Sleep is essential to well-being (Pilcher et al., 1997). It takes up to a third of our lives and is critical to good health (Ferrara & De Gennaro, 2001; Luyster et al., 2012). Unfortunately, the literature describing trends in sleep health can be unclear.
In some reviews, we find that adults sleep less than they used to (Ford et al., 2015; Sheehan et al., 2017). In others, we find that there has been little change (Bin et al., 2012; Matricciani et al., 2017).
Either way, knowing how people sleep is critical. Sleep loss can result in anxiety, depression, poor attention, and suicidal ideation (e.g. Taylor et al., 2003).
EEG lifts the lid on sleep, providing insight into what the brain is doing during the night and when. In this way, we can better understand the factors that contribute to restlessness and fatigue.
Why use EEG to study sleep?
While you sleep, your brain continues to process information; it remains active. We tend to cycle through various sleep stages four to five times a night, alternating between rapid eye movement (REM) and other, non-REM sleep stages (Patel et al., 2022).
Interested in EEG?
Importantly, sleep stages, like REM and N3 (deep sleep), can be recorded using electroencephalography (EEG).
EEG recordings present larger amplitudes and regular, so-called, delta waves (long, slow) during deeper, non-REM sleep (Nayak & Anilkumar, 2022). Conversely, during REM sleep, EEG waves are usually low-voltage and look remarkably similar to wakefulness.
There are many theories as to why our brain sleeps. It is likely that sleep is critical to memory (Sejnowski & Destexhe, 2000) and disposal of waste metabolites (Xie et al., 2013), amongst other things.
How to study sleep
Polysomnography (PSG) is the gold standard for sleep research and diagnosis.
PSG considers many sleep parameters, including muscle activation (measured with electromyography; EMG), eye movement (using electrooculography; EOG), heart rhythm (using electrocardiography; ECG), and brain activity (using EEG).
EEG is particularly important here. As part of the PSG analysis, EEG data is epoched into 30 second bins. Each 30 second bin is assigned a sleep stage, like N3. The AASM manual is normally used to determine which stage each bin represents.
Although PSG is the gold standard, it is far from perfect. Typically PSG is conducted in a laboratory, costing researchers time and money. One solution could be mobile PSG setups.
Why use mobile EEG for polysomnography?
Participants can take mobile EEG devices home with them; they can record their sleep in their own bed.
It is likely that recording sleep at home would result in better data. Certainly, it would save researchers time and money. At-home studies could also mean more people take part in sleep research, especially those that struggle to leave the home.
To track sleep stages using a mobile setup, scientists would have to manually analyze the data once it was returned to them, or use automatic, online pipelines.
Why Mentalab Explore may be a solution
Mentalab Explore is small enough to be taken anywhere. What is more, because it is an ExG device, it can record EEG, ECG, EMG, EOG, and movement data simultaneously. This reduces the need for cumbersome equipment to be transported to a subject’s home.
By recording for up to 12 hours, and being supremely comfortable, Explore excels at recording sleep throughout the night.
Can Mentalab Explore really record good sleep data?
Yes! Our Sales Director Eduard Deneke visited a sleep clinic. He undertook a PSG session using standard equipment (Somnomedics), and simultaneously recorded his sleep using an Explore device.
Take a look at the data below.
The data look remarkably similar.
Conclusion
Good sleep is essential to a good life. At Mentalab, we are excited to contribute to ecologically valid, mobile, participant-friendly sleep research.
If you are planning to conduct a sleep study, and are interested in using our technology, please reach out at contact@mentalab.com.
References
Bin, Y. S., Marshall, N. S., & Glozier, N. (2012). Secular trends in adult sleep duration: a systematic review. Sleep medicine reviews, 16(3), 223–230. https://doi.org/10.1016/j.smrv.2011.07.003
Ferrara, M., & De Gennaro, L. (2001). How much sleep do we need?. Sleep medicine reviews, 5(2), 155–179. https://doi.org/10.1053/smrv.2000.0138
Ford, E. S., Cunningham, T. J., & Croft, J. B. (2015). Trends in Self-Reported Sleep Duration among US Adults from 1985 to 2012. Sleep, 38(5), 829–832. https://doi.org/10.5665/sleep.4684
Luyster, F. S., Strollo, P. J., Jr, Zee, P. C., Walsh, J. K., & Boards of Directors of the American Academy of Sleep Medicine and the Sleep Research Society (2012). Sleep: a health imperative. Sleep, 35(6), 727–734. https://doi.org/10.5665/sleep.1846
Matricciani, L., Bin, Y. S., Lallukka, T., Kronholm, E., Dumuid, D., Paquet, C., & Olds, T. (2017). Past, present, and future: trends in sleep duration and implications for public health. Sleep health, 3(5), 317–323. https://doi.org/10.1016/j.sleh.2017.07.006
Nayak, C. S., & Anilkumar, A. C. (2022). EEG Normal Sleep. In StatPearls. StatPearls Publishing.
Sejnowski, T. J., & Destexhe, A. (2000). Why do we sleep?. Brain research, 886(1-2), 208–223. https://doi.org/10.1016/s0006-8993(00)03007-9
Sheehan, C. M., Frochen, S. E., Walsemann, K. M., & Ailshire, J. A. (2019). Are U.S. adults reporting less sleep?: Findings from sleep duration trends in the National Health Interview Survey, 2004-2017. Sleep, 42(2), zsy221. https://doi.org/10.1093/sleep/zsy221
Taylor, D. J., Lichstein, K. L., & Durrence, H. H. (2003). Insomnia as a health risk factor. Behavioral sleep medicine, 1(4), 227–247. https://doi.org/10.1207/S15402010BSM0104_5
Patel, A. K., Reddy, V., Shumway, K. R., & Araujo, J. F. (2022). Physiology, Sleep Stages. In StatPearls. StatPearls Publishing.
Pilcher, J. J., Ginter, D. R., & Sadowsky, B. (1997). Sleep quality versus sleep quantity: relationships between sleep and measures of health, well-being and sleepiness in college students. Journal of psychosomatic research, 42(6), 583–596. https://doi.org/10.1016/s0022-3999(97)00004-4
Xie, L., Kang, H., Xu, Q., Chen, M. J., Liao, Y., Thiyagarajan, M., O’Donnell, J., Christensen, D. J., Nicholson, C., Iliff, J. J., Takano, T., Deane, R., & Nedergaard, M. (2013). Sleep drives metabolite clearance from the adult brain. Science (New York, N.Y.), 342(6156), 373–377. https://doi.org/10.1126/science.1241224
