An open-source Python toolbox for real-time EEG monitoring, analysis, and sensory stimulation during sleep for dream engineering research.
Dreamento is an open-source Python toolbox for dream engineering that enables real-time monitoring, analysis, and sensory stimulation of sleep EEG data. It provides a graphical interface for recording and modulating sleep, along with offline tools for detailed post-processing, including automatic sleep scoring and event detection. The software is designed to work with wearable EEG devices like the ZMax headband.
Sleep researchers, neuroscientists, and dream engineering practitioners who need to conduct real-time EEG experiments or analyze sleep data with a focus on sensory stimulation and event detection.
Dreamento offers a unique, integrated open-source platform for both real-time dream engineering and offline sleep analysis, featuring validated algorithms like YASA for event detection and a modular design that encourages community extensions.
Dreamento (DReam ENgenieering TOolbox): a Python-based software for dream engineering while monitoring/analyzing real-time EEG data.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Provides live EEG visualization with adjustable time and amplitude scales, plus real-time spectrogram analysis, enabling immediate feedback during sleep experiments as highlighted in the real-time features section.
Uses YASA algorithms for automatic detection of sleep microstructures like spindles, slow oscillations, and REM eye movements, offering reliable offline analysis with ERP representations shown in the README screenshots.
Built as a modular platform that encourages researchers to extend features, promoting transparency and collaboration, as stated in the philosophy and overview sections.
Supports delivery of visual, auditory, and tactile stimuli during sleep, making it unique for dream engineering research, as detailed in the real-time features list.
Enables batch conversion, scoring, and analysis of multiple recordings through tools like DreamentoConverter and bulk autoscoring, saving time for large datasets as described in the post-processing features.
Requires separate virtual environments for real-time and offline use with specific conda or pip commands, and Windows is highly recommended, making setup cumbersome for non-experts or cross-platform users.
Admits that real-time sleep staging is 'not ideal yet, still under development,' which may affect reliability for immediate feedback applications, as noted in the autoscoring section.
Primarily designed for Hypnodyne ZMax headband and requires Hypnodyne software like HDServer and HDRecorder, limiting flexibility for other EEG devices or workflows.
Functionality may differ on Linux-based systems with minor dependency issues, and real-time use is best on Windows, as warned in the installation notes, reducing portability.