From high- to low-density EEG for automatic classification of dream experiences during stage 2 of NREM
SLEEP Advances January 1, 2025 Luis Alfredo Moctezuma, Marta Molinas, Takashi Abe 1 citation
Machine learning models trained on high-density electroencephalography (EEG) signals can automatically detect whether a person is dreaming during the N2 stage of non-rapid eye movement (NREM) sleep with high accuracy. Using permutation-based channel selection, the models achieved up to 0.94 accuracy, F1 score, precision, and recall, an area under the receiver operating characteristic curve of 0.97, and a kappa of 0.88 on a balanced dataset of dream experience and no experience reports. Performance remained similar when using 30–40 EEG channels, and removing occipital channels slightly improved accuracy by 0.02. On a separate set of dream reports without recall, accuracy was 0.7. Reducing the number of channels makes portable, low-cost devices for real-time dream detection feasible.