ArticleBioMed research international2025
Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.
Article in BioMed research international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- DMPat-based SOXFE: investigations of the violence detection using EEG signals.Cognitive neurodynamics · 2025Article
- From high- to low-density EEG for automatic classification of dream experiences during stage 2 of NREM.Sleep advances : a journal of the Sleep Research Society · 2025Article
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Authors and funding
3 authors.
Funding
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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
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