Abstract
Brain computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural 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, 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. In this work we
introduce EEGNet, a compact convolutional network for EEG-based BCIs. We
introduce the use of depthwise and separable convolutions to construct an
EEG-specific model which encapsulates well-known EEG feature extraction
concepts for BCI. We compare EEGNet 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 the reference algorithms when only limited training data is
available. 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, suggesting that the observed
performances were not due to artifact or noise sources in the data.
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