  Data augmentation is a common practice to help generalization in the
procedure of deep model training. In the context of physiological time series
classification, previous research has primarily focused on label-invariant data
augmentation methods. However, another class of augmentation techniques
(\textit{i.e., Mixup}) that emerged in the computer vision field has yet to be
fully explored in the time series domain. In this study, we systematically
review the mix-based augmentations, including mixup, cutmix, and manifold
mixup, on six physiological datasets, evaluating their performance across
different sensory data and classification tasks. Our results demonstrate that
the three mix-based augmentations can consistently improve the performance on
the six datasets. More importantly, the improvement does not rely on expert
knowledge or extensive parameter tuning. Lastly, we provide an overview of the
unique properties of the mix-based augmentation methods and highlight the
potential benefits of using the mix-based augmentation in physiological time
series data.
