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EEG Mu Rhythm in Typical and Atypical Development

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"Counting sheep PSG": EEGLAB-compatible open-source matlab software for signal processing, visualization, event

L B Ray¹, D Baena², S M Fogel³

¹School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada.

Journal of Neuroscience Methods

|May 13, 2024

Summary

Counting Sheep PSG is a new, open-source MATLAB software that enhances sleep research by providing advanced polysomnography (PSG) analysis tools. This EEGLAB-compatible software facilitates signal processing, event detection, and sleep stage scoring, promoting data sharing and scientific advancement.

Keywords:

Automatic detection Electroencephalography Event marking Graphical user interface Hypnogram Open-source Polysomnography Scoring Signal processing Sleep architecture

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Area of Science:

Neuroscience
Sleep Medicine
Computational Biology

Background:

Limited availability of open-source polysomnography (PSG) analysis tools hinders sleep research progress.
Existing commercial software often involves high costs and proprietary formats, restricting data sharing and reproducibility.

Purpose of the Study:

Introduce Counting Sheep PSG, an EEGLAB-compatible, open-source software for MATLAB.
To provide researchers with advanced tools for polysomnography data analysis, visualization, and scoring.

Main Methods:

Developed as an EEGLAB-compatible MATLAB software.
Incorporates comprehensive signal processing, including artifact correction and power spectral analysis.
Features customizable data visualization, event marking, manual sleep stage scoring, and automatic event detection.

Main Results:

Offers advanced signal processing, including bad channel interpolation, ICA, and artifact subspace reconstruction.
Provides customizable display of polysomnographic data and hypnograms.
Includes automatic detection of movement artifacts, sleep spindles, slow waves, and eye movements, alongside exportable sleep architecture statistics.

Conclusions:

Counting Sheep PSG significantly advances EEGLAB integration, preprocessing, artifact correction, and ease of use compared to previous tools.
Addresses the need for free, open-source software in sleep research to overcome limitations of proprietary systems.
Facilitates scientific advancement by enabling standardization and interoperability in sleep data analysis.