Deep Invertible Networks for EEG-based brain-signal decoding

2019/07/17 by Robin Tibor Schirrmeister, Tonio Ball, Schirrmeister, Robin Tibor +1
Neuroscience · Biochemistry, Genetics and Molecular Biology · Computer Science · #EEG and Brain-Computer Interfaces #Fractal and DNA sequence analysis #Blind Source Separation Techniques

paper · pdf · doi:10.48550/arxiv.1907.07746

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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