Brain-computer interfaces (BCIs) allow people to interact with the environment (computer, robots, wheelchair, etc.) by directly using brain signals bypassing the natural pathways of nerves and muscles. BCIs are showing tremendous progress within this decade, with more and more work trying to spin out of a constrained environment like controlled laboratories. BCIs are a highly interdisciplinary research field consisting of researchers from neuroscience, mathematics, computer science, engineering, etc. They contribute to BCIs by developing and proposing new methods, techniques, BCI paradigms, brain signals recording methods, devices, and generating lots of information and data. Although most of the information is regularly available from peer-reviewed platforms and shared over open access data and method repository, it faces an incredible challenge in interpreting, reusing, comparing, and benchmarking. This challenge is growing significantly between computational intelligence and neuroscience research due to the current flow of readily available tools and devices.
Most methods, data, techniques, etc., are available openly for the researcher to reuse. However, although openly available, such a multidisciplinary inclusion and their generated information create significant gaps in sharing methods and datasets, comparing results, and reproducing experiments. Such a gap exists because researchers only share domain-specific information that is not easy to interpret by researchers from other disciplines. Consider what would be needed to reproduce a steady-state visually evoked potential (SSVEP)-BCIs, besides sharing data. There is a need for information like the number of unique flickering stimuli presented to the user, the flickering rate, and signal processing specific details such as the impedance of electrodes, type of reference used, applied signal filters, appropriate labels, etc. These details are usually available in related publications but are hard to interpret for non-domain-specific researchers. This special issue aims to attract researchers from the multidisciplinary domain of BCI, particularly focused on computational intelligence and neuroscience, to provide their advances and challenges in solving the problem of bridging such an interdisciplinary research field.
In this research topic, we welcome studies that help find a unique approach to solve the problem of unifying computational intelligence and the neuroscience community for BCI development. Therefore, we are looking for research studies on different techniques in machine learning, novel framework in BCI, unified format for terminologies and representation, automated tool to convert large open-source datasets, case studies of a converted dataset. We welcome original research, review, methods, and perspective articles that cover, but are not limited to, the following topics:
• Novel frameworks for BCI data resharing
• Unified functional models of BCI
• Automated machine learning tools and pipelines to populate metadata in BCI
• Methods, techniques, and tools to convert large open BCI dataset into a unified format
• Benchmarking approaches for BCI
Brain-computer interfaces (BCIs) allow people to interact with the environment (computer, robots, wheelchair, etc.) by directly using brain signals bypassing the natural pathways of nerves and muscles. BCIs are showing tremendous progress within this decade, with more and more work trying to spin out of a constrained environment like controlled laboratories. BCIs are a highly interdisciplinary research field consisting of researchers from neuroscience, mathematics, computer science, engineering, etc. They contribute to BCIs by developing and proposing new methods, techniques, BCI paradigms, brain signals recording methods, devices, and generating lots of information and data. Although most of the information is regularly available from peer-reviewed platforms and shared over open access data and method repository, it faces an incredible challenge in interpreting, reusing, comparing, and benchmarking. This challenge is growing significantly between computational intelligence and neuroscience research due to the current flow of readily available tools and devices.
Most methods, data, techniques, etc., are available openly for the researcher to reuse. However, although openly available, such a multidisciplinary inclusion and their generated information create significant gaps in sharing methods and datasets, comparing results, and reproducing experiments. Such a gap exists because researchers only share domain-specific information that is not easy to interpret by researchers from other disciplines. Consider what would be needed to reproduce a steady-state visually evoked potential (SSVEP)-BCIs, besides sharing data. There is a need for information like the number of unique flickering stimuli presented to the user, the flickering rate, and signal processing specific details such as the impedance of electrodes, type of reference used, applied signal filters, appropriate labels, etc. These details are usually available in related publications but are hard to interpret for non-domain-specific researchers. This special issue aims to attract researchers from the multidisciplinary domain of BCI, particularly focused on computational intelligence and neuroscience, to provide their advances and challenges in solving the problem of bridging such an interdisciplinary research field.
In this research topic, we welcome studies that help find a unique approach to solve the problem of unifying computational intelligence and the neuroscience community for BCI development. Therefore, we are looking for research studies on different techniques in machine learning, novel framework in BCI, unified format for terminologies and representation, automated tool to convert large open-source datasets, case studies of a converted dataset. We welcome original research, review, methods, and perspective articles that cover, but are not limited to, the following topics:
• Novel frameworks for BCI data resharing
• Unified functional models of BCI
• Automated machine learning tools and pipelines to populate metadata in BCI
• Methods, techniques, and tools to convert large open BCI dataset into a unified format
• Benchmarking approaches for BCI
