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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.


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

Show Author's information Huanyu WuDongrui Wu( )
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

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 interfaces, electroencephalogram, rapid serial visual presentation (RSVP), data imbalance, training-free
Received: 13 January 2022 Revised: 20 February 2022 Accepted: 04 March 2022 Published: 29 June 2022 Issue date: June 2022
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Publication history

Received: 13 January 2022
Revised: 20 February 2022
Accepted: 04 March 2022
Published: 29 June 2022
Issue date: June 2022

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© The authors 2022.

Acknowledgements

Acknowledgements

This research was supported by the National Key Research and Development Program of China (Grant No. 2021ZD0201303), the Technology Innovation Project of Hubei Province of China (Grant No. 2019AEA171), and the Hubei Province Funds for Distinguished Young Scholars (Grant No. 2020CFA050).

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