  We propose EEG-SimpleConv, a straightforward 1D convolutional neural network
for Motor Imagery decoding in BCI. Our main motivation is to propose a simple
and performing baseline to compare to, using only very standard ingredients
from the literature. We evaluate its performance on four EEG Motor Imagery
datasets, including simulated online setups, and compare it to recent Deep
Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or
far more efficient than other approaches, showing strong knowledge-transfer
capabilities across subjects, at the cost of a low inference time. We advocate
that using off-the-shelf ingredients rather than coming with ad-hoc solutions
can significantly help the adoption of Deep Learning approaches for BCI. We
make the code of the models and the experiments accessible.
