop Brain Machine Interfaces (BMIs) are poised to be the future of neuroscience and hold tremendous promise for the treatment of various neurological disorders, such as Parkinson's, Alzheimer's, and seizures. These advanced neural interfaces (implants or wearables) should perform totally independent of the external/peripheral equipment or devices and internally integrate brain signals with processing algorithms to activate or silence part of the brain, produce motor and sensory outputs, and if it is necessary to exchange data, communicate with the external world. These devices have the potential to revolutionize the fields of medicine and rehabilitation, providing new therapies and treatments for a variety of neurological disorders. The closed-loop nature of these systems means they can dynamically adjust their output based on the brain's feedback and changing neurological signals, leading to improved accuracy and performance. Despite their potential, there are still significant technical challenges that must be overcome before Closed-Loop BMIs can be widely adopted in clinical settings. This call for papers from Frontier Neuroscience is aimed at encouraging researchers to advance the field, bring these innovative technologies to those in need, and publish their results. The hope is that through continued research and development, these cutting-edge technologies will soon become a reality and make a profound impact on the lives of those affected by neurological disorders.
Goal of the collection are the following:
->To advance the field of BMI via enabling precision closed-loop mechanisms.
->To bring innovative technologies to those in need.
->To address the technical challenges in the widespread adoption of Closed-Loop BMIs in clinical settings.
->To address the need for new therapies and treatments for neurological disorders.
->To publish cutting-edge research results.
Scope of the Research Topic includes, but is not limited to:
-> Advancing the field of Closed-Loop BMIs (hardware electronics and software)
-> Overcoming technical challenges in the widespread adoption of Closed-Loop BMIs in clinical settings
-> Developing new therapies and treatments for neurological disorders using Closed-Loop BMIs
-> Specific themes that contributors can address:
-> Design and development of Closed-Loop BMIs (implants or wearables)
-> Integration of brain signals with processing algorithms
-> Dynamic adjustment of output based on brain feedback
-> Improved accuracy and performance of Closed-Loop BMIs
-> Clinical applications of Closed-Loop BMIs
Types of manuscripts authors accepted in this collection are:
Original Research articles: original research results on the design, development, and testing of Closed-Loop BMIs
Review articles: comprehensive overviews of the current state of the field of Closed-Loop BMIs
Perspectives: commentary and opinion pieces on the potential impact of Closed-Loop BMIs and future directions for research
Case studies: descriptions of specific applications of Closed-Loop BMIs in treating neurological disorders.
Closed-Loop Brain Machine Interfaces (BMIs) are poised to be the future of neuroscience and hold tremendous promise for the treatment of various neurological disorders, such as Parkinson's, Alzheimer's, and seizures. These advanced neural interfaces (implants or wearables) should perform totally independent of the external/peripheral equipment or devices and internally integrate brain signals with processing algorithms to activate or silence part of the brain, produce motor and sensory outputs, and if it is necessary to exchange data, communicate with the external world. These devices have the potential to revolutionize the fields of medicine and rehabilitation, providing new therapies and treatments for a variety of neurological disorders. The closed-loop nature of these systems means they can dynamically adjust their output based on the brain's feedback and changing neurological signals, leading to improved accuracy and performance. Despite their potential, there are still significant technical challenges that must be overcome before Closed-Loop BMIs can be widely adopted in clinical settings. This call for papers from Frontier Neuroscience is aimed at encouraging researchers to advance the field, bring these innovative technologies to those in need, and publish their results. The hope is that through continued research and development, these cutting-edge technologies will soon become a reality and make a profound impact on the lives of those affected by neurological disorders.
Goal of the collection are the following:
->To advance the field of BMI via enabling precision closed-loop mechanisms.
->To bring innovative technologies to those in need.
->To address the technical challenges in the widespread adoption of Closed-Loop BMIs in clinical settings.
->To address the need for new therapies and treatments for neurological disorders.
->To publish cutting-edge research results.
Scope of the Research Topic includes, but is not limited to:
-> Advancing the field of Closed-Loop BMIs (hardware electronics and software)
-> Overcoming technical challenges in the widespread adoption of Closed-Loop BMIs in clinical settings
-> Developing new therapies and treatments for neurological disorders using Closed-Loop BMIs
-> Specific themes that contributors can address:
-> Design and development of Closed-Loop BMIs (implants or wearables)
-> Integration of brain signals with processing algorithms
-> Dynamic adjustment of output based on brain feedback
-> Improved accuracy and performance of Closed-Loop BMIs
-> Clinical applications of Closed-Loop BMIs
Types of manuscripts authors accepted in this collection are:
Original Research articles: original research results on the design, development, and testing of Closed-Loop BMIs
Review articles: comprehensive overviews of the current state of the field of Closed-Loop BMIs
Perspectives: commentary and opinion pieces on the potential impact of Closed-Loop BMIs and future directions for research
Case studies: descriptions of specific applications of Closed-Loop BMIs in treating neurological disorders.
