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Chapter 24
Clinical Applications
Cilia Jaeger
Abstract EEG is a valuable tool in clinical research with the potential for broader
in clinical practice. It is commonly used in the early phases of clinical research as
use
an objective tool for studying disease pathophysiology, pharmacodynamic effects on
brain activity, and biomarker development. Despite its promise, adoption in routine
clinical care remains limited due to the lack of standardized parameters—such as
channel number, processing pipelines, analysis techniques, and recording
duration—as well as concerns about inter-rater reliability. These challenges have
slowed translation, yet EEG continues to play a growing role in exploring biomarkers, disease onset and progression, and therapeutic outcomes. This chapter first
defines clinical research and covers why EEG is a cost-effective, beneficial tool for
clinical applications. It highlight s applications where the use of EEG is well
established, such as in epilepsy and sleep research. The chapter also covers more
novel applications of EEG as a biomarker in neurodegenerative disease and assessment of drug efficacy in clinical trials. The chapter then ends on the future of using
electrophysiology more widely in clinical settings, including using spectral fingerprints and EEG connectivity patterns for evaluating disease onset and progression.
Keywords Biomarkers · Clinical research · Anesthesia · Epilepsy · Sleep disorders ·
Neuro
degenerative disease · Brain-computer interface
24.1 Introduction
EEG has served as an important tool in neuroscience, offering insight into brain
function through non-invasive, (near) real-time recordings of neural activity. In
academic research, EEG is commonly used to study cognition, sensory processing,
and information processing. Translating these findings into clinical practice requires
moving beyond controlled laboratory experiments toward clinical research. The
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_24
317
318 C. Jaeger
focus shifts to understanding disease processes, identifying measurable indicators of
disease progression, and evaluating thera peutic interventions and outcomes. This
chapter focuses on the use of EEG in clinical research and clinical applications.
24.2 Definition of Clinical Research and Why EEG Is
a Useful Tool
24.2.1 Clinical Research
The NIH defines clinical research as any research conducted within human participants. This includes research that is conducted on materials originating from human
participants such as specimens or cognitive phenomena that were directly obtained
from the participant by the researcher (NIH,
studies
are also classified as clinical research. Also falling under the clinical research
umbrella are outcomes research and health service research, which evaluate the
effects of healthcare interventions on patient health, quality of life, and care delivery.
Many examples of human-oriented research have been described throughout this
book, including Chap.
ations. This refers to research conducted in patient populations in clinical trials
applic
and studies that test and validate diagnostic tools, treatment interventions, and
biomarkers for their use in healthcare.
23. This chapter focuses on examples of research for clinical
2024). Epidemiologic and behavioral
24.2.2 EEG in Research for Clinical Applications
EEG is a fundamental tool in non-invasive neuroscience research including research
for clinical applications. The use of EEG in clinical neurological and psychiatric
research is valuable because it is non-invasive, cost-effective, and offers high
temporal, (near) real-time whole brain monitoring.
EEG is
imaging techniques, such as MRI, because there is a lack of standardized acquisition
and analysis pipelines (Sinha et al.,
across
configuration, quality control checks, and data preprocessing and evaluation
methods. For example, wet electrodes provide better signal quality than dry electrodes; however, they require more preparation time. Another key challenge in
standardizing EEG signal evaluation arises from inter-rater variability of EEG data
analysis, particularly related to artifact removal (Tatum et al.,
2017). Because many EEG artifacts are non-stationary, they cannot fully be removed
by automated artifact algorithms. Therefore, most EEG preprocessing pipelines
require a subjective manual removal of artifacts from the EEG data. This can result
not as commonly used in clinical applications as other whole-brain
2016; Jobert et al., 2012). Inter-data variability
EEG data sets can arise from differences in EEG equipment and channel
2016; Shirk et al.,
24 Clinical Applications 319
in variability in artifact attenuation or even the removal of the signal of interest
(Mumtaz et al.,
other
s. Part of defining standardized EEG methods in clinical research involves
finding EEG features that are robust to some variability in inter-rater evaluation, as
well as incorporating AI-assisted analysis methods (Rutkowski & Saab, 2025).
Efforts are being made to overcome these challenges by providing standardization recommendations such as those defined by the minimal technical requirements
provided by the American Clinical Neurophysiology Society (Sinha et al., 2016).
Other
standardization guidelines have already been adopted by the EEG community
such as standardized electrode layouts defined by the 10–20 system. Further standardization efforts will be discussed at the end of this chapter.
2021). Some EEG features are more robust to residual artifacts than
24.2.3 EEG as a Biomarker
An integral part of clinical research is finding new biomarkers that can be used to
diagnose a disease and assess the progression and severity of a condition or disease.
A biomarker is a measurable biological indicator (Strimbu & Tavel, 2010). Biomark
ers can also be used to measure response to drug and therapeutic intervention.
In a more classic sense biomarkers are often measurable substances such as identification of a specific genetic mutation or protein levels that are linked to a specific
condition. The use of neuroimaging biomarkers to differentiate between normal and
pathological processes is gaining increasing interest (Linden,
further
markers that indicate the probability of a disease occurring or progressing. Whereas
predictive biomarkers determine the likelihood that a group of individuals will
respond to a treatment and are used as an outcome measure for clinical trials (Califf,
2018).
pattern
logical state (Fig. 24.1). Aberrant electrical patterns recorded in EEG are used to
identify
Alzheim
psychi
to
neuror
a vital tool in clinical research to assist with establishing techniques for clinical
diagnosis and disease monitoring. Examples of more novel applications will also be
discussed and finall y the chapter will end with a discussion on what is needed to
standardize EEG recording and signal analysis methods to make EEG a more
commonly used tool in clinical research.
be divided into different types. Prognostic biomarkers are classified as bio-
EEG featu res can be used as biomarkers by providing a measurable electrical
of underlying neural activity that correlates with a physiological or patho-
and evaluate disease progression in epilepsy (Britton et al., 2016),
er’s disease (Monllor et al., 2021), sleep disorders (Dai et al., 2021), and
atric disorders (Farzan et al., 2017). EEG can also be used to monitor response
treatment, such as antiseizure medication (Porcaro et al.,
ehabilitation efforts and brain activity after brain injury (Zhang et al.,
The next sections
highlight examples in which EEG has been widely accepted as
2012). Biomarkers can
2024) or to track
2022).
320 C. Jaeger
Fig. 24.1 EEG features as biomarkers. Different EEG features such as event-related potentials,
frequency patterns of neural oscillations, and EEG connectivity can be used as potential biomarkers.
These features can be used as biomarkers. Diagnostic biomarkers are used to identify diseases
within a population. Prognostic biomarkers are classified as biomarkers that indicate the probability
of a disease occurring or progressing. A predictive biomarker helps identify the probability of a
group of individuals that respond to a treatment
24.3 Examples of Clinical Applications of EEG
24.3.1 Epilepsy
The use of EEG as a clinical tool has been well established in the diagnosis and
evaluation of epilepsy and sleep disorders. EEG has also been vital for the development of anesthetic agents. Both epilepsy and sleep are discussed in much more detail
in their respective dedicated chapters and will only be briefly mentioned here. Yet
they provide examples of how EEG has been standardized to be used effectively in a
clinical setting.
EEG has become an invaluable tool for epilepsy research because it offers a near
real-tim
activity. One of the most useful electrical patterns used to characterize a specific
epilepsy syndrome are interictal epileptiform discharges (IEDs). IEDs are large
electrophysiological events that can be observed in the EEG intermittently between
seizures. The type, location, and frequency of IEDs can be used to localize the onset
of seizure activity within a specific brain region and characterize seizure type
(Britton et al.,
gold standard for clinical epilepsy research (Beniczky & Schomer, 2020). This
enable
facilitating the identification and classification of speci fic epilepsy syndromes. The
presence and frequency of IEDs are also frequently used as predictive biomarkers to
e, high temporal resolution signal that is time-locked to seizure onset and
2016).
Combined
with video monitoring, long-term EEG record ing has thus become the
s simultaneous assessment of clinical behavior and electrostatic artifacts,
24 Clinical Applications 321
evaluate the efficacy of antiseizure medication (Reynolds et al., 2023). To ensure
diagnostic efficacy and reliability, guidelines have been developed to define minimum requirements for recording durations, channel counts, and hardware configurations (Beniczky & Schomer,
widespr
locations across individuals. Additionally, regulatory boards have emerged to establish and assess these guidelines, such as the International League Against Epilepsy or
the American Epilepsy Society.
ead acceptance in epilepsy research to ensure EEG is recorded in similar
2020). Standard electrode layouts have also gained
24.3.2 Sleep and Sleep Disorders
Another application in which EEG has been established as a valuable clinical tool is
sleep. In Chap. 6, the distinct electrical patterns associated with different sleep stages
were introduced. In clinical applications, deviations from these distinct electrical
patterns can be used to characterize sleep disorders. During sleep studies, EEG is
often combined with other physiological measurements. The combination of these
physiological recordings is defined as polysomnography (PSG), which records
changes in physiological activity such as eye movements, muscle activity, heart
and respiration rate (Jobert et al.,
Polysomnography is commonly used in the diagnosis and evaluation of disease
progression in obstructive sleep apnea. Sleep apnea is typically characterized by
obstruction of the upper airways, which disrupts respiration and leads to sleep
disturbances (Martins & Conde, 2021). In a clinical setting, polysomnography
ings during sleep are used to calculate the number of apnea events, defined
record
as a complete pause of breathing for over 10 s, within a given period. Based on these
events, a respiratory disturbance index can be calculated to determine the severity of
sleep apnea (O’Connor et al.,
effective
monitor fewer parameters than in-lab recordings for ease of use, they remain a
valuable initial screening tool for detecting sleep apnea (Kapur et al.,
tionall
environment rather than a hospital setting, where discomfort and unfamiliarity can
impact sleep quality and alter results. This approach balances convenience with
diagnostic accuracy, demonstrating the v alue of having portable and cost-effective
tools that can be worn by the patient not only within a clinical setting.
and the presentation of obstructive sleep apnea. Research suggests that men generally exhibit more apneic events than women. However, women often exhibit more
fragmented sleep due to apneic events, as women express a lower arousal threshold
that is thought to result from the influence of hormone regulation of arousal and
ventilatory control (Martins & Conde,
during REM sleep are also more common in women, whereas apnea events generally
only occur during NREM sleep in men (O’Connor et al., 2000). These findings have
tool for at-ho me sleep recordings. While home-based sleep apnea tests
y, home testing may yield more reliable data, as patients sleep in a familiar
Polysomnograph
ic studies have identified gender differences in sleep architecture
2012).
2000). PSG also presents itself as a useful and cost-
2017). Addi-
2021; Bonsignore et al., 2019).
Apnea events
322 C. Jaeger
also been correlated to hormonal differences in men and women and highlight the
importance of considering gender-specific factors in the diagnosis and management
of sleep disorders. Understanding these differences can lead to more tailored and
effective treatment strategies for both men and women.
24.3.3 Anesthesia
EEG is also used to monitor sedation and loss of consciousness in response to
anesthetic drugs to evaluate their ef ficacy, optimize drug administration, and minimize drug-related complications. During the induction of sedation, a decrease in
high-frequency activity and an increase in alpha and theta oscillatory activity are
observed for most anesthetic drugs. This correlates with drowsiness and loss of
awareness of the envir onment. During the maintenance phase, during which surgery
is performed, slow delta waves below 4 Hz and alpha oscillations are predominant in
the EEG (Yousefi-Banaem et al., 2020). Intermittent burst suppressions can also be
observed, which are characterized by bursts of high-amplitude slow-wave activity
followed by flat or isoelectric EEG. For many anesthetic drugs, the presence of burst
suppression indicates deep sedation (Shanker et al.,
to
monitor for oversedation, which can cause complications in patients. Oversedation has been linked to post-operative delirium, especially in older adults (Mei
et al., 2020). Studies also show that prolonged exposure to deep sedation may lead to
ive impairments affecting memory and executive function (Vacas et al., 2021).
cognit
In clinical practice, the objective assessment of depth of anesthesia induced by
sedat
ive drug is crucial. The bispectral index (BIS) monitor serves as an example for
standardizing and objectively analyzing EEG data to monitor sedation levels. The
BIS is a number given between a range of 0 and 100, which indicates the level of
brain activity. Zero indicates total loss of brain activity, and one hundred indicates
wakefulness (Fig.
bispectral analysis methods. It requires analyzing EEG data from a set of frontal
and
electrodes and removing common artifacts such as muscle activity and eye movement. Time-based measures such as the suppression ratio are calculated from these
electrodes. The ratio is defined as the time of isoelectric activity divided by the total
time of EEG activity. The EEG signal is also decomposed into different frequency
bands to assess the power of the low-frequency bands in comparison to the power of
the high-frequency bands. The bispectral analysis then includes measuring phase
synchronization between different frequency components in the EEG. Higher
degrees of synchronization across the frequency bands indicate a deeper level of
anesthesia. A weighting algorithm is used to combine the different EEG-derived
parameters to create the bispectral index (Yousefi-Banaem et al.,
BIS monitor
ously processed and analyzed, providing an update on the bispectral index every
15–30 s. A continuous display of this index can thus help clinicians administer and
monitor correct anesthesia levels. However, there are some limitations to the
24.2). The BIS index is calculated using a combination of spectral
ing can be applied in near real-time. The raw EEG data is continu-
2021). This feature can be used
2020).
24 Clinical Applications 323
Fig. 24.2 Example demonstrating real-time analysis of the bispectral index. (a) The raw EEG data
is analyzed in near real time to monitor the level of anesthesia and adjust the level of drug
administration. (b) The bispectral index is a value ranging from 0 (no brain activity) to 100 (fully
awake). The BIS value increases as the brain wave amplitude decreases and the frequency increases.
The awake state has a BIS index of 90 and is characterized by small amplitude, fast frequency
waves. Deep anesthesia occurs around a score of 30 and is characterized by large amplitude and
slow frequency oscillations. Part B of this figure has been reproduced by Yousefi-Banaem et al.
(
2020) with permission
bispectral index. Since BIS monitoring is proprietary, there is a lack of transparency,
difficulty in identifying bias within the algorithm, and a lack of reproducibility of
results that were used to verify the algorithm (Connor et al.,
is
sensitive to certain artifacts such as EMG activity (Youefi-Banaem et al., 2020).
Certai
n drugs like ketamine present with distinct EEG patterns leading to an increase
2022). Additionally, BIS
in high frequency activity and low frequency power. The BIS score will inaccurately
indicate a higher level of wakefulness for ketamine that does not reflect the depth of
sedation induced by the drug (Hans et al.,
the limitations of the BIS, other clinical tools for assessing EEG and anesthesia
of
2005; Musizza & Ribaric, 2010). Because
depth have been considered, such as the Patient State Index and the Richmond
Agitation-Sedation Scale (Han et al.,
2023).
This section provides some examples where EEG is well established for assessing
neurological disorders and, importantly, wher e some consensus on recording techniques and analysis has been established. These advances have made EEG indispensable for disease differentiation and for using EEG features as predictive
biomarkers to guide treatment decisions, such as assessing drug responsiveness in
epilepsy and sedation. The next section will shift focus to EEG’s role as a prognostic
biomarker.
324 C. Jaeger
24.4 Examples of EEG as a Biomarker in Disease
Prognostics and Treatment
This subsection will focus on more novel applications using EEG as a biomarker in
clinical research focusing on diagnosing and monitoring neurodegenerative diseases
and movement disorders.
24.4.1 Alzheimer’s Disease and Neurodegeneration
As life expectancy has increased worldwide, the prevalence of neurodegenerative
diseases has also strongly increased. Early detection of neurodegenerative diseases
can help slow the progression of the disease and improve quality of life. However,
finding cost-effective tools to diagnose early signs of neurodegeneration is still a
challenge. EEG presents a cost-effective and portable tool that is a potential candidate for detecting Alzheimer’s disease (AD) in its presymptomatic stages (Whelan
et al.,
2022). Along with cognitive impairment and loss of memory, AD is charac-
terized by the presence of clusters of beta-amyloid protein in the brain. While
amyloid plaques serve as a good AD biomarker candidate, they can only be detected
by using PET imaging, which is costly and invasive (Monllor et al., 2021). Slowing
of
EEG activity in amyloid-positive compared to amyloid-negative populations has
been observed. EEG slowing is characterized by reduction of alpha an d beta activity
and an increase in delta and theta activity across the brain (Monllor et al., 2021; Hata
al., 2024). Sleep disturbances including reduction in the percentage of slow-wave
et
sleep
and REM sleep have been correlated with the progression of AD (Zhang et al.,
2022).
Currently, most EEG-related clinical research on AD is focused on validating
EEG biomarkers in populations already diagnosed with AD and presenting with
cognitive impairment. The EEG abnormalities observed in AD are not exclusive to
Alzheimer’s and overlap with other psychiatric and neurodegenerative disorders.
The complexity of brain activity linked to cognition presents a further challenge.
There is distinct individual variability in brain activity associated with cognitive
function, and the onset of cortical activity changes linked to dementia is subtle
(Horvath et al.,
in-ea
r EEG devices are emerging as a cost-effective solution for collecting long-term
and individualized data (Byrom et al.,
lliams et al. (
McWi
simult
aneous EEG recorded from portable headsets that was played repeatedly
over a period of 12 weeks within a healthy, aging population. The study characterized cognitive performance in correlation with EEG features that are indicative of
cognitive decline with age. Such study designs implementing long-term monitoring
can help classify cognitive aging and differentiate between normal and aberrant
2018; Al-Nuaimi et al., 2021 ). However, portable EEG headsets and
2018; Musaeus et al., 2022). A study by
2021) assessed the performance of a cognitive game with
24 Clinical Applications 325
progression. Together with other diagnostic modalities, portable long-term EEG
monitoring has the potential as a non-invasive, cost-effective tool for early diagnosis
of AD.
24.4.2 Brain-Computer Interfaces and Movement Disorders
EEG is also a valuable tool in brain-computer interfaces and the treatment of
neurological conditions. In short, brain-computer interfaces (BCIs) are systems
that create a direct communication pathway between the brain and an external
device, allowing for quantification of CNS activity and translation of the CNS
activity into an artificial output that replaces or enhances the biological output
(Varbu et al.,
BCI is within the study and treatment of movement disorders (Wolpaw et al.,
of
2002). For example, in studies where the activity from the motor cortex is used to
contr
ol motor output, such as limb movement in individuals experiencing paralysis
or other movement disorders (Zhang et al., 2022; Varbu et al., 2022). Because of its
high
temporal resolution, EEG can provide near real-time feedback and thus presents
a valuable tool for BCI research. Sensorimotor rhythms such as the Mu rhythm and
beta rhythm observed in the sensorimotor cortex have been identified and associated
with motor planning and movement intention. The desynchronization or decrease in
Mu power is observed when preparing a movement or when imagining a movement
(Pfurtscheller et al.,
se in clinical populations with amyotrophic lateral sclerosis (ALS) who
respon
develop severe motor impairments over time. These individuals lose the ability to
perform motor control, yet they are still able to perform motor imagery (McFarland,
2020). Desynchronization of the Mu rhythm during motor imagery thus serves as a
ial measure that can be used to drive artificially assisted motor commands in
potent
ALS populations.
Event-related potentials such as the P300 component also serve as EEG features
that
can be evaluated and used to facilitate communication between the brain and the
artificial system. Individuals with severe progression of ALS lose the ability to
speak. A study by Geronimo and Simmons (
which individuals with ALS were asked to focus on a specific letter to spell a
in
word. The study showed that the P300 response can be used to select the letter the
participant is focusing on. This enables the participants to spell words and gives
them a method for communicating their thoughts.
While such
patients affected by movement disorders, BCI applications for initiating behavioral
tasks are limited because they require a robust EEG feature that is unique to one task
and has little overlap with other cognitive functions (Wolpaw et al.,
, a major limitation of using scalp EEG in BCI applications is its low spatial
more
resolution. The blurring of signals at the scalp makes it difficult to identify the exact
location of a specific neural response, making it difficult to precisely decode an
2022; Wolpaw et al., 2020). One of the most frequent applications
2006). This response can be utilized to help initiate a motor
2020) used speech encoding paradigms
applications prove beneficial for improving the quality of life for
2020). Further-
326 C. Jaeger
intended action (Mumtaz et al., 2021). For example, within the context of motor
movement, distinct brain regions within the motor cortex are responsible for the
movement of specific body parts. Due to EEG’s low spatial resolution, electrical
signals from adjacent brain regions that are responsible for the movement of
different parts of the body can ov erlap, making it challenging to decode the user’s
intended movement.
24.5 Future of EEG in Clinical Applications
EEG has been widely recognized as an important tool in clinical research. It provides
a cost-effective, (near) real-time direct measure of brain activity. With advancements
in recording technology and analysis methods, it has the potential to be applied to
more clinical applications.
For example, over the last few decades, there has been significant progress in
implem
difficult to diagnose because they can manifest differently in individuals with
varying degrees of severity (van den Heuvel et al.,
2014). Many psychiatric disorders also share common symptoms such as cognitive
dysfun
differences in brain activity and function within normal and psychiatric populations
is crucial yet challenging (Linden, 2012). Recent research has shown that psychiatric
disord
slow, sustained changes and fast, transient activity (van den Heuvel et al.,
Sadaghi
networks
imaging modalities such as functional magnetic resonance imaging, EEG-derived
connectivity measures are of increasing interest. Due to its high temporal resolution,
EEG connectivity analyses can provide important information on fast, transient brain
dynamics. Frequency-distinct connectivity changes have also proven important for
building a better understanding of how complex information processing and communication across the brain can occur concurrently by having differential coupling
and decoupling across and within frequency bands (Sadaghiani et al.,
from artifacts, the complexity of EEG data associated with brain states makes it hard
to use EEG analysis in clinical settings to distinguish normal patterns of behavior
and cognition from abnormal patterns (Mumtaz et al.,
However
methods is important for making EEG a more widely accepted tool in clinical
research. The emergence of regulatory boards, such as the American Clinical
Neurophysiology Society, to establish best practice guidelines within clinical
research is also important.
enting EEG to study various psychiatric disorders. Psychiatric disorders are
2019; Lenartowicz & Loo,
ction or mood disturbances. Differentiating varying degrees of individual
ers often involve disruption of large-scale brain networks that include both
ani et al., 2022). While the study of dynamic temporal changes within brain
or functional connectivity has classically been studied using different
2022).
In addition
to common limitations like low spatial resolution and interference
2021; Babiloni et al., 2021).
, a push for standardizing EEG recording, pre-processing, and analysis
2019;