  Over the past decade, convolutional neural networks (CNNs) have become the
driving force of an ever-increasing set of applications, achieving
state-of-the-art performance. Most of the modern CNN architectures are composed
of many convolutional and fully connected layers and typically require
thousands or millions of parameters to learn. CNNs have also been effective in
the detection of Event-Related Potentials from electroencephalogram (EEG)
signals, notably the P300 component which is frequently employed in
Brain-Computer Interfaces (BCIs). However, for this task, the increase in
detection rates compared to approaches based on human-engineered features has
not been as impressive as in other areas and might not justify such a large
number of parameters. In this paper, we study the performances of existing CNN
architectures with diverse complexities for single-trial within-subject and
cross-subject P300 detection on four different datasets. We also proposed
SepConv1D, a very simple CNN architecture consisting of a single depthwise
separable 1D convolutional layer followed by a fully connected Sigmoid
classification neuron. We found that with as few as four filters in its
convolutional layer and a small overall number of parameters, SepConv1D
obtained competitive performances in the four datasets. We believe this may
represent an important step towards building simpler, cheaper, faster, and more
portable BCIs.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning