Chowdhury, Anirban and Andreu-Perez, Javier (2021) Clinical Brain-Computer Interface Challenge 2020 (CBCIC at WCCI2020): Overview, methods and results. IEEE Transactions on Medical Robotics and Bionics, 3 (3). pp. 661-670. DOI https://doi.org/10.1109/TMRB.2021.3098108
Chowdhury, Anirban and Andreu-Perez, Javier (2021) Clinical Brain-Computer Interface Challenge 2020 (CBCIC at WCCI2020): Overview, methods and results. IEEE Transactions on Medical Robotics and Bionics, 3 (3). pp. 661-670. DOI https://doi.org/10.1109/TMRB.2021.3098108
Chowdhury, Anirban and Andreu-Perez, Javier (2021) Clinical Brain-Computer Interface Challenge 2020 (CBCIC at WCCI2020): Overview, methods and results. IEEE Transactions on Medical Robotics and Bionics, 3 (3). pp. 661-670. DOI https://doi.org/10.1109/TMRB.2021.3098108
Abstract
In the field of brain-computer interface (BCI) research, the availability of high-quality open-access datasets is essential to benchmark the performance of emerging algorithms. The existing open-access datasets from past competitions mostly deal with healthy individuals’ data, while the major application area of BCI is in the clinical domain. Thus the newly proposed algorithms to enhance the performance of BCI technology are very often tested against the healthy subjects’ datasets only, which doesn’t guarantee their success on patients’ datasets which are more challenging due to the presence of more nonstationarity and altered neurodynamics. In order to partially mitigate this scarcity, Clinical BCI Challenge aimed to provide an open-access rich dataset of stroke patients recorded similar to a neurorehabilitation paradigm. Another key feature of this challenge is that unlike many competitions in the past, it was designed for algorithms in both with-in subject and cross-subject categories as a major thrust area of current BCI technology is to realize calibration-free BCI designs. In this paper, we have discussed the winning algorithms and their performances across both competition categories which may help develop advanced algorithms for reliable BCIs for real-world practical applications.
Item Type: | Article |
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Uncontrolled Keywords: | BCI; Brain-computer interface; Exoskeleton; Hand; Rehabilitation; Neurorehabilitation; Stroke; Dataset; Competition |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 03 Aug 2021 13:39 |
Last Modified: | 30 Oct 2024 16:14 |
URI: | http://repository.essex.ac.uk/id/eprint/30769 |
Available files
Filename: Preprint_competition_paper.pdf