Advances and Challenges of Wearable EEG Technology in Home-Based Sleep Monitoring

Authors

  • Yang Chen

DOI:

https://doi.org/10.54097/nwhhpz44

Keywords:

Wearable EEG, Sleep Monitoring, Home-Based Healthcare, Dry Electrode, Polysomnography (PSG).

Abstract

Sleep is essential for human health and well-being. Although the traditional sleep polysomnography (PSG) monitoring is the clinical gold standard for sleep assessment, its operation is complex and costly, so it is not suitable for long-term home use. Wearable electroencephalography (EEG) is a promising alternative, providing a new way to facilitate continuous sleep monitoring. This study focuses on the latest advances in wearable EEG devices for sleep monitoring, with a particular focus on headband, in-ear, and flexible forehead patch systems, including evaluating their design principles, balance between user comfort and signal quality, and performance in sleep staging technology compared to PSG. At the same time, not only the ongoing challenges of reducing signal artifacts and accurately detecting light sleep layers were discussed, but also the important advantages of wearable EEG in supporting personalized health tracking and longitudinal sleep studies. Finally, the study envisions the revolutionary potential of wearable EEG in popularizing sleep health management, including the use of artificial intelligence and cloud computing to optimize data processing to improve accuracy, and the positive shift from passive observation to active sleep intervention.

Downloads

Download data is not yet available.

References

[1] M. P. Walker, R. Stickgold, "Sleep, memory, and plasticity", Annual Review of Psychology, Vol. 57, pp. 139–166, 2006.

[2] G. Medic, M. Wille, M. E. Hemels, "Short- and long-term health consequences of sleep disruption", Nature and Science of Sleep, Vol. 9, pp. 151–161, 2017.

[3] R. B. Berry, R. Brooks, C. E. Gamaldo, S. M. Harding, C. L. Marcus, B. V. Vaughn, "The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications", American Academy of Sleep Medicine, Version 2.0, 2012.

[4] N. A. Collop, W. M. Anderson, B. Boehlecke, D. Claman, R. Goldberg, D. J. Gottlieb, D. Hudgel, M. Sateia, R. Schwab, "Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients", Journal of Clinical Sleep Medicine, Vol. 3, pp. 737–747, 2007.

[5] E. D. Chinoy, J. A. Cuellar, J. T. Jameson, R. R. Markwald, "Performance of four commercial wearable sleep-tracking devices tested under unrestricted conditions at home in healthy young adults", Nature and Science of Sleep, Vol. 14, pp. 493–516, 2022.

[6] C. J. de Gans, P. Burger, E. S. van den Ende, J. Hermanides, P. W. B. Nanayakkara, R. J. B. J. Gemke, F. Rutters, D. J. Stenvers, "Sleep assessment using EEG-based wearables – A systematic review", Sleep Medicine Reviews, Vol. 76, p. 101951, 2024.

[7] S. Chokroverty, "Overview of sleep & sleep disorders", The Indian Journal of Medical Research, Vol. 131, pp. 126–140, 2010.

[8] M. H. Silber, S. Ancoli-Israel, M. H. Bonnet, S. Chokroverty, M. M. Grigg-Damberger, M. Hirshkowitz, S. Kapen, S. A. Keenan, C. Iber, "The visual scoring of sleep in adults", Journal of Clinical Sleep Medicine, Vol. 3, pp. 121–131, 2007.

[9] C.-T. Lin, L.-D. Liao, Y.-H. Liu, I.-J. Wang, B.-S. Lin, J.-Y. Chang, "Novel dry polymer foam electrodes for long-term EEG measurement", IEEE Transactions on Biomedical Engineering, Vol. 58, pp. 1200–1207, 2011.

[10] P. J. Arnal, V. Thorey, E. Debellemaniere, M. E. Ballard, A. Bou Hernandez, A. Guillot, H. Jourde, M. Harris, M. Guillard, P. Van Beers, M. Chennaoui, F. Sauvet, "The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging", Sleep, Vol. 43, p. zsaa097, 2020.

[11] D. J. Levendowski, L. Ferini-Strambi, C. Gamaldo, M. Cetel, R. Rosenberg, P. R. Westbrook, "The accuracy, night-to-night variability, and stability of frontopolar sleep electroencephalography biomarkers", Journal of Clinical Sleep Medicine, Vol. 13, pp. 791-803, 2017.

[12] M. Mohamed, N. Mohamed, J. G. Kim, "Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review", Biosensors, Vol. 13, p. 1019, 2023.

[13] A. Sterr, J. K. Ebajemito, K. B. Mikkelsen, M. A. Bonmati-Carrion, N. Santhi, C. della Monica, L. Grainger, G. Atzori, V. Revell, S. Debener, D. Dijk, M. DeVos, "Sleep EEG derived from behind-the-ear electrodes (cEEGrid) compared to standard polysomnography: A proof of concept study", Frontiers in Human Neuroscience, Vol. 12, p. 452, 2018.

[14] T. Nakamura, V. Goverdovsky, M. J. Morrell, D. P. Mandic, "Automatic sleep monitoring using ear-EEG", IEEE Journal of Translational Engineering in Health and Medicine, Vol. 5, p. 2702558, 2017.

[15] S. Kwon, H. S. Kim, K. Kwon, H. Kim, Y. S. Kim, S. H. Lee, Y. T. Kwon, J. W. Jeong, L. M. Trotti, A. Duarte, W. H. Yeo, "At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea", Science Advances, Vol. 9, p. eadg9671, 2023.

[16] S. Shustak, L. Inzelberg, S. Steinberg, D. Rand, M. D. Pur, I. Hillel, S. Katzav, F. Fahoum, M. De Vos, A. Mirelman, "Home monitoring of sleep with a temporary-tattoo EEG, EOG and EMG electrode array: A feasibility study", Journal of Neural Engineering, Vol. 16, p. 026024, 2019.

[17] M. R. Carneiro, A. T. de Almeida, M. Tavakoli, "Wearable and comfortable e-textile headband for long-term acquisition of forehead EEG signals", IEEE Sensors Journal, Vol. 20, pp. 15107–15116, 2020.

[18] E. Wood, J. K. Westphal, I. Lerner, "Re-evaluating two popular EEG-based mobile sleep-monitoring devices for home use", Journal of Sleep Research, Vol. 32, p. e13824, 2023.

[19] Y. R. Tabar, K. B. Mikkelsen, N. Shenton, S. L. Kappel, A. S. R. Bertelsen, R. Nikbakht, P. Kidmose, "At-home sleep monitoring using generic ear-EEG", Frontiers in Neuroscience, Vol. 17, p. 987578, 2023.

[20] S. Khosla, M. C. Deak, D. Gault, C. A. Goldstein, D. Hwang, Y. Kwon, D. O'Hearn, S. Schutte-Rodin, "Consumer Sleep Technology: An American Academy of Sleep Medicine Position Statement", Journal of Clinical Sleep Medicine, Vol. 14, pp. 877–880, 2018.

[21] A. Supratak, H. Dong, C. Wu, Y. Guo, "DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG", IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 25, pp. 1998-2008, 2017.

[22] M. A. Jaoude, A. Ravi, J. Niu, H. Banville, N. Torres, C. Aimone, K. E. Ono, "Automated sleep staging on wearable EEG enables sleep analysis at scale", in Proc. 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 1-4, Apr. 2023.

[23] G. Garcia-Molina, T. Tsoneva, J. Jasko, B. Steele, A. Aquino, K. Baher, B. Peters, "Closed-loop system to enhance slow-wave activity", in Proc. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2320-2324, Nov. 2018.

Downloads

Published

10-02-2026

Issue

Section

Articles

How to Cite

Chen, Y. (2026). Advances and Challenges of Wearable EEG Technology in Home-Based Sleep Monitoring. International Journal of Biology and Life Sciences, 13(2), 150-157. https://doi.org/10.54097/nwhhpz44