Self-Supervised Learning of Band-Limited Spectral Features from Sleep EEG Using Variational Autoencoders

We use a $\beta$-variational autoencoder regularized by a denoising autoencoder to learn latent representations of sleep electroencephalography (EEG) timefrequency spectrograms in a self-supervised manner. Our approach enables the discovery of sleep EEG spectral features unbiased by extrinsic sleep stage labels. We provide sample results that demonstrate the effectiveness of this approach in identifying EEG features that exhibit a variety of patterns of specialization in time and frequency without supervision, including features resembling sleep spindles and theta bursts that occur in sleep stages N2 and N3.