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-subj...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.展开更多
基金the Special Projects in Key Fields Supported by the Technology Development Project of Guangdong Province(Grant No.2020ZDZX3018)the Special Fund for Science and Technology of Guangdong Province(Grant No.2020182)the Wuyi University and Hong Kong&Macao joint Research Project(Grant No.2019WGALH16)。
文摘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.
