  Automated classification of electroencephalogram (EEG) signals is complex due
to their high dimensionality, non-stationarity, low signal-to-noise ratio, and
variability between subjects. Deep neural networks (DNNs) have shown promising
results for EEG classification, but the above challenges hinder their
performance. Euclidean Alignment (EA) and Data Augmentation (DA) are two
promising techniques for improving DNN training by permitting the use of data
from multiple subjects, increasing the data, and regularizing the available
data. In this paper, we perform a detailed evaluation of the combined use of EA
and DA with DNNs for EEG decoding. We trained individual models and shared
models with data from multiple subjects and showed that combining EA and DA
generates synergies that improve the accuracy of most models and datasets.
Also, the shared models combined with fine-tuning benefited the most, with an
overall increase of 8.41\% in classification accuracy.
