girl wih preparing for a sleep EEG test
Go back to blog
Sleep EEG for Diagnosis and Research
16 Min.
Medium
Sleep is a natural behavior that forms part of our daily routine. It can be found in virtually the entire animal kingdom and plays a fundamental role in people's well-being. During sleep, a significant number of brain and body functions remain active for restorative purposes. Without enough sleep, the homeostasis of the sleep-wake cycle is seriously compromised, which can result in a number of disorders directly affecting cognitive performance and physical health.

At the brain level, sleep involves various discrete states, commonly ranging from light to deep sleep. Each stage is associated with a characteristic pattern of electrical brain activity that can be recorded with an electroencephalogram (EEG). By examining the EEG markers of each sleep stage, experts can determine the sleep quality of an individual as well as the diagnosis of a related sleep disorder. Given the importance of sleep for physical and mental health, along with the growing prevalence of sleeping disorders, the study of EEG patterns raises a major interest among scientists. In this post, you will discover some of the latest neuroscientific findings in the study of human sleep, together with compelling aspects of how we study it using EEG.

What is a sleep EEG?

An EEG is a brain recording routine used to evaluate and monitor changes in patterns of electrical activity. It involves small sensors attached to the scalp using ergonomic headsets and caps. Typically, an EEG test is used to identify abnormal activity linked to neurological disorders, such as seizures and head injuries.

A man and a woman wearing dry and semi-dry EEG

Image 1: On the left, a wearable and dry-EEG (Bitbrain Diadem). On the right, a semi-dry water-based EEG cap (Bitbrain Versatile EEG 32). 

A sleep EEG tracks and registers the electrical activity of the brain in real-time during different states of sleep. It is a valuable tool in the context of sleep research as well as a method to detect sleep-related disorders in clinical settings.

Depending on the purpose of the test, there are commonly two types of sleep EEG approaches:

  1. Natural sleep EEG test: entails tracking and recording the normal activity of the brain while the person is asleep. The test usually monitors sleep during one or two hours, or even the entire night. It is very useful in the diagnosis of sleep disorders (Campbell, 2009). 
  2. Deprived-Sleep EEG test: in the context of epilepsy, this test is often carried out after several hours of sleep deprivation, which means getting less than the needed amount of sleep. This kind of test may be helpful in people suspected of having absence, myoclonic, or focal seizures (Giorgi et al., 2013).

Both types of sleep EEG tests can provide sufficient information about the quality of sleep. Specifically, a whole-night sleep EEG can inform about general sleep stability, cycle lengths, stage lengths, dominant frequencies, and other neural indices of sleep architecture. 

Normal Sleep EEG patterns 

Normal healthy sleep is characterized by a typical duration of 7-9 hours, a certain regularity, and the absence of sleep disruptions. The normal structure divides into two broad phases, including Non-rapid Eye Movement sleep (NREM) and Rapid Eye Movement sleep (REM), which alternate throughout the night. Based on a well-differentiated pattern of  EEG changes, NREM is divided further into four stages, namely, stage I, stage II, stage III - IV   (Nayak and Anilkumar, 2020; Patel, Reddy, Araujo, 2020).

Cycling structure between REM and NREM stages: 

Sleep EEG stages

STAGE I

 This stage corresponds with the transition period between wakefulness and sleep mostly characterized by drowsiness. It can last around 5-10 min (5% of total sleep).

 During this stage the EEG usually shows:

  • Shift from alpha frequency activity (8-12 Hz, >50 %) to theta activity  (3-7 Hz,  <50).
  • A mixed EEG pattern with low amplitude theta waves (3–7 Hz) along with slow-rolling eye movements (Schupp and Hanning, 2003).
  • Vertex sharp transients (VST), which are bilateral sharply contoured waves (sharp waves) with maximum amplitude over central scalp areas (Stern et al., 2011).

Sleep EEG - Stage I

STAGE II

Stage II  occupies approximately 50% of the entire night. It is considered a light sleep stage and lasts for approximately 20-25 minutes per sleep cycle.

During this stage, the brain activity continues descending and the EEG test shows a very characteristic brain wave sleep patterns:

  • Presence of sleep spindles (∼11–15 Hz). This EEG activity consists of waxing-and-waning waves lasting approximately 0.5 seconds, usually maximal in amplitude in central EEG locations (Nayak et al., 2020).
  • K-complex events are large negative deflections (or downstate), followed by a less intense positive deflection (or upstate) in the EEG. They are most prominent over frontocentral regions and generally last around one second(De Gennaro, Ferrara, and Bertini, 2000).

Sleep EEG - Stage II

STAGE III and IV

This corresponds to the deepest NREM sleep stage from which it is most difficult to wake up. Stage 3 entails 13–20% and stage 4 approximately 50% of total sleep time. According to the new sleep scoring classification of the American Academy of Sleep Medicine (AASM, 2009) both stages can be also identified as only one stage. It usually lasts between 20 and 40 minutes.

 The EEG activity has a recognizable pattern that includes:

  • High amplitude low-frequency delta wave (0.5–2 Hz) also called slow-wave sleep (SWS; Stern et al., 2011).
  • Sleep spindles may remain during stage 3, while notably decreasing in stage 4 (Moser et al., 2009).

Experts believe that deep sleep plays an important restorative role, allowing for mental and body recovery (Vandekerckhove and Wang, 2017). It is also critical for the strengthening of new memories (Jenkins & Dallenbach 1924; Klinzing et al. 2019).

Sleep EEG - Stage III and IV

REM 

REM sleep gets its name from the characteristic rapid eye movements (REM) seen in this sleep stage. The brain state associated with REM is as different from NREM sleep (stage I-IV) as both are from wakefulness. We dream in both NREM and REM sleep, but in the latter, dreams tend to be more vivid and hallucinatory in nature (Nir & Tononi, 2010). On average, a person can spend about 20% of their total sleep in this stage. However, REM sleep increases as sleep cycles progress over a night, including a total of four periods of REM sleep of approximately 10-15 min duration.

The EEG brainwave features resemble those seen in the awake state, but tend to be a bit slower and higher in amplitude:

  • Faster and low voltage activity in the frequency range of alpha (8-13 Hz) and theta (4-7 Hz) waves.
  • EEG flattening interval composed by low-voltage waves (Takahara et al., 2006).
  • A particular type of theta activity (2-5 Hz) termed "sawtooth waves" recognized by its morphology that resembles the blade of a saw. Such phasic activity precedes the onset of rapid eye movements (Surady et al., 2015).

REM sleep is essential to enhance emotional processing and cognitive functions such as memory and learning (Walker, 2009; Walker and Stickgold, 2010).

Sleep EEG - REM Stage
Sleep EEG stages

Polysomnography

Polysomnography is a widely accepted method for assessing the structure and quality of sleep. The main goal is to record ongoing brain and other physiological activity in all sleep stages, sometimes across several nights. 

It is classically carried out in a sleep lab or hospital during the participant´s normal sleeping hours. However, modern neurotechnology also allows home sleep EEG monitoring with high accuracy (Mikkelsen et al., 2019; Park and Choi, 2019).

  • In clinical settings, an EEG test can be essential in detecting brainwave anomalies that may be indicative of several sleep diseases. Typical examples include obstructive sleep apnea and circadian rhythm sleep-wake disorders (Tan et al., 2012). In this respect, recent research has demonstrated the value of longitudinal sleep monitoring to investigate the effects of insomnia and poor sleep in cognition (Ciano et al., 2017).
  • In research,  EEG recordings allow the study of the neural basis of sleep over extended periods of time, especially under different experimental conditions (e.g., natural sleep, deprived sleep, or during pharmacological interventions).

Sleep EEG in research settings

 

Latest sleep EEG research studies and results

The EEG study of human sleep has surged considerably in recent years. The current scope of research involves a wide range of topics that have gained increasing attention in recent years, including neurodevelopmental and neuropsychological disorders, as well as cognitive performance in healthy and clinical populations.

The following details describe what some of these investigations can reveal about EEG sleep.

Neurodevelopmental disorders

EEG sleep traces undergo prominent changes from birth to adulthood. Disruption of early brain maturation may entail sleep problems in individuals with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder / Asperger's syndrome, and developmental dyslexia (DD)  (Angriman et al., 2015).

  • Autism spectrum disorder:

The latest research reveals a disrupted pattern in EEG slow-wave activity during sleep architecture in autistic individuals. This is what is shown in the reported results of Lehoux and colleagues (2019) research. Specifically, children with autism spectrum disorder showed a pattern of EEG sleep activity that differed from that of their typically developing peers. Interestingly, differences were found in brainwave morphological characteristics and scalp distribution at the level of NREM sleep. These results suggest an impaired capacity to modulate EEG activity during sleep in autism.

  • ADHD:

In a study conducted by the University of Massachusetts (USA), a relevant finding indicates that inhibitory control deficits — typical in children with ADHD— are related to an abnormal EEG pattern involving REM-theta activity (Cremone et al., 2017). 

More recently, researchers from the University of Granada (Spain) investigated the relation between certain sleep parameters and cognitive functioning in children with ADHD. The results revealed that slow-wave sleep and REM latency may be predictive of cognitive performance in ADHD children (Ruiz-Herrera et al., 2021).

  • Developmental dyslexia (DD)

The relation between phasic EEG sleep activity and consolidation of vocabulary in children with DD was investigated by a group of researchers from Newcastle and York Universities (UK). The study concluded a reduced role of EEG sleep parameters (e.g., slow-wave activity and sleep spindles intensity) in vocabulary consolidation in dyslexic children compared to their typically developing counterparts (Smith et al., 2018). This is a very interesting finding because it suggests potential differences in the way that sleep works to support vocabulary strengthening in dyslexia.

Cognitive performance

Cognitive functions such as learning and memory, processing speed, or attention benefit from healthy sleep. Today, a main research question is whether cognitive performance can be improved through enhancement of sleep or sleep-associated neuronal activity patterns

  • Processing Speed

A recently published research led by Harvard Medical School (Boston, USA), studied the effects of sleep quality in a broad sample of 3819 older adults using whole night EEG monitoring. One remarkable finding revealed that individuals who scored better in processing speed and other cognitive domains tended to show profiles of EEG sleep similar to those often observed in younger individuals (Djonlagic et al., 2021). These findings point to multiple facets in the evolution of sleep EEG activity in older people, predicting different effects on cognitive performance.

  • Spatial Memory

Insights on the effects of obstructive sleep apnea (OSA) ­­, a serious sleep disorder, on spatial navigational memory processing in cognitively normal older individuals have been also reported in a published study of Mullins and colleagues (2021). A pattern of EEG slow-wave activity (sleep stage 3-4) was associated with better spatial navigation performance improvements in participants with OSA disorder, but not in those participants without.

  • Memory consolidation

Sleep plays a fundamental role in the processing and strengthening of different types of memories. The impact of NREM and REM phases of sleep has been demonstrated in access to declarative memories (recalling autobiographical facts or events), motor skills, and emotional memories (Wang, 2019). Researchers from the University of Montreal (Canada) demonstrated a direct link between sleep-spindles and the reactivation of a motor sequence learning memory (Fogel et al., 2017).  In a series of studies, the interplay of slow-wave and spindle activity during NREM sleep was shown relevant to facilitate memory consolidation (Mikutta et al., 2019) and closely related to the attenuation of forgetfulness (Denis et al., 2021)   This evidence suggests that, after learning, overnight memory consolidation can be actively enhanced by sleep.

Mental health

There is overwhelming evidence supporting the idea that appropriate sleep is crucial in maintaining high physiological and mental health levels. A prominent line of research in recent years focuses on the neurocognitive effects of disrupted sleep in patients with different neurological and psychiatric disorders.

  • Psychiatric disorders

Abnormal sleep activity on the EEG is typical in certain psychiatric disorders (Manoach, and Stickgold, 2019). One relevant study published in 2018 identified a phenotypic neuromarker of schizophrenia that was revealed by EEG -NREM sleep. The reported results indicated for the first time a disrupted pattern of slow-waves and sleep spindles activity in healthy relatives of patients diagnosed with schizophrenia that differed substantially from a control group (D´Agostino et al., 2018).

  • Epilepsy

Anomalous patterns of EEG sleep, such as nocturnal seizures, are common in epilepsy. There are also forms of epilepsy that mainly occur at night, like Rolandic epilepsy. A recent study by Laure Peter-Derex et al, (2020), relates sleep disruptions in epileptic patients with epileptiform activity (both during and between seizures) during different sleep phases. As highlighted by this group of researchers, sleep instability increases nocturnal waking times, resulting in a substantial impairment of sleep quality, ultimately affecting memory consolidation.

  • Cognitive decline in neurodegenerative disorders

Sleep disruptions can accelerate the aging process and symptoms of dementia. Interesting evidence reported by Djonlagic et al. (2019) identified important differences in the EEG-NREM sleep activity found in elderly women, compared to a normal control group, five years before the onset of cognitive decline and subsequent diagnosis of dementia. One important contribution of this research is the validation of a quantitative sleep EEG pattern as a promising neuromarker for early prediction of cognitive decline.

Conclusion

Normal sleep fulfills a restorative function in the brain, helping in the integration of many cognitive processes and the modulation of emotional stress. Examination of EEG brainwave activity allows researchers and clinicians to obtain a more comprehensive assessment of brain activity patterns associated with normal sleep. Given the detrimental impact of disrupted sleep, an EEG test is essential in the detection of sleep disorders, helping health care professionals to effectively create a treatment plan.

Currently, the study of EEG sleep is a prominent area of research that involves multiple topics of interest. A quick search of the latest published articles suggests that the evidence supporting the role of sleep in regulating mood, cognitive function, and mental health is quite compelling, although much remains to be understood about specific neural markers of sleep in healthy people and clinical populations. Undoubtedly, research will continue to yield new insights into the development and function of sleep, but it will increasingly take advantage of more real-world, home-based advances in EEG technology.

About the Author

Cristina Gil-López, Ph.D. - Researcher at the Psychology and Neurocognition institute (LPNC), University of Grenoble (France) / Associated Research Scientist at Polytechnic University of Valencia (Spain)

Cristina Gil-López is an experienced neuroscientist with an extensive career in human cognition research in academic and industrial settings. Her research includes the study of EEG patterns of visual perception, language processing, and emotion. She also investigates the effects of non-invasive brain stimulation on cognitive enhancement. Cristina is passionate about the secrets of the human brain, committed to high quality research and the applied use of neurotechnology.

You might also be interested in

References

  • Angriman M, Caravale B, Novelli L, Ferri R, Bruni O (2015). Sleep in children with neurodevelopmental disabilities. Neuropediatrics 46(3):199-210. DOI: 10.1055/s-0035-1550151.
  • Campbell I. G. (2009). EEG recording and analysis for sleep research. Current protocols in neuroscience, Chapter 10, Unit10.2. https://doi.org/10.1002/0471142301.ns1002s49
  • Ciano, C., King, T. S., Wright, R. R., Perlis, M., & Sawyer, A. M. (2017). Longitudinal Study of Insomnia Symptoms Among Women During Perimenopause. Journal of obstetric, gynecologic, and neonatal nursing: JOGNN, 46(6), 804–813. https://doi.org/10.1016/j.jogn.2017.07.011
  • Cremone, A., Lugo-Candelas, C. I., Harvey, E. A., McDermott, J. M., & Spencer, R. M. (2017). REM theta activity enhances inhibitory control in typically developing children but not children with ADHD symptoms. Experimental brain research, 235(5), 1491–1500. https://doi.org/10.1007/s00221-017-4906-7
  • D’Agostino, A., Castelnovo, A., Cavallotti, S. et al. Sleep endophenotypes of schizophrenia: slow waves and sleep spindles in unaffected first-degree relatives. npj Schizophr 4, 2 (2018). https://doi.org/10.1038/s41537-018-0045-9
  • De Gennaro, L., Ferrara, M., & Bertini, M. (2000). The spontaneous K-complex during stage 2 sleep: is it the 'forerunner' of delta waves?. Neuroscience letters, 291(1), 41–43. https://doi.org/10.1016/s0304-3940(00)01366-5
  • Denis, D., Mylonas, D., Poskanzer, C., Bursal, V., Payne, J. D., & Stickgold, R. (2021). Sleep spindles preferentially consolidate weakly encoded memories. The Journal of neuroscience : the official journal of the Society for Neuroscience, JN-RM-0818-20. Advance online publication. https://doi.org/10.1523/JNEUROSCI.0818-20.2021
  • Djonlagic, I., Mariani, S., Fitzpatrick, A. L., Van Der Klei, V., Johnson, D. A., Wood, A. C., Seeman, T., Nguyen, H. T., Prerau, M. J., Luchsinger, J. A., Dzierzewski, J. M., Rapp, S. R., Tranah, G. J., Yaffe, K., Burdick, K. E., Stone, K. L., Redline, S., & Purcell, S. M. (2021). Macro and micro sleep architecture and cognitive performance in older adults. Nature human behaviour, 5(1), 123–145. https://doi.org/10.1038/s41562-020-00964-y
  • Djonlagic, I., Aeschbach, D., Harrison, S. L., Dean, D., Yaffe, K., Ancoli-Israel, S., Stone, K., & Redline, S. (2019). Associations between quantitative sleep EEG and subsequent cognitive decline in older women. Journal of sleep research, 28(3), e12666. https://doi.org/10.1111/jsr.12666
  •  Feriante, J., & Singh, S. (2020). REM Rebound Effect. In StatPearls. StatPearls Publishing.
  • Fogel, S., Albouy, G., King, B. R., Lungu, O., Vien, C., Bore, A., Pinsard, B., Benali, H., Carrier, J., & Doyon, J. (2017). Reactivation or transformation? Motor memory consolidation associated with cerebral activation time-locked to sleep spindles. PLoS ONE, 12(4), Article e0174755. https://doi.org/10.1371/journal.pone.0174755
  • Giorgi, F. S., Perini, D., Maestri, M., Guida, M., Pizzanelli, C., Caserta, A., Iudice, A., & Bonanni, E. (2013). Usefulness of a simple sleep-deprived EEG protocol for epilepsy diagnosis in de novo subjects. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 124(11), 2101–2107. https://doi.org/10.1016/j.clinph.2013.04.342
  • Institute of Medicine (US) Committee on Sleep Medicine and Research; Colten HR, Altevogt BM, editors. Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. Washington (DC): National Academies Press (US); 2006. 2, Sleep Physiology. Available from: https://www.ncbi.nlm.nih.gov/books/NBK19956/
  •  Manoach, D. S., & Stickgold, R. (2019). Abnormal Sleep Spindles, Memory Consolidation, and Schizophrenia. Annual review of clinical psychology, 15, 451–479. https://doi.org/10.1146/annurev-clinpsy-050718-095754
  • Mikkelsen, K.B., Tabar, Y.R., Kappel, S.L. et al. (2019) Accurate whole-night sleep monitoring with dry-contact ear-EEG. Sci Rep 9, 16824. https://doi.org/10.1038/s41598-019-53115-3
  • Mikutta, C., Feige, B., Maier, J. G., Hertenstein, E., Holz, J., Riemann, D., & Nissen, C. (2019). Phase-amplitude coupling of sleep slow oscillatory and spindle activity correlates with overnight memory consolidation. Journal of sleep research, 28(6), e12835. https://doi.org/10.1111/jsr.12835
  • Mullins, A. E., Williams, M. K., Kam, K., Parekh, A., Bubu, O. M., Castillo, B., Roberts, Z. J., Rapoport, D. M., Ayappa, I., Osorio, R. S., & Varga, A. W. (2021). Effects of obstructive sleep apnea on human spatial navigational memory processing in cognitively normal older individuals. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 10.5664/jcsm.9080. Advance online publication. https://doi.org/10.5664/jcsm.9080
  • Moser, D., Anderer, P., Gruber, G., Parapatics, S., Loretz, E., Boeck, M., Kloesch, G., Heller, E., Schmidt, A., Danker-Hopfe, H., Saletu, B., Zeitlhofer, J., & Dorffner, G. (2009). Sleep classification according to AASM and Rechtschaffen & Kales: effects on sleep scoring parameters. Sleep, 32(2), 139–149. https://doi.org/10.1093/sleep/32.2.139
  • Nayak CS, Anilkumar AC. EEG Normal Sleep. 2020 Jun 5. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2021 Jan–. PMID: 30725708.
  • Nir, Y., & Tononi, G. (2010). Dreaming and the brain: from phenomenology to neurophysiology. Trends in Cognitive Sciences, 14, 88–100.
  • Park, K.S., Choi, S.H. Smart technologies toward sleep monitoring at home. Biomed. Eng. Lett. 9, 73–85 (2019). https://doi.org/10.1007/s13534-018-0091-2
  • Patel AK, Reddy V, Araujo JF. Physiology, Sleep Stages, (2020). In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2021 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK526132/
  • Peter-Derex, L., Klimes, P., Latreille, V., Bouhadoun, S., Dubeau, F., & Frauscher, B. (2020). Sleep Disruption in Epilepsy: Ictal and Interictal Epileptic Activity Matter. Annals of neurology, 88(5), 907–920. https://doi.org/10.1002/ana.25884
  • Ruiz‐Herrera, N., Guillén‐Riquelme, A.,  Díaz‐Román, A.,  Buela‐Casal, Gualberto. (2021). Sleep, academic achievement, and cognitive performance in children with attention‐deficit hyperactivity disorder: A polysomnographic study. Journal of Sleep Research 48. https://doi.org/10.1111/jsr.13275
  • Schönauer, M., & Pöhlchen, D. (2018). Sleep spindles. Current biology : CB, 28(19), R1129–R1130. https://doi.org/10.1016/j.cub.2018.07.035
  • Schupp, M., & Hanning, C. (2003). Physiology of sleep. BJA CEPD Reviews, 3, 69-74.
  • Smith, F., Gaskell, M. G., Weighall, A. R., Warmington, M., Reid, A. M., & Henderson, L. M. (2018). Consolidation of vocabulary is associated with sleep in typically developing children, but not in children with dyslexia. Developmental science, 21(5), e12639. https://doi.org/10.1111/desc.12639
  • Stern, J. M., Caporro, M., Haneef, Z., Yeh, H. J., Buttinelli, C., Lenartowicz, A., Mumford, J. A., Parvizi, J., & Poldrack, R. A. (2011). Functional imaging of sleep vertex sharp transients. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 122(7), 1382–1386. https://doi.org/10.1016/j.clinph.2010.12.049
  • Suradi, Y. M., Algreeshah, F. S., Rincon-Flores, N., & Benbadis, S. R. (2015). Mystery case: sawtooth waves: another normal variant not to over-read as epileptiform discharges. Neurology, 84(12), e87–e88. https://doi.org/10.1212/WNL.0000000000001386
  • Tan DEB, Tung RS, Leong WY, Than JCM. (2012). Sleep Disorder Detection and Identification. Procedia Engineering 41: 289-295. ISSN 1877-7058. https://doi.org/10.1016/j.proeng.2012.07.175.
  • Vandekerckhove, M., & Wang, Y. L. (2017). Emotion, emotion regulation and sleep: An intimate relationship. AIMS neuroscience, 5(1), 1–17. https://doi.org/10.3934/Neuroscience.2018.1.1
  • Walker, M., Stickgold, R. (2010). Overnight alchemy: sleep-dependent memory evolution. Nat Rev Neurosci 11, 218. https://doi.org/10.1038/nrn2762-c1
  • Walker M. P. (2009). The role of sleep in cognition and emotion. Annals of the New York Academy of Sciences, 1156, 168–197. https://doi.org/10.1111/j.1749-6632.2009.04416.x
  • Ye, L., & Richards, K. C. (2018). Sleep and Long-Term Care. Sleep medicine clinics, 13(1), 117–125. https://doi.org/10.1016/j.jsmc.2017.09.011