CLC number: TP39
On-line Access: 2022-08-22
Received: 2021-10-14
Revision Accepted: 2022-08-29
Crosschecked: 2022-07-28
Cited: 0
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Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0003-2432-0482
https://orcid.org/0000-0002-5388-129X
Shaojie LI, Wei LI, Zejian XING, Wenjie YUAN, Xiangyu WEI, Xiaowei ZHANG, Bin HU. A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1158-1173.
@article{title="A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines",
author="Shaojie LI, Wei LI, Zejian XING, Wenjie YUAN, Xiangyu WEI, Xiaowei ZHANG, Bin HU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="8",
pages="1158-1173",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100489"
}
%0 Journal Article
%T A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines
%A Shaojie LI
%A Wei LI
%A Zejian XING
%A Wenjie YUAN
%A Xiangyu WEI
%A Xiaowei ZHANG
%A Bin HU
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 8
%P 1158-1173
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100489
TY - JOUR
T1 - A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines
A1 - Shaojie LI
A1 - Wei LI
A1 - Zejian XING
A1 - Wenjie YUAN
A1 - Xiangyu WEI
A1 - Xiaowei ZHANG
A1 - Bin HU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 8
SP - 1158
EP - 1173
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100489
Abstract: Affective brain–computer interfaces have become an increasingly important topic to achieve emotional intelligence in human–machine collaboration. However, due to the complexity of electroencephalogram (EEG) signals and the individual differences in emotional response, it is still a great challenge to design a reliable and effective model. Considering the influence of personality traits on emotional response, it would be helpful to integrate personality information and EEG signals for emotion recognition. This study proposes a personality-guided attention neural network that can use personality information to learn effective EEG representations for emotion recognition. Specifically, we first use a convolutional neural network to extract rich temporal and regional representations of EEG signals, and a special convolution kernel is designed to learn inter- and intra-regional correlations simultaneously. Second, inspired by the fact that electrodes within distinct brain scalp regions play different roles in emotion recognition, a personality-guided regional-attention mechanism is proposed to further explore the contributions of electrodes within a region and between regions. Finally, attention-based long short-term memory is designed to explore the temporal dynamics of EEG signals. Experiments on the AMIGOS dataset, which is a dataset for multimodal research for affect, personality traits, and mood on individuals and groups, show that the proposed method can significantly improve the performance of subject-independent emotion recognition and outperform state-of-the-art methods.
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