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Electroencephalography (EEG) signals are frequently used for variousBrain-Computer Interface (BCI) tasks. While Deep Learning (DL) techniques haveshown promising results, they are hindered by the substantial datarequirements. By leveraging data from multiple subjects, transfer learningenables more effective training of DL models. A technique that is gainingpopularity is Euclidean Alignment (EA) due to its ease of use, lowcomputational complexity, and compatibility with Deep Learning models. However,few studies evaluate its impact on the training performance of shared andindividual DL models. In this work, we systematically evaluate the effect of EAcombined with DL for decoding BCI signals. We used EA to train shared modelswith data from multiple subjects and evaluated its transferability to newsubjects. Our experimental results show that it improves decoding in the targetsubject by 4.33% and decreases convergence time by more than 70%. We alsotrained individual models for each subject to use as a majority-voting ensembleclassifier. In this scenario, using EA improved the 3-model ensemble accuracyby 3.7%. However, when compared to the shared model with EA, the ensembleaccuracy was 3.62% lower.

A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding