Learning a robust unified domain adaptation framework for cross-subject EEG-based emotion recognition

• Published in: Biomedical signal processing and control Vol. 86; p. 105138
• Main Authors: Jiménez-Guarneros, Magdiel, Fuentes-Pineda, Gibran
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023
• Subjects: Deep learning; Electroencephalogram; Emotion recognition; Unsupervised domain adaptation; Deep learning; Unsupervised domain adaptation; Emotion recognition; Electroencephalogram
• DOI: 10.1016/j.bspc.2023.105138
• ISSN: 1746-8094

Over the last few years, unsupervised domain adaptation (UDA) based on deep learning has emerged as a solution to build cross-subject emotion recognition models from Electroencephalogram (EEG) signals, aligning the subject distributions within a latent feature space. However, most reported works have a common intrinsic limitation: the subject distribution alignment is coarse-grained, but not all of the feature space is shared between subjects. In this paper, we propose a robust unified domain adaptation framework, named Multi-source Feature Alignment and Label Rectification (MFA-LR), which performs a fine-grained domain alignment at subject and class levels, while inter-class separation and robustness against input perturbations are encouraged in coarse grain. As a complementary step, a pseudo-labeling correction procedure is used to rectify mislabeled target samples. Our proposal was assessed over two public datasets, SEED and SEED-IV, on each of the three available sessions, using leave-one-subject-out cross-validation. Experimental results show an accuracy performance of up to 89.11 ± 07.72% and 74.99 ± 12.10% for the best session on SEED and SEED-IV, as well as an average accuracy of 85.27% and 69.58% on all three sessions, outperforming state-of-the-art results. •We proposed the MFA-LR framework for cross-subject EEG-based emotion recognition.•MFA-LR performs a fine-grained alignment and an inter-class coarse-grain separation.•We performed an extensive evaluation to demonstrate the robustness of MFA-LR.•Our proposal outperforms state-of-the-art results on two public emotion datasets.•MFA-LR is able to build more effective classifiers on new subjects than existing methods.