Martin Dresler

Mahdad Jafarzadeh Esfahani, Amir Hossein Daraie, Paul Zerr et al.

We introduce Dreamento (Dream engineering toolbox), an open-source Python package for dream engineering using sleep electroencephalography (EEG) wearables. Dreamento main functions are (1) real-time recording, monitoring, analysis, and sensory stimulation, and (2) offline post-processing of the resulting data, both in a graphical user interface (GUI). In real-time, Dreamento is capable of (1) data recording, visualization, and navigation, (2) power-spectrum analysis, (3) automatic sleep scoring, (4) sensory stimulation (visual, auditory, tactile), (5) establishing text-to-speech communication, and (6) managing annotations of automatic and manual events. The offline functions aid in post-processing the acquired data with features to reformat the wearable data and integrate it with non-wearable recorded modalities such as electromyography (EMG). While Dreamento was primarily developed for (lucid) dreaming studies, its applications can be extended to other areas of sleep research such as closed-loop auditory stimulation and targeted memory reactivation.

Guisong Liu, Xin Gao, Martin Dresler et al.

Automatic sleep staging commonly adopts Transformers under the assumption that they learn complex long-range dependencies. We challenge this view by revealing a neglected property of sleep sequences: strong local temporal continuity. We show that a randomly initialized Transformer, without any training, substantially improves sleep staging performance and consistently outperforms heuristic smoothing. We formalize this effect via a Random Attention Prior Kernel (RAPK), showing that random self-attention acts as an adaptive smoother by balancing global averaging and content-based similarity while preserving stage transitions. Using two metrics, the Local Smoothness Influence Index (LSII) and the Weighted Transition Entropy (WTE), we provide evidence that most performance gains in Transformer-based sleep staging arise from architectural inductive bias rather than parameter learning. Our results suggest that sleep staging can be effectively addressed with structure-driven smoothing mechanisms rather than complex dependency modeling, enabling more efficient and edge-deployable healthcare systems for large-scale physiological monitoring.

Niloy Sikder, Paul Zerr, Mahdad Jafarzadeh Esfahani et al.

Electroencephalography (EEG) allows monitoring of brain activity, providing insights into the functional dynamics of various brain regions and their roles in cognitive processes. EEG is a cornerstone in sleep research, serving as the primary modality of polysomnography, the gold standard in the field. However, EEG signals are prone to artifacts caused by both internal (device-specific) factors and external (environmental) interferences. As sleep studies are becoming larger, most rely on automatic sleep staging, a process highly susceptible to artifacts, leading to erroneous sleep scores. This paper addresses this challenge by introducing eegFloss, an open-source Python package to utilize eegUsability, a novel machine learning (ML) model designed to detect segments with artifacts in sleep EEG recordings. eegUsability has been trained and evaluated on manually artifact-labeled EEG data collected from 15 participants over 127 nights using the Zmax headband. It demonstrates solid overall classification performance (F1-score is approximately 0.85, Cohens kappa is 0.78), achieving a high recall rate of approximately 94% in identifying channel-wise usable EEG data, and extends beyond Zmax. Additionally, eegFloss offers features such as automatic time-in-bed detection using another ML model named eegMobility, filtering out certain artifacts, and generating hypnograms and sleep statistics. By addressing a fundamental challenge faced by most sleep studies, eegFloss can enhance the precision and rigor of their analysis as well as the accuracy and reliability of their outcomes.