EEG2Rep is a self-supervised approach that tackles challenges in EEG data representation learning. By predicting masked inputs in latent space and preserving semantic subsequences, it improves accuracy across diverse tasks and demonstrates robustness to noise.
The study highlights the importance of self-supervised learning in extracting valuable information from EEG data. It compares various masking strategies, explores the impact of loss regularization, and evaluates model robustness to noise. Overall, EEG2Rep shows promising results in enhancing EEG representation learning.
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by Navid Mohamm... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.17772.pdfDeeper Inquiries