Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.

Abstract

Research suggests that dreams play a role in the regulation of emotional processing and memory consolidation; electroencephalography (EEG) is useful for studying them, but manual annotation is time-consuming and prone to bias. This study was aimed at developing an EEG-based machine learning (ML) model to automatically identify dream and dreamless states in sleep. We extracted features from EEG data using common spatial patterns (CSPs) and the discrete wavelet transform (DWT) and used them to classify EEG signals into dream and dreamless states using ML models. To determine the most informative channels for classification, we used the permutation-based channel selection method and the nondominated sorting genetic algorithm II (NSGA-II). We evaluated our proposal using a public dataset that is part of the DREAM project, which was collected from 58 EEG channels during rapid eye movement (REM) and non-REM sleep, while 28 subjects reported dream or dreamless experiences. We achieved accuracies greater than 0.85 to distinguish dream and dreamless states using CSP-based feature extraction combined with k-nearest neighbors (KNN), as well as through multiple combinations of EEG channels identified by channel selection methods. Our findings suggest that as few as 8-10 EEG channels may be sufficient for dream recognition. Excluding one subject at a time during model training revealed challenges in generalizing the models to unseen subjects. Channel selection methods have proven to be effective in selecting relevant subsets of EEG channels to classify dreams and dreamless experiences. Our results demonstrate the feasibility of automatic dream detection and highlight the need to improve ML generalization.