Connectionists: Special issue: Advances and Challenges to Bridge Computational Intelligence and Neuroscience for Brain-computer Interface

[Avinash K Singh]

Dear All,

Brain-computer interfaces (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 context, 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


Topic Editor(s):

   -   Avinash Kumar Singh, University of Technology Sydney, Sydney,
   Australia
   -   Luigi Bianchi, University of Rome Tor Vergata, Roma, Italy
   -   Davide Valeriani, Neurable Inc., Boston, United States
   -   Masaki Nakanishi, Institute for Neural Computation, University of
   California, San Diego, San Diego, United States


Journal/Specialty: Frontiers in Neuroergonomics - section Neurotechnology
and Systems Neuroergonomics

Research Topic Title: Advances and Challenges to Bridge Computational
Intelligence and Neuroscience for Brain-computer Interface

Manuscripts can be submitted directly here:
https://www.frontiersin.org/research-topics/27045

Here are quick links to:

   - Author guidelines: https://www.frontiersin.org/about/author-guidelines
   - List of article types and publishing fees:
   https://www.frontiersin.org/about/publishing-fees


  Kind Regards,

  Avinash Kumar Singh
  Topic Editor,
  Neurotechnology and Systems Neuroergonomics Section,
  Frontiers in Neuroergonomics
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