  The early detection of drowsiness has become vital to ensure the correct and
safe development of several industries' tasks. Due to the transient mental
state of a human subject between alertness and drowsiness, automated drowsiness
detection is a complex problem to tackle. The electroencephalography signals
allow us to record variations in an individual's brain's electrical potential,
where each of them gives specific information about a subject's mental state.
However, due to this type of signal's nature, its acquisition, in general, is
complex, so it is hard to have a large volume of data to apply techniques of
Deep Learning for processing and classification optimally. Nevertheless,
Capsule Neural Networks are a brand-new Deep Learning algorithm proposed for
work with reduced amounts of data. It is a robust algorithm to handle the
data's hierarchical relationships, which is an essential characteristic for
work with biomedical signals. Therefore, this paper presents a Deep
Learning-based method for drowsiness detection with CapsNet by using a
concatenation of spectrogram images of the electroencephalography signals
channels. The proposed CapsNet model is compared with a Convolutional Neural
Network, which is outperformed by the proposed model, which obtains an average
accuracy of 86,44% and 87,57% of sensitivity against an average accuracy of
75,86% and 79,47% sensitivity for the CNN, showing that CapsNet is more
suitable for this kind of datasets and tasks.
