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

Esfahani, Mahdad Jafarzadeh; Daraie, Amir Hossein; Weber, Frederik D.; Dresler, Martin

doi:10.48550/arxiv.2207.03977

Cited by 3 publications

(5 citation statements)

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“…Sleep modulation is typically subdivided into (1) non-REM sleep modulation, e.g., to enhance deep sleep and memory consolidation using CLAS and TMR techniques, or (2) REM sleep modulation for studying (lucid) dreams. In our previous work, we have introduced Dreamento as an opensource tool that allows conducting such studies with wearables (Esfahani et al, 2022a). In this work, we have deemed to address the similarities and differences between the characteristics of the oscillations such as SO, spindles, and REM events in ZMax with respect to PSG signal which have to be considered either for sleep modulation studies or to conduct post-processing of the microstructural features of sleep.…”

Section: Discussionmentioning

confidence: 99%

“…We compared the performance of two autoscoring approaches with the human scoring as the ground truth. The autoscoring algorithms encompass: (1) ZLab, which is the autoscoring service provided by ZMax Hypnodyne, and (2) the DreamentoScorer (Figure 3) from our open-source dream engineering toolbox (Esfahani et al, 2022a; https://github.com/dreamento/dreamento). To receive the results from ZLab, all the available ZMax data from datasets 1-4 were pseudonymized and then shared with Hypnodyne for autoscoring.…”

Section: Autoscoringmentioning

confidence: 99%

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Validation of the sleep EEG headband ZMax

Esfahani,

Weber,

Boon

et al. 2023

Preprint

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Polysomnography (PSG) is the gold standard for recording sleep. However, the standard PSG systems are bulky, expensive, and often confined to lab environments. These systems are also time-consuming in electrode placement and sleep scoring. Such limitations render standard PSG systems less suitable for large-scale or longitudinal studies of sleep. Recent advances in electronics and artificial intelligence enabled "wearable" PSG systems. Here, we present a study aimed at validating the performance of ZMax, a widely-used wearable PSG that includes frontal electroencephalography (EEG) and actigraphy but no submental electromyography (EMG). We analyzed 135 nights with simultaneous ZMax and standard PSG recordings amounting to over 900 hours from four different datasets, and evaluated the performance of the headband's proprietary automatic sleep scoring (ZLab) alongside our open-source algorithm (DreamentoScorer) in comparison with human sleep scoring. ZLab and DreamentoScorer compared to human scorers with moderate and substantial agreement and Cohen's kappa scores of 59.61% and 72.18%, respectively. We further analyzed the competence of these algorithms in determining sleep assessment metrics, as well as shedding more lights on the bandpower computation, and morphological analysis of sleep microstructural features between ZMax and standard PSG. Relative bandpower computed by ZMax implied an error of 5.5% (delta), 4.5% (theta), 1.6% (alpha), 0.5% (sigma), 0.8% (beta), and 0.2% (gamma), compared to standard PSG. In addition, the microstructural features detected in ZMax did not represent exactly the same characteristics as in standard PSG. Besides similarities and discrepancies between ZMax and standard PSG, we measured and discussed the technology acceptance rate, feasibility of data collection with ZMax, and highlighted essential factors for utilizing ZMax as a reliable tool for both monitoring and modulating sleep.

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Section: Discussionmentioning

confidence: 99%

Section: Autoscoringmentioning

confidence: 99%

Validation of the sleep EEG headband ZMax

Esfahani,

Weber,

Boon

et al. 2023

Preprint

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“…It has a proper signal correlation to the PSG, an autoscoring algorithm, and can detect microstructural sleep features, e.g., K-complexes, spindles, and rapid eye movements reliably (validation study in preparation). This headband is open-source and therefore any developer can build upon the existing features of the technology (e.g., Esfahani et al, 2022a). Another headband commonly used for research purposes is the Dreem headband, which has five dry EEG electrodes (F7, FpZ, F8, O1, O2) in addition to supplementary sensors such as an accelerometer sensor, a pulse oximeter.…”

Section: Hardware For the Citizen Neuroscience Of Sleepmentioning

confidence: 99%

“…Reactivation (TMR, Rudoy et al, 2009;Hu et al, 2020), or slow wave/sleep spindle triggered stimulation (e.g. Ngo et al, 2013;Wang et al 2022) (Esfahani et al, 2022a). Furthermore, a comprehensive documentation page together with several video tutorials from how to record and stimulate sleep to how to analyze the outputs can be found on the GitHub page of Dreamento.…”

Section: Hardware For the Citizen Neuroscience Of Sleepmentioning

confidence: 99%

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

Esfahani¹,

Sikder²,

Horst³

et al. 2023

Preprint

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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.

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“…B. unwillkürliche Muskelartefakte aufgrund von Erregungszuständen, sakkadische Augenbewegungen, Signaldrifts aufgrund langer Messungen, eine höhere Wahrscheinlichkeit von Schwitzen und wechselndem Druck auf die Elektroden. Um Interpretationsunterschiede zu vermeiden, die auf unterschiedliche Methoden zur Bereinigung von EEG-Daten zurückzuführen sind, sollten künftige Studien automatische Methoden verwenden, die sich bereits etabliert haben oder sich derzeit in Entwicklung befinden (wie zum Beispiel Dreamento [7] oder SleepTrip [18]). Diese Vorverarbeitungsalgorithmen sollten unter Verwendung nichtproprietärer Software als Open Source zur Verfügung gestellt werden, um ein Höchstmaß an Dokumentation zu gewährleisten.…”

Section: Standardisierung Der Eeg-vorverarbeitungunclassified

Methodological recommendations for the neuroscience of lucid dreaming

Adelhöfer

2023

Somnologie

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ZusammenfassungDas luzide Träumen ist ein einzigartiges Phänomen, bei dem sich eine Person während des Schlafs bewusst ist, dass sie träumt, und dadurch häufig ihre Handlungen während des Traums bewusst steuern kann. Dieser Artikel beleuchtet kritisch die methodischen Herausforderungen bei der Erforschung des luziden Träumens und bietet Strategien zu deren Bewältigung unter Berücksichtigung neuer theoretischer und empirischer Erkenntnisse. Methodische Strenge ist für den Fortschritt auf diesem Gebiet unerlässlich. Zu den Schlüsselbereichen, auf die man sich konzentrieren sollte, gehören standardisierte EEG-Vorverarbeitungen, die Berücksichtigung von nächtlichen Störfaktoren und die Untersuchung von nichtoszillatorischen Signalaspekten wie neuronalem Rauschen. Die Verwendung von Datenanalysen auf Quellenebene könnte neue neurophysiologische Marker für luzides Träumen aufdecken und so zu einem umfassenderen Verständnis des Phänomens führen. Der Artikel enthält auch Hinweise, wie sichergestellt werden kann, dass neu erhobene Daten über luzides Träumen für die vorgeschlagenen methodischen Verbesserungen geeignet sind. Obwohl der Ton dieses Artikels kritisch ist, hebt er das Potenzial hervor, das die Forschung zum luziden Träumen hat, wenn diese Herausforderungen richtig angegangen werden. Die Überwindung der hier beschriebenen Einschränkungen wird es den Forschenden ermöglichen, das breite Spektrum der Möglichkeiten, die das luzide Träumen bietet, voll auszuschöpfen – von der Erweiterung unseres Wissens über das Bewusstsein bis hin zu neuen therapeutischen Anwendungen. Indem wir unsere Methoden verfeinern und diese Probleme angehen, kann das Forschungsfeld des luziden Träumens weiter wachsen und sich entwickeln und schließlich sein volles Potenzial entfalten.

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

Cited by 3 publications

References 0 publications

Validation of the sleep EEG headband ZMax

Validation of the sleep EEG headband ZMax

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

Methodological recommendations for the neuroscience of lucid dreaming

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