A self-supervised data augmentation strategy for EEG-based emotion recognition

Yingxiao Qiao

doi:10.1016/j.inffus.2025.103279

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A self-supervised data augmentation strategy for EEG-based emotion recognition

2025-06-07

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Yingxiao Qiao

赵倩 (Qian Zhao) *

DOI：10.1016/j.inffus.2025.103279

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Abstract

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Due to the scarcity problem of electroencephalogram (EEG) data, building high-precision emotion recognition models using deep learning faces great challenges. In recent years, data augmentation has significantly enhanced deep learning performance. Therefore, this paper proposed an innovative self-supervised data augmentation strategy, named SSDAS-EER, to generate high-quality and various artificial EEG feature maps. Firstly, EEG feature maps were constructed by combining differential entropy (DE) and power spectral density (PSD) features to obtain rich spatial and spectral information. Secondly, a masking strategy was used to mask part of the EEG feature maps, which prompted the designed generative adversarial network (GAN) to focus on learning the unmasked feature information and effectively filled in the masked parts. Meanwhile, the elaborated GAN could accurately capture the distribution characteristics of spatial and spectral information, thus ensuring the quality of the generated artificial EEG feature maps. In particular, this paper introduced a self-supervised learning mechanism to further optimize the designed classifier with good generalization ability to the generated samples. This strategy integrated data augmentation and model training into an end-to-end pipeline capable of augmenting EEG data for each subject. In this study, a systematic experiment was conducted on the DEAP dataset, and the results showed that the proposed method achieved an average accuracy of 97.27% and 97.45% on all subjects in valence and arousal, respectively, which was 1.46% and 1.39% higher compared to the time before the strategy was applied. Simultaneously, the similarity between the generated EEG feature maps and the original EEG feature maps was verified. These results indicated that SSDAS-EER had significant performance improvement in EEG emotion recognition tasks, demonstrating its great potential in improving the efficiency of EEG data utilization and emotion recognition accuracy.

Keywords:

EEG

Emotion recognition

Data augmentation

Generative adversarial network

Self-supervised learning