Dynamic Label Smoothing Strategy for Biosignal Classification

Biological signals classification is essential for human machine interaction. Although previous research has achieved high classification performance, compensating for domain shift due to the intra and inter individual variations remains a challenge. In this paper, we propose a novel dynamic label smoothing strategy, named DLS, to address this issue. The proposed DLS constructs an auxiliary neural network to adjust the true label and to supervise the primary neural network. Experiments on the NinaPro DB1 dataset demonstrate that the proposed DLS outperforms current state-of-the-art methods. Furthermore, the proposed DLS has significant potential for practical applications as it can maintain or even improve the performance of the primary neural network on noisy data. The source code is publicly available at: https://github.com/peijii/DLS