EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

Brain computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional Neural Networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible (defined as the number of parameters in the model). In this work we introduce EEGNet, a compact convolutional network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to more efficiently extract relevant features 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, traditional approaches, while simultaneously fitting up to two orders of magnitude fewer parameters. We also demonstrate ways to visualize the contents of a trained EEGNet model to enable interpretation of the learned features.