Deep Source Semi-Supervised Transfer Learning (DS3TL) for Cross-Subject EEG Classification

Xue Jiang, Lubin Meng, Ziwei Wang, Dongrui Wu

Abstract: Objective: An electroencephalogram (EEG) based brain-computer interface (BCI) maps the user's EEG signals into commands for external device control. Usually a large amount of labeled EEG trials are required to train a reliable EEG recognition model. However, acquiring labeled EEG data is time-consuming and user-unfriendly. Semi-supervised learning (SSL) and transfer learning can be used to exploit the unlabeled data and the auxiliary data, respectively, to reduce the amount of labeled data for a new subject. Methods: This paper proposes deep source semi-supervised transfer learning (DS3TL) for EEG-based BCIs, which assumes the source subject has a small number of labeled EEG trials and a large number of unlabeled ones, whereas all EEG trials from the target subject are unlabeled. DS3TL mainly includes a hybrid SSL module, a weakly-supervised contrastive module, and a domain adaptation module. The hybrid SSL module integrates pseudo-labeling and consistency regularization for SSL. The weakly-supervised contrastive module performs contrastive learning by using the true labels of the labeled data and the pseudo-labels of the unlabeled data. The domain adaptation module reduces the individual differences by uncertainty reduction. Results: Experiments on three EEG datasets from different tasks demonstrated that DS3TL outperformed a supervised learning baseline with many more labeled training data, and multiple state-of-the-art SSL approaches with the same number of labeled data. Significance: To our knowledge, this is the first approach in EEG-based BCIs that exploits the unlabeled source data for more accurate target classifier training.