As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target i...As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition.This paper proposed an improved EEGNet model to achieve good performance in offline and online data.Specifically,the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram(EEG)signals.The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples.Additionally,the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model.We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place.The average recall rate of the four participants reached 51.56%in triple classification.In the offline data benchmark dataset(64 subjects-RSVP tasks),the average recall rates of groups A and B reached 76.07%and 78.11%,respectively.We provided an alternative method to identify targets based on the RSVP paradigm.展开更多
基金This work is granted by 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)+2 种基金the Wuyi University and Hong Kong&Macao Joint Research Project(Grant No.2019WGALH16)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2020A1515111154)the Characteristic Innovation Projects of Ordinary Universities in Guangdong Province(Grant No.2021KTSCX136).
文摘As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition.This paper proposed an improved EEGNet model to achieve good performance in offline and online data.Specifically,the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram(EEG)signals.The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples.Additionally,the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model.We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place.The average recall rate of the four participants reached 51.56%in triple classification.In the offline data benchmark dataset(64 subjects-RSVP tasks),the average recall rates of groups A and B reached 76.07%and 78.11%,respectively.We provided an alternative method to identify targets based on the RSVP paradigm.
