Dreamento: an open-source dream engineering toolbox for sleep EEG wearables

We introduce Dreamento (Dream engineering toolbox), an open-source Python package for dream engineering utilizing the ZMax (Hypnodyne Corp., Sofia, Bulgaria) headband sleep wearable. Dreamento main functions are (1) real-time recording, monitoring, analysis, and stimulation in a graphical user interface (GUI) (2) and offline post-processing of the resulting data. In real-time, Dreamento is capable of (1) recording data, (2) visualizing data, including power-spectrum analysis and navigation, (3) automatic sleep-scoring, (4) sensory stimulation (visual, auditory, tactile), (5) establishing text-to-speech communication, and (6) managing the annotations of automatic and manual events. The offline functionality aids in post-processing the acquired data with features to reformat the wearable data and integrate it with non-wearable recorded modalities such as electromyography. While the primary application of Dreamento was developed for (lucid) dreaming studies, it is open to being adapted for other purposes and measurement modalities

Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices.

Sleep is essential for our physical and mental well-being. During sleep, despite the paucity of overt behavior, our brain remains active and exhibits a wide range of coupled brain oscillations. In particular slow oscillations are characteristic for sleep, however whether they are directly involved in the functions of sleep, or are mere epiphenomena, is not yet fully understood. To disentangle the causality of these relationships, experiments utilizing techniques to detect and manipulate sleep oscillations in real-time are essential. In this review, we first overview the theoretical principles of closed-loop auditory stimulation (CLAS) as a method to study the role of slow oscillations in the functions of sleep. We then describe technical guidelines and best practices to perform CLAS and analyze results from such experiments. We further provide an overview of how CLAS has been used to investigate the causal role of slow oscillations in various sleep functions. We close by discussing important caveats, open questions, and potential topics for future research

Citizen neuroscience: wearable technology and open software to study the human brain in its natural habitat

Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and analysis of the resulting data. Citizen science has a long history in several fields of the natural sciences, and with recent developments in technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the development of minimal sensing systems for the consumer market, allowing for do-it-yourself (DIY) or quantified-self (QS) investigations of an individual's brain. While most subfields of neuroscience require sophisticated monitoring devices in the laboratory, the study of sleep characteristics has been widely embraced by citizen neuroscientists, likely due to the strong influence of sleep quality on waking life and an increasingly broad accessibility of relevant non-invasive consumer devices. Here, we introduce into the emerging field of citizen neuroscience, illustrating examples of citizen neuroscience projects in the field of sleep research. We then give an overview on wearable technologies for tracking human neurophysiology, and on open software to run them, each with unique capabilities and intended purposes. Finally, we discuss chances and challenges in citizen neuroscience research, and suggest how to improve studying the human brain outside the laboratory

Virtual reality training of lucid dreaming

Contains fulltext : 228582.pdf (publisher's version ) (Open Access)14 december 202

Lucid dream induction with sleep EEG wearables

Lucid dreaming (LD) is defined as a state of awareness of the ongoing dream state while sleeping. Lucid dreaming is a rather rare phenomenon; however, it can be learned and trained, and various studies have proposed different techniques to ‘induce’ lucid dreams. Nonetheless, these studies either lacked physiological measurements and were therefore merely limited to self-reported questionnaires, or in the case of including physiological measurements, their generalizability was restricted mainly due to the exclusive recruitment of ‘experienced’ lucid dreamers. Only a few studies attempted to reliably induce lucid dreams in ‘naive’ participants, but they involved small sample sizes and have not yet been replicated. To overcome these limitations, we designed a multi-center study including three laboratories, in the Netherlands, Canada, and Italy respectively, with the aim of recruiting 60 participants overall (i.e. 20 participants per laboratory). This is the largest sample size for a lucid dreaming induction study with physiological measurements to date. We will test the applicability of a combination of two lucid dreaming induction techniques: targeted lucidity reactivation (TLR) and sense-initiated lucid dream (SSILD), which will be implemented by presenting perceptual cues (visual, auditory, and tactile) before and during REM sleep. To do so, we will employ minimal measurement modalities, i.e., an EEG headband and three additional chin EMG electrodes. We will also use this dataset to develop and validate the first open-source dream engineering toolbox, Dreamento (DREAM ENgineering TOolbox, Esfahani et al., 2022). Participants will visit the laboratory three times throughout an approximately two week period, including an intake session and two morning naps (stimulation and control, in counterbalanced order across subjects). During the intake session, participants will receive information about the study and complete preliminary screening questionnaires. Then, participants will complete daily dream diaries for the following two weeks. The morning nap sessions will be held at least one and two weeks after the intake session, respectively. Both nap sessions consist of the same cognitive training procedure during wakefulness, but differ in terms of the sensory stimulation procedure during sleep. Participants will receive sensory cues upon detection of REM sleep during the stimulation session, but not during the control session. They will be instructed to signal their lucidity using a predefined intentional eye movement pattern (left-right-left-right, LRLR) and will be awakened once the REM period ends to report any subjective experience and complete a lucidity questionnaire

Virtual reality training of lucid dreaming

Metacognitive reflections on one's current state of mind are largely absent during dreaming. Lucid dreaming as the exception to this rule is a rare phenomenon; however, its occurrence can be facilitated through cognitive training. A central idea of respective training strategies is to regularly question one's phenomenal experience: is the currently experienced world real, or just a dream? Here, we tested if such lucid dreaming training can be enhanced with dream-like virtual reality (VR): over the course of four weeks, volunteers underwent lucid dreaming training in VR scenarios comprising dream-like elements, classical lucid dreaming training or no training. We found that VR-assisted training led to significantly stronger increases in lucid dreaming compared to the no-training condition. Eye signal-verified lucid dreams during polysomnography supported behavioural results. We discuss the potential mechanisms underlying these findings, in particular the role of synthetic dream-like experiences, incorporation of VR content in dream imagery serving as memory cues, and extended dissociative effects of VR session on subsequent experiences that might amplify lucid dreaming training during wakefulness