当前位置: X-MOL 学术arXiv.cs.AI › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Boosting Generalization in Bio-Signal Classification by Learning the Phase-Amplitude Coupling
arXiv - CS - Artificial Intelligence Pub Date : 2020-09-16 , DOI: arxiv-2009.07664
Abdelhak Lemkhenter and Paolo Favaro

Various hand-crafted features representations of bio-signals rely primarily on the amplitude or power of the signal in specific frequency bands. The phase component is often discarded as it is more sample specific, and thus more sensitive to noise, than the amplitude. However, in general, the phase component also carries information relevant to the underlying biological processes. In fact, in this paper we show the benefits of learning the coupling of both phase and amplitude components of a bio-signal. We do so by introducing a novel self-supervised learning task, which we call Phase-Swap, that detects if bio-signals have been obtained by merging the amplitude and phase from different sources. We show in our evaluation that neural networks trained on this task generalize better across subjects and recording sessions than their fully supervised counterpart.

中文翻译:

通过学习相位-幅度耦合促进生物信号分类的泛化

生物信号的各种手工特征表示主要依赖于特定频带中信号的幅度或功率。相位分量通常被丢弃,因为它比幅度更具有样本特异性,因此对噪声更敏感。然而,一般而言,相位分量也携带与潜在生物过程相关的信息。事实上,在本文中,我们展示了学习生物信号的相位和幅度分量的耦合的好处。为此,我们引入了一种新的自监督学习任务,我们称之为“相位交换”,该任务通过合并来自不同来源的幅度和相位来检测是否已获得生物信号。
更新日期:2020-10-19
down
wechat
bug