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

Author:

Wang Zehui12,Zhang Hongfei12,Ji Zhouyu12,Yang Yuliang12,Wang Hongtao12

Affiliation:

1. Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, Guangdong, China

2. Jiangmen Brain-like Computation and Hybrid Intelligence Research Center, Jiangmen 529020, Guangdong, China

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.

Publisher

Tsinghua University Press

Subject

Microbiology (medical),Immunology,Immunology and Allergy

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