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This paper proposes adaptive schemes for myoelectric based human-machine interface (HMI) applied to a video game. Adaptive schemes modify the classification criteria to keep a stable performance in long-term operations. Online support vector machine (SVM) is used as the core of classification to facilitate incremental training during run-time. Supervised and unsupervised methods are individually employed to update online training data set. The experimental results show that the proposed adaptive schemes increase the achieved scores and make a stable performance for myoelectric HMI.