Deep Invertible Networks for EEG-based brain-signal decoding

Deep-learning-based brain-signal decoding has recently achieved competitive accuracies compared with traditional feature-based decoding approaches. For example, they were used to decode movement-related EEG signals with accuracies at least as good as well-established movement-decoding approaches (Schirrmeister et al., 2017a) and been applied to error or event-related-based decoding (Lawhern et al., 2018, Völker et al., 2018) as well as automatic diagnosis of pathologies (Schirrmeister et.