Datasets for Brain-Computer Interface Applications

180.9K

views

87

authors

16

articles

Datasets for Brain-Computer Interface Applications

180.9K

views

87

authors

16

articles

Editorial

29 September 2021

Editorial: Datasets for Brain-Computer Interface Applications

Ian Daly

,

Ana Matran-Fernandez

,

Davide Valeriani

,

Mikhail Lebedev

and

Andrea Kübler

3,408 views

3 citations

Editors

5

Department of Mathematical Analysis, Faculty of Mechanics and Mathematics, Lomonosov Moscow State University

Andrea Kübler

Julius Maximilian University of Würzburg

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About

The Volume II of this Research Topic is available here.

Brain-computer interfaces (BCIs) are exciting technologies that provide channels of communication between the brain and a computer system. They can be used as communication devices, rehabilitation systems, entertainment devices, and for a whole host of other applications.

Research in BCI is developing rapidly and is a highly multidisciplinary field, involving neuroscientists, engineers, psychologists, computer scientists, clinicians etc. However, reliability and reproducibility of BCI research is held back by a dearth of high quality, publicly available, datasets with which new systems, tools, and technologies, can be evaluated and compared. Indeed the small number of high-quality datasets that are publicly available date back up to 10 years and are now at risk of being over-fit due to frequent use.

This Research Topic seeks to provide a place to collate descriptions, reviews, surveys, and evaluations of publicly available physiological datasets recorded during training and evaluation of BCI systems from leading BCI research labs around the world. This will allow further use of these datasets in development of future BCI systems and encourage high quality and rapid continual development of BCI systems by providing multiple datasets upon which new methods may be rigorously evaluated and compared.

This collection of articles also seeks to provide a platform for new scientific publications, literature reviews, and any other article types related to the availability and use of public datasets in BCI research.

Additionally, all authors are encouraged to upload their data sets on
the BNCI Horizon 2020 web repository to make them available for the
community. Thus, our Research Topic will provide a collated collection of BCI datasets and their descriptions as a resource for future BCI developers.

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Editors

Department of Mathematical Analysis, Faculty of Mechanics and Mathematics, Lomonosov Moscow State University

Andrea Kübler

Julius Maximilian University of Würzburg

Impact

180,945 Total views

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Published In

Frontiers in Neuroscience

Neural Technology

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