SEDA-EEG: A semi-supervised emotion recognition network with domain adaptation for cross-subject EEG analysis - Tan, Weilong; Zhang, Hongyi; Wang, Yingbei; Wen, Weimin; Chen, Liang; Li, Han; Gao, Xingen; Zeng, Nianyin (Neurocomputing (Amsterdam), 2025)
In this paper, a cross-subject emotion recognition network based on semi-supervised and domain adversarial learning with electroencephalogram (EEG) signals (SEDA-EEG) is proposed, which addresses the challenge of high EEG variability from different subjects in brain-computer interface (BCI) research. By employing the differential entropy features of EEG signal processed by the linear dynamic systems as input, influences of noises can be greatly reduced and the essential changes in EEG can be well reflected while capturing the implicit signal features over time. Simultaneously, a deep neural network is designed to match similarity relationships between samples and labels by capturing key patterns in the data, which improves the performance of recognizing EEG signals. Moreover, a domain transfer module based on domain adversarial loss and offline feature decomposition semi-supervised learning is proposed to enhance inter-domain knowledge generalization, which achieves cross-domain feature alignment and enables the model more adapt to the target data. Experimental results show that the proposed SEDA-EEG yields the state-of-the-art performance, which outperforms other advanced models by 12.90% on the SEED-IV dataset with stronger robustness, indicating the potential of applying into the EEG-oriented cross-subject emotion recognition.