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Research Article | Open Access

Review of training-free event-related potential classification approaches in the World Robot Contest 2021

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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Abstract

Recently, rapid serial visual presentation (RSVP), as a new event- related potential (ERP) paradigm, has become one of the most popular forms in electroencephalogram signal processing technologies. Several improvement approaches have been proposed to improve the performance of RSVP analysis. In brain-computer interface systems based on RSVP, the family of approaches that do not depend on training specific parameters is essential. The participating teams proposed several effective training-free frameworks of algorithms in the ERP competition of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper discusses the effectiveness of various approaches in improving the performance of the system without requiring training and suggests how to apply these approaches in a practical system. First, appropriate preprocessing techniques will greatly improve the results. Then, the non-deep learning algorithm may be more stable than the deep learning approach. Furthermore, ensemble learning can make the model more stable and robust.

Keywords

brain-computer interfaceselectroencephalogramrapid serial visual presentation (RSVP)data imbalancetraining-free

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Brain Science Advances
Volume 8 Issue 2,
June 2022
Pages 82-98
DOI: 10.26599/BSA.2022.9050001
Cite this article:
Wu H, Wu D. Review of training-free event-related potential classification approaches in the World Robot Contest 2021. Brain Science Advances, 2022, 8(2): 82-98. https://doi.org/10.26599/BSA.2022.9050001

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Received: 13 January 2022
Revised: 20 February 2022
Accepted: 04 March 2022
Published: 29 June 2022
© The authors 2022.

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).
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