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Review Article | Open Access

A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022

Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, Guangdong, China
Jiangmen Brain-like Computation and Hybrid Intelligence Research Center, Jiangmen 529020, Guangdong, China
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Abstract

The rapid serial visual presentation (RSVP) paradigm has garnered considerable attention in brain–computer interface (BCI) systems. Studies have focused on using cross-subject electroencephalogram data to train cross-subject RSVP detection models. In this study, we performed a comparative analysis of the top 5 deep learning algorithms used by various teams in the event-related potential competition of the BCI Controlled Robot Contest in World Robot Contest 2022. We evaluated these algorithms on the final data set and compared their performance in cross-subject RSVP detection. The results revealed that deep learning models can achieve excellent results with appropriate training methods when applied to cross-subject detection tasks. We discussed the limitations of existing deep learning algorithms in cross-subject RSVP detection and highlighted potential research directions.

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Brain Science Advances
Pages 195-209
Cite this article:
Wang Z, Zhang H, Ji Z, et al. A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022. Brain Science Advances, 2023, 9(3): 195-209. https://doi.org/10.26599/BSA.2023.9050013

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Received: 14 March 2023
Revised: 03 April 2023
Accepted: 08 May 2023
Published: 05 September 2023
© The authors 2023.

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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