# Assessing the performance of a portable electroencephalographic sleep monitor against level 1 polysomnography

**Authors:** Malika Lanthier, Michael-Christopher Foti, Karina Fonseca, Caitlin Higginson, Defne Oksit, David Smith, Jean-Marc Lina, Paniz Tavakoli, Stuart Fogel, Laura Ray, Rébecca Robillard, Malika Lanthier, Malika Lanthier, Micheal-Christopher Foti, Karina Fonseca, Caitlin Higginson, Defne Oksit, David Smith, Jean-Marc Lina, Paniz Tavakoli, Stuart Fogel, Laura Ray, Rébecca Robillard

PMC · DOI: 10.1093/sleepadvances/zpaf089 · Sleep Advances: A Journal of the Sleep Research Society · 2025-12-12

## TL;DR

A portable EEG device called Muse-S was tested against standard sleep monitoring and showed good agreement, suggesting it could be a useful tool for accessible sleep studies.

## Contribution

The study provides an independent validation of a portable EEG device for sleep monitoring against gold-standard polysomnography.

## Key findings

- The Muse-S device showed substantial agreement with polysomnography (Cohen’s Kappa = 0.76) for sleep staging.
- Accuracy across sleep stages ranged from 88% to 96%, with near-perfect agreement for REM sleep.
- The device performed well even in participants with sleep-related breathing disorders.

## Abstract

To assess the performance of a portable electroencephalography device for sleep monitoring against polysomnography.

Fifty-six adults underwent one night of in-laboratory sleep recording with the Muse-S headband and simultaneous level 1 polysomnography. Muse-S data were scored by an automated sleep staging algorithm. A registered technologist, blind to the Muse-S automated sleep scoring, scored the polysomnography data.

Good quality data were available for 47 (84 per cent) participants (53 per cent females; 20–71 years old; 17 per cent with sleep-related breathing disorder). Epoch-by-epoch analyses showed substantial agreement between the Muse-S and polysomnography (full night Cohen’s Kappa = 0.76). Cohen’s Kappa were in the fair agreement range for non-rapid eye movement (NREM) 1, substantial agreement range for NREM2 and NREM3, and near-perfect agreement range for rapid eye movement sleep and wake. Accuracy ranged from 88 per cent to 96 per cent across all sleep stages, with a sensitivity of 79–92 per cent and a specificity of 90–99 per cent. Similar results were observed in the subgroup with sleep-related breathing disorder. On average, the Muse-S had higher mean values than polysomnography for total sleep time (+6 min), NREM3 (+15 min), rapid eye movement sleep (+6 min), and sleep efficiency (+1.5 per cent), and lower mean values for sleep latency (−3 min), wake after sleep onset (−3 min), and light sleep (−14 min).

When compared to standard polysomnography, the Muse-S performed well to measure sleep macroarchitecture. This portable device shows great potential as an accessible tool for sleep electroencephalography monitoring. More work is required to validate this tool in more diverse populations to ensure robustness across age, sex, neurological conditions, and sleep profiles.

This article is part of the Consumer Sleep Technology Special Collection

Statement of SignificanceMost portable monitors are restricted to indirect measures to estimate sleep. This study offers an independent assessment of the performance of a portable electroencephalography headband. Compared to in-laboratory polysomnography, this type of device enables more accessible multi-night data collection in the natural sleeping environment. Such technologies have tremendous potential to expand research capacity and clinical applications. This article may help to inform the choice of appropriate technologies to be used to address specific research questions and to anticipate the strengths and limitations of this new technology.

Most portable monitors are restricted to indirect measures to estimate sleep. This study offers an independent assessment of the performance of a portable electroencephalography headband. Compared to in-laboratory polysomnography, this type of device enables more accessible multi-night data collection in the natural sleeping environment. Such technologies have tremendous potential to expand research capacity and clinical applications. This article may help to inform the choice of appropriate technologies to be used to address specific research questions and to anticipate the strengths and limitations of this new technology.

## Full-text entities

- **Diseases:** sleep-related breathing disorder (MESH:D012891)

## Full text

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## Figures

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## References

18 references — full list in the complete paper: https://tomesphere.com/paper/PMC12782022/full.md

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Source: https://tomesphere.com/paper/PMC12782022