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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.


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An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task

Show Author's information Hongfei ZhangZehui WangYinhu YuHaojun YinChuangquan ChenHongtao Wang( )
Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, Guangdong, China

Abstract

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.

Keywords: event-related potential, rapid serial visual presentation, electroencephalogram, EEGNet, subject-specific model

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Received: 08 February 2022
Revised: 11 March 2022
Accepted: 21 March 2022
Published: 29 June 2022
Issue date: June 2022

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© The authors 2022.

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Acknowledgements

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), the Wuyi University and Hong Kong & Macao Joint Research Project (Grant No. 2019WGALH16), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2020A1515111154), and the Characteristic Innovation Projects of Ordinary Universities in Guangdong Province (Grant No. 2021KTSCX136).

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