 This paper proposes a novel unsupervised domain adaptation, UDA, method for myoelectric pattern recognition. The method uses a self-guided adaptive sampling, SGAS, strategy to improve the feature representation of myoelectric patterns across users. It achieves better alignment of feature representations than existing UDA methods, resulting in higher accuracy in a cross-user classification setting. This article was authored by Xian Zhang, Su Zhang, Lu Wu, and others.