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

Autor: Luo, Jing, Mao, Qi, Shi, Weiwei, Shi, Zhenghao, Wang, Xiaofan, Lu, Xiaofeng, Hei, Xinhong

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 e

Externí odkaz: http://arxiv.org/abs/2409.00130

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AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning

Autor: Zhang, Tianxing, Wu, Hanzhou, Lu, Xiaofeng, Sun, Guangling

Publikováno v: Applied Sciences (2023)

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

Externí odkaz: http://arxiv.org/abs/2208.03948

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