  Electroencephalography (EEG) signals are frequently used for various
Brain-Computer Interface (BCI) tasks. While Deep Learning (DL) techniques have
shown promising results, they are hindered by the substantial data
requirements. By leveraging data from multiple subjects, transfer learning
enables more effective training of DL models. A technique that is gaining
popularity is Euclidean Alignment (EA) due to its ease of use, low
computational complexity, and compatibility with Deep Learning models. However,
few studies evaluate its impact on the training performance of shared and
individual DL models. In this work, we systematically evaluate the effect of EA
combined with DL for decoding BCI signals. We used EA to train shared models
with data from multiple subjects and evaluated its transferability to new
subjects. Our experimental results show that it improves decoding in the target
subject by 4.33% and decreases convergence time by more than 70%. We also
trained individual models for each subject to use as a majority-voting ensemble
classifier. In this scenario, using EA improved the 3-model ensemble accuracy
by 3.7%. However, when compared to the shared model with EA, the ensemble
accuracy was 3.62% lower.
