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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Engagement ability plays a fundamental role in allocating attentional resources and helps us perform daily tasks efficiently. Therefore, it is of great importance to recognize engagement level. Electroencephalography is frequently employed to recognize engagement for its objective and harmless nature. To fully exploit the information contained in EEG signals, an engagement recognition method integrating multi-domain information is proposed. The proposed method extracts frequency information by a filter bank. In order to utilize spatial information, the correlation-based common spatial patterns method is introduced and extended into three versions by replacing different correlation coefficients. In addition, the Hilbert transform helps to obtain both amplitude and phase information. Finally, features in three domains are combined and fed into a support vector machine to realize engagement recognition. The proposed method is experimentally validated on an open dataset composed of 29 subjects. In the comparison with six existing methods, it achieves the best accuracy of 87.74±5.98% in binary engagement recognition with an improvement of 4.03%, which proves its efficiency in the engagement recognition field.

Details

Title
Engagement Recognition Using a Multi-Domain Feature Extraction Method Based on Correlation-Based Common Spatial Patterns
Author
Xu, Guiying 1   VIAFID ORCID Logo  ; Wang, Zhenyu 2 ; Xu, Tianheng 2   VIAFID ORCID Logo  ; Zhou, Ting 3 ; Hu, Honglin 2 

 Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China 
 Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China 
 School of Microelectronics, Shanghai University, Shanghai 201800, China 
First page
11924
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2888111352
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.