@article{Wang2023, 
author = {Zehui Wang and Hongfei Zhang and Zhouyu Ji and Yuliang Yang and Hongtao Wang},
title = {A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022},
year = {2023},
journal = {Brain Science Advances},
volume = {9},
number = {3},
pages = {195-209},
keywords = {rapid serial visual presentation, brain–computer interface, cross-subject, deep learning, detection},
url = {https://www.sciopen.com/article/10.26599/BSA.2023.9050013},
doi = {10.26599/BSA.2023.9050013},
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.}
}