Semi-Supervised Privacy-Preserving EEG-Based Motor Imagery Classification via Self and Adversarial Training

Electroencephalogram (EEG)-based motor imagery (MI) signals are frequently used in brain-computer interfaces (BCIs) due to their wide applications in the rehabilitation field. However, cross-subject variations often result in a model trained on one participant failing when applied to another. Additionally, privacy concerns regarding sensitive health and mental information in EEG-based MI signals further complicate the situation. Source-free domain adaptation aims to address these cross-subject variations by transferring knowledge from a source domain (i.e., a previous participant) to a target domain (i.e., a new participant) without accessing sensitive source data. However, source-free unsupervised domain adaptation models often face issues with incorrect pseudo-labels, which can lead to unstable and ineffective adaptation. To address this, we propose a source-free semi-supervised domain adaptation algorithm for EEG-based MI signal classification. This algorithm tackles noise accumulation caused by incorrect pseudo-labels while effectively handling data distribution variations and privacy concerns, similar to source-free unsupervised domain adaptation models. Specifically, we train the classifier head using only a limited amount of labeled target data to prevent noise accumulation, and generate pseudo-labels for the unlabeled target data. Furthermore, we introduce an independent self-training head that learns better representations using the generated pseudo-labels, mitigating overfitting caused by the limited labeled target data. Additionally, we design an adversarial head that plays a minimax game to extract more discriminative feature representations from the unlabeled target data. Extensive experiments on three benchmark datasets, compared with eighteen state-of-the-art SFDA methods, demonstrate the superiority of our approach.