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Research Article | Open Access

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

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

electroencephalogramrapid serial visual presentationevent-related potentialEEGNetsubject-specific model

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Brain Science Advances
Volume 8 Issue 2,
June 2022
Pages 111-126
DOI: 10.26599/BSA.2022.9050007
Cite this article:
Zhang H, Wang Z, Yu Y, et al. An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task. Brain Science Advances, 2022, 8(2): 111-126. https://doi.org/10.26599/BSA.2022.9050007

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Received: 08 February 2022
Revised: 11 March 2022
Accepted: 21 March 2022
Published: 29 June 2022
© The authors 2022.

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).
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