EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces.OBJECTIVE
 In this work we introduce EEGNet, a compact convolutional neural network for EEG-based BCIs.  We compare EEGNet, both for within-subject and cross-subject classification, to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR).


 We show that EEGNet generalizes across paradigms better than, and achieves comparably high performance to, the reference algorithms when only limited training data is available across all tested paradigms.  In addition, we demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features.


 Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. 
EEGNet