A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding
This work addresses data efficiency in EEG decoding for BCI applications, but it is incremental as it builds on existing EA and transfer learning methods.
The paper tackled the problem of high data requirements for deep learning in EEG-based brain-computer interfaces by systematically evaluating Euclidean Alignment (EA) with deep learning, finding that EA improved decoding accuracy by 4.33% and reduced convergence time by over 70% for shared models, and enhanced ensemble accuracy by 3.7% for individual models.
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.