Enhancing Self-supervised EEG Representation with EEG2Rep

EEG2Rep introduces a novel approach to self-supervised EEG representation learning by predicting masked inputs in latent space and utilizing semantic subsequence preservation. This method enhances the quality of representations and addresses challenges inherent in EEG data.