Dynamic Label Smoothing Strategy for Biosignal Classification

Peiji Chen; Dian Li; Yifan Tang; Shunta Togo; Hiroshi Yokoi; Yinlai Jiang

ICASSP 2024accepted0 citations

Dynamic Label Smoothing Strategy for Biosignal Classification

Peiji Chen, Dian Li, Yifan Tang, Shunta Togo, Hiroshi Yokoi, Yinlai Jiang

Abstract

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

BibTeX

@inproceedings{icassp2024_dynamiclabelsmoo,
  title = {Dynamic Label Smoothing Strategy for Biosignal Classification},
  author = {Peiji Chen and Dian Li and Yifan Tang and Shunta Togo and Hiroshi Yokoi and Yinlai Jiang},
  booktitle = {ICASSP 2024},
  year = {2024}
}