eegFloss: A Python package for refining sleep EEG recordings using machine learning models

• URL: http://arxiv.org/abs/2507.06433v1
• Date: Tue, 08 Jul 2025 22:27:43 GMT
• Title: eegFloss: A Python package for refining sleep EEG recordings using machine learning models
• Authors: Niloy Sikder, Paul Zerr, Mahdad Jafarzadeh Esfahani, Martin Dresler, Matthias Krauledat,
• Abstract summary: This paper introduces eegFloss, an open-source Python package to utilize eegUsability, a novel machine learning (ML) model to detect artifacts in sleep EEG recordings.<n>eegUsability has been trained and evaluated on manually artifact-labeled EEG data collected from 15 participants over 127 nights using the Zmax headband.<n>eegFloss can enhance the precision and rigor of their analysis as well as the accuracy and reliability of their outcomes.
• Score: 0.559239450391449
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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