A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding. (arXiv:2401.10746v2 [eess.SP] UPDATED)

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 …

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