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 nonrapid 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 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, 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.