eegFloss: A Python package for refining sleep EEG recordings using machine learning models
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
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.
