Paper-Conference

Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models

CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion

Deep Transfer Learning for Improving Single-EEG Arousal Detection

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

Deep Residual Networks for Automatic Sleep Stage Classification of Raw Polysomnographic Waveforms

A New Fully Automated Random-Forest Algorithm for Sleep Staging

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning