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.

Shaojie LI;Wei LI;Zejian XING;Wenjie YUAN;Xiangyu WEI;Xiaowei ZHANG;Bin HU

School of Information Science and Engineering,Lanzhou University,Lanzhou 730099,China

信息与电子工程前沿（英文）

[1]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].信息与电子工程前沿（英文）,2022(08):1158-1173

AMIGOS

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