From high- to low-density EEG for automatic classification of dream experiences during stage 2 of NREM

This study proposes a method to automatically identify dream experience (DE) and no experience (NE) during the sleep stage N2 of non-rapid eye movement (NREM). We investigated the use of machine learning (ML) to automatically identify when a subject is having a dream during NREM from electroencephalography (EEG) signals. We use permutation-based channel selection to identify the most informative EEG channels for the classification of DE and NE and to select a set of channels that allow focus on the most important areas of the brain at the scalp level. The results show that when the ML models are trained on a balanced dataset containing both DE and NE reports, along with high-density EEG, they can achieve a classification performance of up to 0.94 in accuracy, F1 score, precision and recall, an Area Under the Receiver Operating Characteristic (AUROC) of 0.97, and a kappa of 0.88. Performance decreases while we reduce the number of channels, but it remains like using up to 30-40 EEG channels. We show that ML models trained on high-density EEG to classify NE and DE can identify whether a subject was dreaming, achieving an accuracy of 0.7 on a separate set of dream reports where subjects reported a dream experience without recall (DEWR), and channel selection methods have shown that performance could increase by 0.02 when EEG channels are removed from the occipital area. Our results show a high classification performance for automatic dream detection and the need to reduce the number of EEG channels needed to create the ML models, thus obtaining low-cost portable devices that can be used in real-life scenarios.