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Chapter 6
Brain States
Cilia Jaeger
Abstract Brain states are patterns of neural activity that correlate with behavior and
flect underl ying neural processing. Within the electroencephalography (EEG)
re
recording, brain states can be classified by observing the temporary patterns or
fluctuations within the electrical signal over time that can be observed across
individuals. Technological advancements in recording and analysis methods have
greatly enhanced our ability to study brain dynamics, which has in turn given rise to
a better understanding of the structure and function of the brain. In this chapter, we
define brain states in the context of EEG and provide examples of research fields
studying brain states. We highlight how classification of brain states within specific
applications has helped us to understand the function of both the healthy brain and
the disruption of function in disease.
Keywords Wakefulness · Sleep states · Anesthesia · Neural synchrony · Oscillatory
rhyth
ms · Brain networks
6.1 Introduction
The raw electroencephalography (EEG) signal is a mix of electrical activity generated by different sources of underlying neural populations and other physiological
signals (see Chap.
Yet
how exactly can we relate EEG to a neuroscientific question? Neural processing
and subsequent brain activity are not static. Rather, brain activity changes with
arousal, cognitive tasks, and pathology. Similarly, EEG recordings fluctuate over
time. These fluctuations are not only random noise but can instead reflect meaningful
patterns of neural activity that can correspond to a functional role. This chapter
introduces the concept of brain states—dynamic patterns of neural activity—and
explains how EEG can be used to study them.
C. Jaeger (✉)
Brain Products GmbH, Gilching, Germany
e-mail:
cilia.jaeger@brainproducts.com
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026
T.
Warbrick (ed.), The EEG Handbook,
https://doi.org/10.1007/978-3-032-20450-9_6
2: “What Is EEG?” for further information on the EEG signal).
63
64 C. Jaeger
6.2 Definition of Brain States in the Context of EEG
6.2.1 What Is a Brain State?
A brain state is defined as a recurring, reliable pattern of neural activity observed
across large-scale brain networks. These patterns are temporary and serve a functional role in supporting cognitive and behavioral processes (Tang et al., 2012;
Kringel
neural
other whole-brain neuroimaging methods (Greene et al., 2023).
distinct
uration that enables or constrains cognitive processes. A cognitive state refers to a
specific mental operation, such as attention, memory, meditation, or mental imagery.
A physiological state consists of bodily or homeostatic processes, including arousalrelated states, sleep, hunger, and stress (Greene et al.,
subsequent neural activity. Neuromodulation shapes network coordination and shifts
between brain states (Lee & Dan, 2012). Brain states are also affected by environmental factors such as sensory input, task demand, and cognitive workload, which
can cause changes in arousal, attention, and emotional responses (Chang et al., 2021;
López
adapts
dynamics to mental and physiological functions.
bach & Deco, 2020; Lee et al., 2024). Brain states are identified by their
patterns observed in EEG , functional magnetic resonance imaging (fMRI), or
While brain states emerge with cognitive or physiological processes, they are
from physiological or cognitive states. Brain states reflect the neural config-
2023).
Brain states are influenced by neuromodulation of neurotransmitter levels and
et al., 2019; Deco et al., 2022).
Studying the transition between brain states provides insight into how the brain
information processing to support cognition and behavior and links neural
6.2.2 Brain States Measured with EEG
When measuring scalp EEG, dynamic patterns distributed across distinct spatial
regions or electrodes can be observed over time in response to specific behavior or
cognitive feature within a population of individuals. The distinct and recurring
configurations of electrical activity that emerge with a behavioral response or
cognitive process correspond to underlying neural activity and function (Buzsaki
& Watson,
Patte
to classify and study the relevance of brain states.
One of
amplitude of the posterior alpha rhythm that occurs in response to closing the
eyes. Since this observation, patterns within the alpha response have been studied
within the context of vigilance and attention, sensory processing, and working
memory (Siegel et al., 2012). Fluctuations of neural oscillations within distinct
2012). EEG can be a useful tool to measure these so-called brain states.
rns observed in either the EEG time-domain or frequency-domain can be used
the first distinct patterns observed with EEG was an increase in the
6 Brain States 65
Fig. 6.1 EEG features to characterize brain states. (a) Temporal or spectral features can be
extracted from the raw EEG data. In the time-domain, the voltage changes evoked in response to
a stimulus are averaged together across several repeated trials to measure the event-related potential
evoked by the stimulus. The raw EEG data can also be broken down into its component frequencies
to investigate activity across neural oscillations. An event-related time-frequency analysis can also
be applied to study how the brain’s oscillatory activity changes over time in response to a stimulus.
(b) EEG features can also be extracted spatially by either looking at the spatial distribution of EEG
signal properties across scalp topography maps or by source localizing specific EEG features.
(Reproduced from Lenartowicz and Loo (
2014), with permission
frequency bands are one of the prominent features used to characterize brain states
and their link to behavior and cognition (Van Bree et al.,
2025; Babiloni et al., 2020).
Frequency-based measures are one example of an EEG feature that can be used to
characterize brain states. Time-domain features such as event-related potentials
(Woodman,
ence
states.
2010) and connectivity measures such as phase locking value or coher-
(Sadaghiani et al., 2022) are other common EEG features used to study brain
Figure 6.1 provides a brief overview of common EEG features used to study
brain states.
The study
of brain states not only plays an important role in understanding the
function of the brain during different states of consciousness, cognition, and behavior. It also helps study aberrant brain states, in which atypical patterns of neural
66 C. Jaeger
activity are associated with neurological or psychiatric conditions (Voytek &
Knight,
dysreg
example, the onset of epileptic seizures induces a specific pattern of high-amplitude
spikes that can be observed across a subset of EEG channels (Andrade-Valenca
et al., 2011). The characteristic spiking features and high temporal resolution of EEG
have
with EEG, their role in understanding the function of the brain, and how the
classification of brain states allows the study of pathological brain function.
2015; Buzsaki & Watson, 2012). EEG has been a crucial tool to measure
ulated patterns of electrical activity and the underlying neural dynamics. For
made it an important tool for studying epilepsy.
The following sections highlight examples of brain states that can be recorded
6.3 Examples of Brain States
6.3.1 Awake State Sleep State
EEG has been a fundamental tool for linking the circadian rhythm to neural activity
patterns and distinct sleep and wakefulness states. The circadian rhythm results from
oscillatory gene transcription that drives rhythmic changes in hormone expression
and sleep-wake cycles in response to the 24-h day–night cycle (Wright et al., 2012;
Mendoz
sleep
nated by high frequencies and low amplitudes that change in response to external
stimuli and cognitive demand (Scammell et al., 2017). Sleep can be categorized into
two
that is present during REM sleep and lacking during non-REM sleep. During REM,
eye movements elicit polarity changes in ocular electrodes and frontal electrodes and
desynchronous, low-amplitude, mixed-frequency neural oscillations within EEG
recordings (Girardeau & Lopes-Dos-Santos,
highet al.,
wave
2011).
imaging
from wake to sleep has highlighted key brain structures. It has also revealed the
communication patterns that drive circadian rhythms in the body and the brain.
Specifically, it has helped clarify the intricate link between the suprachiasmatic
nucleus (SCN) in the hypothalamus, thought to be the master clock driving rhythmic
gene transcription and hormone regulation, and rhythmic signaling along the hypothalamic nerve cells with other brain structures via neurotransmitter regulation
(Mendoza,
in
complex feedback loop that regulates arousal, wakefulness, and consciousness.
a, 2025). Distinct EEG patterns have helped characterize the awake and
states and their transitions. During wakeful states, EEG recordings are domi-
main stages based on a characteristic behavior of rapid eye movements (REM)
amplitude, slow-frequency oscillations dominate the EEG signal (Clawson
2016). During non-REM sleep, transient states of cortical coupling of slow
s and sleep spindles have been linked to memory consolidation (Fogel & Smith,
Along with a combination of other imaging techniques, such as the structural
of nerve tracts, the characterization of neural patterns during the transition
2025). Figure 6.2 highlights signaling pathways between the structures
the hypothalamus, the brainstem, thalamus, and cortex that are thought to create a
,
2021). Whereas in non-REM sleep,
6 Brain States 67
Fig. 6.2 The circadian rhythm. The suprachiasmatic nucleus (SCN) of the hypothalamus acts as the
principal clock that regulates rhythmic bodily processes such as sleep–wake cycles and appetite, as
well as rhythmic processes of the autonomic nervous system that are important for homeostasis. The
SCN neurons have a 24-h rhythm that is generated through a feedback loop of clock gene
transcription and protein inhibition. The SCN then transmits electrical signals to other brain regions,
which are involved in cognition, mood, and other neural processes. (The figure is reproduced from
Mendoza (
2025), with permission)
The transition from sleep to wakefulness is characterized by the shift in the
prominence of synchronous slow-wave activity to desynchronous high-frequency
activity in the EEG. The degree of attenuation of high-amplitude, low-frequency
activity has also been linked to varying degrees of arousal and attention (Xu et al.,
2021; Wright et al., 2012; von Gall, 2022).
While it is still not fully understood which underlying processes modulate
ncy-specific oscillatory activity, one key structure involved in wakefulness
freque
and arousal is the reticular activating system (RAS) in the brainstem (Garcia-Rill
et al., 2013). Electrical stimulation of the RAS has been shown to cause a shift in
EEG
signal from the prominence of slow wave synchronicity to an EEG pattern of
desynchronous, low-amplitude, high-frequency activity resembling wakefulness
(Moruzzi & Magoun,
ncies and decreased complexity in the EEG patterns correlate with a comatose
freque
1949). These EEG patterns of decreased power across high
state as compared to an awake state responsive to external stimulus.
6.3.2 Consciousness States: Presence Loss (Anesthesia)
,
The RAS is thought to neuromodulate cortical activity via ascending projections to
the thalamus, hypothalamus, and cerebral cortex (Kinomura et al., 1996). Measuring
differences
in neural activity along the ascending RAS pathways has become a main
target for studies of consciousness and consciousness disorders. Lesion studies and
pharmacological modulation of activity along these pathways have helped define the
causal mechanisms behind behavior, brain states, and levels of consciousness
68 C. Jaeger
(Edlow et al., 2012; Garcia-Ri ll et al., 2013; Scammell et al., 2017). Anesthesia is a
prime example of how transitions between brain states can be linked to different
levels of consciousness. To assess levels of consciousness, general anesthesia can be
administered during a cognitive task with more and less salient stimuli. Generally,
the responsiveness to less salient stimuli will decrease first, followed by a loss of
response to salient stimuli (Purdon et al.,
to
changes in heart rate, muscle tone, and blood pressure, which can further be
monitored to assess physiological states. Applying propofol, a general anesthetic,
results in a shift of more high-freque ncy beta-gamma activity to alpha activity in
frontal EEG electrodes. Coherent occipital alpha oscillations diminished, whereas
coherent frontal alpha oscillations emerged (Sepulveda et al.,
se to stimuli also coincides with the increase in ultra-low-frequency (less than
respon
1 Hz) EEG power (Purdon et al.,
when
regaining consciousness. By observing EEG patterns under anesthesia,
researchers gain controlled, reproducible states that allow them to study the neural
dynamics underlying the shifts between wakefulness and unconscious states.
In this section, we have highlighted brain states that exhibit distinct neural
patterns and elicit specific behavioral responses, which can be easily studied, such
as the transition between wakefulness and sleep or the loss of consciousness induced
by anesthesia. The next section explores how defining brain states in healthy
individuals can help identify deviations linked to neurological or physiological
conditions.
2013; Hagihira, 2015). These EEG patterns reverse
2013). Loss of consciousness is also linked
2020). The loss of
6.4 Pathological Brain States
Brain states have the potential to provide insight into how normal brain functions
differ from abnormal states that may be associated with various neurological and
psychiatric conditions. This section provides a brief insight into specific electrical
patterns recorded in EEG that have been linked to certain pathologies.
6.4.1 Traumatic Brain Injury
EEG may serve as a valuable tool to identify pathological brain states linked to
traumatic brain injury. For example, epileptiform, high-amplitude, high-frequency
activity has been observed in EEG shortly after a concussion. These high activity
patterns are followed by distributed suppression of cortical activity and slowing of
oscillatory activity recorded in EEG (Ianof & Anghinah,
ncy of the posterior alpha rhythm have also been observed subacutely within
freque
EEG recordings following mild traumatic brain injury (Kadri & Apriani,
While
it is difficult to quantify these changes in EEG features within specific
individuals experiencing traumatic brain injury, classifying aberrant brain states
linked to traumatic events may help identify the impact of brain injury on cognition.
2017). Alterations in the
2022).
6 Brain States 69
6.4.2 ADHD
Population comparisons between participants diagnosed with and without attentiondeficit hyperactivity disorder have revealed elevated theta power and decreased beta
power in participants with attention deficit hyperactivity disorder (ADHD) (Newson
& Thiagarajan,
due to ADHD being a spectrum disorder, and individuals diagnosed with it may
part
exhibit a variety of symptoms. Other EEG features, such as event-related potentials,
may be more advantageous for linking deficits in sensory processing and sensory
discrimination, a common behavioral outcome in ADHD, with a specific brain state.
For example, early event-related components such as N1, P1, and P2 have been
linked to early sensory processing and have been found to be altered in ADHD
(Kaiser et al., 2020). A study by Kaiser et al. (2021) demonstrated that during a cued
contin
uous performance task, the amplitude of the parietal P3 component was lower
in children with ADHD than the control group. This study illustrates how the P3
component, an EEG feature that has been linked with sustained attention (Johnstone
et al., 2013) differs between two study populations in response to a given task.
These examples underscore the importance of EEG in understanding various
pathol
ogies by highlighting how specific electrical activity patterns are linked to
conditions such as epilepsy, concussion, traumatic brain injury (TBI), ADHD, and
schizophrenia. Since many neurological and psychiatric disorders present heterogeneously within clinical populations, it has proven challenging to define brain states
linked to specific disorders. Yet identifying neural activation patterns corresponding
to distinct symptoms in combination offers promising insight into using brain states
for diagnosis and treatment options. Some in-depth examples are covered in
Chap.
24 (“Clinical Applications”).
2019). Yet the reproducibility of these results has proven difficult in
6.5 Framework of Brain States
So why does the characterization of brain states remain such an integral part of
neuroscience research? The brain is highly organized and dynamic, and it is constantly coordinating neural processes linked to complex behavior and cognition.
Brain states representing distributed patterns of activity elicited by physiological or
cognitive processes also have a functional relevance for future behavioral and
physiological responses (Lopez et al.
has highlighted the value of identifying distinct brain states. Yet defining brain states
linked to more complex cognitive tasks is challenging due to the distributed and less
well-defined nature of the underlying neural activity. Within this framework, it is
possible that more than one brain state can be present at a given instant of time and
can be present across different cognitive tasks (Greene et al.,
2025; Spitzer & Haegens, 2017).
in one specific brain region but rather emerges from large-scale networks (Voytek &
2019; Tang et al., 2012).
Furthermore, cognitive function is rarely localized
So far, this chapter
2023; van Bree et al.,
70 C. Jaeger
Fig. 6.3 Brain states characterized by differences in neural activity patterns in the beta frequency.
(a) Changes in beta power, within-area, and between-area coherence have been observed in
different brain regions and are also linked with different cognitive processes. (b) The location
and type of beta-related activity change with respect to content-specific information processing, for
example, during working memory. This figure highlights how activity patterns across brain regions
can overlap for different cognitive processes, therefore emphasizing the importance of studying
brain states and dynamic neural activity patterns. (This figure is reproduced from Spitzer and
Haegens (
2017), with permission)
Knight, 2015; Siegel et al., 2012). Figure 6.3 depicts different brain regions that have
been shown to be activated in multiple cognitive tasks. This makes linking brain
states to specific cognitive functions challenging.
A variety of techniques are available for recording whole-brain activity and
studying brain function, including positron emission tomography (PET), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), EEG, and
fMRI. There are also tools for modulating brain activity, such as transcranial
magnetic stimulation (TMS) or focused ultrasound. Yet these techniques differ
with respect to the type of neural signal recorded, as well as the temporal and spatial
scales of the signal. Due to the number of imaging techniques and differences in
spatial and temporal resolution, brain states may present differently when recorded
with different tools (Greene et al.,
states
and has left an incomplete picture of how the dynamics of neural activity from
2023). This can make it challenging to define brain
the individual cell level to whole brain activity relate to behavior and function.
Advancements in combining neural acquisition techniques and computational
modelling provide an opportunity for conceptualizing the relationship between
different signal properties, neural dynamics, and brain function in health and disease
and have helped decompose complex and dynamic cognitive proces ses.
For example,
within EEG, patterns of frequency-speci fic oscillatory activity that
change in line with a measurable behavioral response in a cognitive task have shaped
theories for their role in neural communication. During sensory processing, localized
narrow-band high-freque ncy gamma oscillations correlate with distinct stimuli features such as luminance and color in visual processing (Han et al.,
2022). Based on
6 Brain States 71
such correlations, localized high-frequency activity is thought to play a role in
feature binding and object representation during sensory perceptual processing
(Hermes et al.,
mance have been linked to fluctuations in the amplitude or phase of
perfor
2015). Whereas correlations between fluctuations in perceptual
low-frequency oscillations (Voytek et al., 2010; Siegel et al., 2012). Furthermore,
task
performance and attention have also been correlated with synchronization of
low-frequency oscillations across distributed brain regions (Canolty & Knight,
2010). These findings suggest low-freque
ncy oscillations may modulate cognitive
processes such as attention, which are thought to occur across distributed brain
regions.
Dynamic cross-frequency coupling between frequency-specific oscillations in
correspondence with cognit ive tasks suggests that cross-frequency coupling may
coordinate neural activity organized across different temporal scales across different
brain regions. For instance, low-frequency alpha coupling with high-frequency
gamma oscillations may set the temporal framework for which sensory information
is encoded within a specific region (Voytek et al.,
ncy alpha-gamma coupling may represent a causal shift in a brain state
freque
2010). Thus, a shift in the cross-
corresponding to a transition in attentional demand (Canolty & Knight, 2010).
Exami
ning changes in cross-frequency coupling across different brain states is just
one example of how information obtained from EEG can be interpreted to build a
complex model of how the brain can maintain hierarchical organization and flexibility. Further, classifying brain states in combination with other neuroscience
techniques can help neuroscientists build a better representation of brain biology
and function in a normal state.
More recently, there is growing interest in using EEG to study state-related
connectivity, complementing more traditional fMRI connectivity research. While
resting state fMRI has been instrumental in mapping large-scale brain networks, the
high temporal resolution of EEG is advantageous for studying rapid, real-time brain
state dynamics that cannot be captured with fMRI. Studying the fast reconfiguration
of neural activity enhances understanding of processes such as cognitive flexibility
and adaptability (Sadaghiani et al.,
reorganization of low-frequency oscillatory networks in preparation for
the
2022). Task-switching studies have found that
switching tasks appears to support the brain’s ability to proactively prepare for and
maintain task-relevant information (Lopez et al.,
Historically,
large-scale functional connectivity of the brain is more typically
2019).
studied in MRI. However, EEG connectivity also offers key insights into polyrhythmic brain networks, which are defined as patterns of coordinated communication
across multiple frequency bands. Sadaghiani et al. (
that
slower neural rhythms can organize the timing of faster, local activity. Thereby
2022) review evidence showing
suggesting that fast, transient, and electrophysiological patterns are linked to largescale network dynamics more traditionally studied in fMRI. This interplay of
connectivity stren gthens the argument that rhythmic interactions measured across
different neuroimaging techniques coordinate complex neural communication
within anatomically and functionally defined networks.
72 C. Jaeger
6.6 Concluding Summary
EEG can be used to measure patterns of neural activity corresponding to some
behavioral outcome. Studying dynamic patterns in EEG recordings, such as the
composition of specific frequency bands within oscillatory activity, has led to a
better understanding of how the brain can adaptively process information in relation
to behavior and cognition. However, brain states remain incompletely understood
because they are dynamic, often overlapping, and can share similar neural signatures
across different cognitive or physiological conditions. Furthermore, there is no
universal framework for classifying brain states. This is due to different measuring
techniques available for measuring brain activity that require special signal
processing and are prone to artifacts. These factors can also influence how the neural
activity is interpreted.
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