Comparison of cross-subject EEG emotion recognition algorithms in the BCI Controlled Robot Contest in World Robot Contest 2021

Chao Tang; Yunhuan Li; Badong Chen

doi:10.26599/BSA.2022.9050013

Brain Science Advances 2022, 8(2): 142-152 https://doi.org/10.26599/BSA.2022.9050013

Research Article |

Open Access | Issue | Published: 29 June 2022

Comparison of cross-subject EEG emotion recognition algorithms in the BCI Controlled Robot Contest in World Robot Contest 2021

Show Author's Information Hide Author's Information Chao Tang^{^§}, Yunhuan Li^{^§}, Badong Chen(

)

Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China

^§ These authors contributed equally to this work.

Keywords:

electroencephalography, emotion recognition, brain-computer interface, online decoding, cross-subject

Cite this article:

Tang C, Li Y, Chen B. Comparison of cross-subject EEG emotion recognition algorithms in the BCI Controlled Robot Contest in World Robot Contest 2021. Brain Science Advances, 2022, 8(2): 142-152. https://doi.org/10.26599/BSA.2022.9050013

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Abstract

Electroencephalogram (EEG) data depict various emotional states and reflect brain activity. There has been increasing interest in EEG emotion recognition in brain-computer interface systems (BCIs). In the World Robot Contest (WRC), the BCI Controlled Robot Contest successfully staged an emotion recognition technology competition. Three types of emotions (happy, sad, and neutral) are modeled using EEG signals. In this study, 5 methods employed by different teams are compared. The results reveal that classical machine learning approaches and deep learning methods perform similarly in offline recognition, whereas deep learning methods perform better in online cross-subject decoding.

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Comparison of cross-subject EEG emotion recognition algorithms in the BCI Controlled Robot Contest in World Robot Contest 2021

Show Author's information Hide Author's Information Chao Tang^{^§}, Yunhuan Li^{^§}, Badong Chen(

)

Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China

^§ These authors contributed equally to this work.

Abstract

Keywords: electroencephalography, emotion recognition, brain-computer interface, online decoding, cross-subject

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Acknowledgements

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Publication history

Received: 18 April 2022

Revised: 20 May 2022

Accepted: 30 May 2022

Published: 29 June 2022

Issue date: June 2022

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. U21A20485, 61976175).

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