Deep Invertible Networks for EEG-based brain-signal decoding

2019/07/17 by Robin Tibor Schirrmeister, Tonio Ball, Schirrmeister, Robin Tibor +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Neuroscience · #Blind Source Separation Techniques #EEG and Brain-Computer Interfaces #Fractal and DNA sequence analysis #cs.LG #cs.NE #eess.SP #stat.ML

paper · pdf · doi:10.48550/arxiv.1907.07746

arxiv created 2019/07/17 · arxiv updated 2019/07/19

Abstract

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.

Citations

• Deep learning with convolutional neural networks for EEG decoding and visualization

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