Cross-subject EEG-based Emotion Recognition Using Adversarial Domain Adaption with Attention Mechanism

Cross-subject EEG-based emotion recognition (ER) is a rewarding work in real-life applications, due to individual differences between one subject and another subject. Most existing studies focus on training a subject-specific ER model. However, it is time-consuming and unrealistic to design the cust...

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Bibliographic Details
Published in2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 1140 - 1144
Main Authors Ye, Yalan, Zhu, Xin, Li, Yunxia, Pan, Tongjie, He, Wenwen
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.11.2021
Subjects
Adaptation models
Convolution
Design methodology
Electroencephalography
Emotion recognition
Emotions
Feature extraction
Neural networks
Neural Networks, Computer
Research Design
Reward
Training
Online AccessGet full text
ISSN2694-0604
DOI10.1109/EMBC46164.2021.9630777

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Summary:Cross-subject EEG-based emotion recognition (ER) is a rewarding work in real-life applications, due to individual differences between one subject and another subject. Most existing studies focus on training a subject-specific ER model. However, it is time-consuming and unrealistic to design the customized subject-specific model for a new subject in cross-subject scenarios. In this paper, we propose an Adversarial Domain Adaption with an Attention Mechanism method for EEG-based ER, namely ADAAM-ER, to decrease the individual discrepancy. ADAAM-ER consists of a Graph Convolution Neural Networks with CNNs (GCNN-CNNs) and an Adversarial Domain Adaption with a Level-wise Attention Mechanism (ADALAM). Specifically, GCNN-CNNs as a feature extractor, which constructs a broader feature space, is designed to obtain more discriminative features. And ADALAM, which can decrease the individual discrepancy by alignment of the more transferable feature regions, is introduced to further obtain the discriminative features with higher transferability. Consequently, the proposed ADAAM-ER method can design a more transferable emotion recognition model with more discriminative features for a new subject via improving transferability. Experimental results on the SEED dataset have verified the effectiveness of the proposed ADAAM-ER method with the mean accuracy of 86.58%.
ISSN:2694-0604
DOI:10.1109/EMBC46164.2021.9630777