Mirror contrastive loss based sliding window transformer for subject-independent motor imagery based EEG signal recognition

While deep learning models have been extensively utilized in motor imagery based EEG signal recognition, they often operate as black boxes. Motivated by neurological findings indicating that the mental imagery of left or right-hand movement induces event-related desynchronization (ERD) in the contralateral sensorimotor area of the brain, we propose a Mirror Contrastive Loss based Sliding Window Transformer (MCL-SWT) to enhance subject-independent motor imagery-based EEG signal recognition. Specifically, our proposed mirror contrastive loss enhances sensitivity to the spatial location of ERD by contrasting the original EEG signals with their mirror counterparts-mirror EEG signals generated by interchanging the channels of the left and right hemispheres of the EEG signals. Moreover, we introduce a temporal sliding window transformer that computes self-attention scores from high temporal resolution features, thereby improving model performance with manageable computational complexity. We evaluate the performance of MCL-SWT on subject-independent motor imagery EEG signal recognition tasks, and our experimental results demonstrate that MCL-SWT achieved accuracies of 66.48% and 75.62%, surpassing the state-of-the-art (SOTA) model by 2.82% and 2.17%, respectively. Furthermore, ablation experiments confirm the effectiveness of the proposed mirror contrastive loss. A code demo of MCL-SWT is available at https://github.com/roniusLuo/MCL_SWT.

AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning

As a self-supervised learning paradigm, contrastive learning has been widely used to pre-train a powerful encoder as an effective feature extractor for various downstream tasks. This process requires numerous unlabeled training data and computational resources, which makes the pre-trained encoder become valuable intellectual property of the owner. However, the lack of a priori knowledge of downstream tasks makes it non-trivial to protect the intellectual property of the pre-trained encoder by applying conventional watermarking methods. To deal with this problem, in this paper, we introduce AWEncoder, an adversarial method for watermarking the pre-trained encoder in contrastive learning. First, as an adversarial perturbation, the watermark is generated by enforcing the training samples to be marked to deviate respective location and surround a randomly selected key image in the embedding space. Then, the watermark is embedded into the pre-trained encoder by further optimizing a joint loss function. As a result, the watermarked encoder not only performs very well for downstream tasks, but also enables us to verify its ownership by analyzing the discrepancy of output provided using the encoder as the backbone under both white-box and black-box conditions. Extensive experiments demonstrate that the proposed work enjoys pretty good effectiveness and robustness on different contrastive learning algorithms and downstream tasks, which has verified the superiority and applicability of the proposed work.