MGFKD: A Semi-Supervised Multi-Source Domain Adaptation Algorithm for Cross-Subject EEG Emotion Recognition

Brain Res Bull. 2024 Feb 12:110901. doi: 10.1016/j.brainresbull.2024.110901. Online ahead of print.ABSTRACTCurrently, most models rarely consider the negative transfer problem in the research field of cross-subject EEG emotion recognition. To solve this problem, this paper proposes a semi-supervised domain adaptive algorithm based on few labeled samples of target subject, which called multi-domain geodesic flow kernel dynamic distribution alignment (MGFKD). It consists of three modules: 1) GFK common feature extractor: projects the feature distribution of source and target subjects to the Grassmann manifold space, and obtains the latent common features of the two feature distributions through GFK method. 2) Source domain selector: obtains pseudo-labels of the target subject through weak classifier, finds "golden source subjects" by using few known labels of target subjects. 3) Label corrector: uses a dynamic distribution balance strategy to correct the pseudo-labels of the target subject. We conducted comparison experiments on the SEED and SEED-IV datasets, and the results show that MGFKD outperforms unsupervised and semi-supervised domain adaptation algorithms, achieving an average accuracy of 87.51±7.68% and 68.79±8.25% on the SEED and SEED-IV datasets with only one labeled sample per video for target subject. Especially when the number of source domains is set as 6 and the number of known labels is set as 5, the accuracy increase to 90.20±7.57% and 69.99±7.38%, respect...

Source: Brain Research Bulletin - February 14, 2024 Category: Neurology Authors: Rui Zhang Huifeng Guo Zongxin Xu Yuxia Hu Mingming Chen Lipeng Zhang Source Type: research