  Recent promises of generative deep learning lately brought interest to its
potential uses in neural engineering. In this paper we firstly review recently
emerging studies on generating artificial electroencephalography (EEG) signals
with deep neural networks. Subsequently, we present our feasibility experiments
on generating condition-specific multichannel EEG signals using conditional
variational autoencoders. By manipulating real resting-state EEG epochs, we
present an approach to synthetically generate time-series multichannel signals
that show spectro-temporal EEG patterns which are expected to be observed
during distinct motor imagery conditions.
