From high- to low-density EEG for automatic classification of dream experiences during stage 2 of NREM
Luis Alfredo Moctezuma, Marta Molinas, Takashi Abe
SLEEP Advances 2025 DOI: 10.1093/sleepadvances/zpaf066 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Topics | Lucid dreaming |
| Keywords | Electroencephalography Pattern recognition psychology Binary classification Channel broadcasting Selection genetic algorithm Artificial intelligence Sleep stages Speech recognition Brain–computer interface Feature selection Audiology |
| Citations | 1 |
| Key findings | Machine learning models using high-density EEG can classify dream experience versus no experience during N2 sleep with up to 0.94 accuracy, and performance is maintained with a reduced set of 30–40 EEG channels. |
Abstract
Abstract 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. Statement of Significance Dreaming during non-REM sleep is difficult to detect objectively. Here, we show that machine learning models leveraging high-density EEG can accurately classify the presence vs absence of dream experiences during N2 sleep. A key finding is that this high performance is maintained with a substantially reduced channel set, establishing the feasibility of portable, low-density EEG systems for real-time dream detection. Our work provides a critical objective correlate for subjective dreaming and opens new avenues for practical applications in sleep research and clinical diagnostics.
Comparable studies
Other observational and cohort studies on lucid dreaming, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Lucid Dreaming: a State of Consciousness with Features of Both Waking and Non-Lucid Dreaming Student volunteers trained to become lucid and signal lucidity with horizontal eye... | 2009 | Observational study | n = 6 |
| Neural Correlates of Dream Lucidity Obtained from Contrasting Lucid versus Non-Lucid REM Sleep: A Combined EEG/fMRI Case Study Experienced lucid dreamers | 2012 | Observational cohort | n = 4 |
| Real-time dialogue between experimenters and dreamers during REM sleep. Individuals during REM sleep, including frequent lucid dreamers, a novice, and a... | 2021 | Observational study | n = 36 |
| Lucid Dreaming in Narcolepsy Consecutive patients with narcolepsy and healthy controls | 2015 | Case-control study | n = 106 |
| Metacognitive Mechanisms Underlying Lucid Dreaming Human participants split into high- and low-lucidity groups based on reported dream... | 2015 | Observational cohort |
Citations in the library
Cites 21
- Assessing sleep consciousness within subjects using a serial awakening paradigm Frontiers in Psychology 2013
- Dream content analysis: Basic principles heiDOK (Heidelberg University) 2010
- The Dream Catcher experiment: blinded analyses failed to detect markers of dreaming consciousness in EEG spectral power. Neuroscience of Consciousness 2020
- Shared EEG correlates between non-REM parasomnia experiences and dreams. Nature Communications May 9, 2024
- Targeted dream incubation at sleep onset increases post-sleep creative performance Scientific Reports May 15, 2023
and 16 more in the library