Combining Euclidean Alignment and Data Augmentation for BCI decoding - A&O (Apprentissage et Optimisation)
Communication Dans Un Congrès Année : 2024

Combining Euclidean Alignment and Data Augmentation for BCI decoding

Résumé

Automated classification of electroencephalogram (EEG) signals is complex due to their high dimensionality, nonstationarity, 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.
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Dates et versions

hal-04743791 , version 1 (18-10-2024)

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Gustavo H Rodrigues, Bruno Aristimunha, Sylvain Chevallier, Raphael Y de Camargo. Combining Euclidean Alignment and Data Augmentation for BCI decoding. EUSIPCO 2024 - 32nd European Signal Processing Conference, European Signal Processing Conference (EUSIPCO), Aug 2024, Lyon, France. ⟨10.48550/arXiv.2405.14994⟩. ⟨hal-04743791⟩
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