EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss

EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss

Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Conventional reconstruction algorith...

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Bibliographic Details
Main Authors: Tian-jian Luo, Yachao Fan, Lifei Chen, Gongde Guo, Changle Zhou
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-04-01
Series:Frontiers in Neuroinformatics
Subjects:
EEG signals reconstruction
generative adversarial network
Wasserstein distance
sampling rate
sensitivity
Online Access:https://www.frontiersin.org/article/10.3389/fninf.2020.00015/full

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