Deep Invertible Networks for EEG-based brain-signal decoding

In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals above chance. Further ideas for their regularization towards better decoding accuracies are discussed.

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Bibliographic Details
Main Authors Schirrmeister, Robin Tibor, Ball, Tonio
Format Journal Article
LanguageEnglish
Published 17.07.2019
Subjects
Computer Science - Learning
Computer Science - Neural and Evolutionary Computing
Statistics - Machine Learning
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Summary:In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals above chance. Further ideas for their regularization towards better decoding accuracies are discussed.
DOI:10.48550/arxiv.1907.07746