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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_6027_Библиотеки_им_академика_М_И_Перельмана.pdf
368 J. Mavi and K. Whitehead
200
RR intervals (msec) from ECG
900
0
Cz
Pz
F8
T8
TP10
P8
O2
F4
C4
P4
F7
T7
TP9
P7
O1
F3
C3
P3
ECG
Resp
882 914Seconds
Hypopnea onset
ds
Seconds
EEG attenuation
Widest RR interval
1200
500uV
Fig. 27.1 Illustrative data showing how an episode of subtle bradycardia is preceded by a
hypopnoea and EEG attenuation. Top panel: Heartbeat (RR) intervals (asterisks) extracted from a
19 min recording segment from an infant of 35 weeks corrected gestational age (gestational
age + postnatal age) using the EEG-Beats toolbox (Thanapaisal et al.,
variability,
including intermittent bradycardias with one example shaded pink. Bottom panel: EEG,
2020). Note the RR intervals
electrocardiography (ECG) and respiratory movement (Resp) recordings associated with the shaded
example bradycardia. The infant is awake, 3 min before a transition into REM sleep. No fi lters
applied except 50 Hz notch filter; DC offset removed. Midline central referential EEG montage
27.2.2 Somatosensory States Monitoring in Neonates
The sleeping neonatal brain can process somatosensory inputs (Hrbek et al., 1973),
without awakening (Georgoulas et al.,
experi
mental stimuli, like taps from a device. These controlled protocols have
elucidated many important principles, such as how somatosensory cortical excitability spans hierarchical networks (Whitehead et al.,
2021). This has often been researched using
2019b). However, such protocols
27 EEG Applications in Neonatal and Paediatric Clinical Neuroscience 369
lack ecological validity, and are not suitable for the most sensitive parts of the body,
like the face. As an example of what is possible using a naturalistic paradigm,
stimulation gently delivered by the experimenter’s forefinger has allowed to map the
face within a full cortical body map in preterm infants: with leg, arm, and face
representation moving downwards from the central vertex (Donadio et al.,
Given that functional somatosensory representation of the facial area is in place
from preterm age, we can also study how it is modulated by sucking—a major
primitive reflex observed during REM sleep and wakefulness. EEG recordings show
that higher frequency, lower power rhythms are enhanced by sucking in neonates,
although some co-linearity between the stimulus and sleep-wake shifts is likely
(Lehtonen et al.,
ally during feeding, above non-nutritive sucking (pacifier use) (Lehtonen
especi
et al., 2016). This could indicate that the ‘extra’ sensory stimuli when sucking is
associated with milk intake (gustatory, olfactory, interoceptive from milk entering
the stomach) heightens the engagement of cortical areas. Thus, EEG can capture the
fullness of multi-modal sensory experience.
Another naturalistic way to study somatosensory processing is to use the infant’s
self-generated movements, for example, by examining coherence between limb
own
electromyographic signals and EEG. Such studies have shown that not only does the
proprioceptive and tactile feedback from muscle activation evoke changes in EEG
(Milh et al.,
2019c), but causality flows in the other direction too (cortico-muscular coherence)
from
the first few months after birth (Kanazawa et al., 2014; Ritterband-Rosenbaum
al., 2017). Therefore, EEG can track the early emergence of the mechanisms
et
which
will link the somatosensory body map to later voluntary motor control
(e.g. reaching for objects) (Kanazawa et al., 2014).
To summarise, as in Sect. 27.2.1, and Sect. 27.2.2, we reiterate how EEG activity
does
not exist in a vacuum. We show how ongoing naturalistic somatosensory inputs
modulate cortical rhythms. Indeed, this may underlie the ability of positive somatosensory inputs—like naturalistic touch and feeding—to counteract the negative
impact of nociceptive (painful) stimuli in hospitalised neonates (Maitre et al.,
2017). In Sect. 27.3, devoted to children and young adults especially with
neurode
complex sensory inputs like pain, after first describing its value in monitoring sleep.
velopmental conditions, we expand upon EEG ’ s contribution to tracking
2016; Barlow et al., 2014). These effects on the EEG occur
2007; Losito et al., 2017; Whitehead et al., 2018; Whitehead et al.,
2018).
27.3 Paediatric EEG Applications
Unlike preterm and ill neonates, who must be cared for in hospital, the wider
paediatric population typically resides at home and participates in everyday community life. For these populations, traditional EEG recordings in a hospital or
laboratory pose substantial challenges: the unfamiliar environment, travel-related
stress, and demands of prolonged monitoring introduce diverse external factors that
370 J. Mavi and K. Whitehead
may affect the interpretability and reliability of observed activity (Riley et al., 1981;
Milne-Ives et al.,
2023).
Recent developments in EEG technology have led to advances in both mobile
(ambulatory) and home-based systems (Biondi et al., 2021; Rehman et al., 2024).
Mobile EEG
permits moni toring across diver se and dynamic settings, such as
school, but lacks video recording. In contrast, home EEG generally includes video
recording, facilitating precise correlation between environmental stimuli,
behavioural changes, and EEG activity, but for this reason is typically restricted to
a single location. While distinct in their advantages and limitations, both methods
offer unique extended windows into brain activity in real-world contexts,
e.g. incorporating eating, caregiver contac t, and free movement, in addition to
increased practicality and inclusiveness (Debener et al.,
2015; Lau-Zhu et al.,
2019). This enables the capture of neural activity during unstructured behavioural
and ordinary routines, which are often disrupted or unrepresentative in hospital
states
or laboratory settings. This also presents a valuable opportunity for use in younger
populations with complex medical needs or neurodevelopmental conditions, such as
intellectual disability, whose assessments may be particularly liable to disruption in
an unfam iliar setting (Lau-Zhu et al., 2019). Therefore, mobile and home EEG could
facilit
ate accurate monitoring and interpretation of the internal states that are critical
to child wellbeing and development, but otherwise remain challenging to capture,
particularly for children who are minimally verbal, cognitively impaired, or
behaviourally atypical (Cassidy et al., 2023).
While mobile
and home EEG are still emerging as research tools in medically
complex children, they already see established clinical use in routine paediatric
epilepsy diagnostics (Michaeli et al.,
2024; Brunnhuber et al., 2014; Carlson et al.,
2018), offering an effective alternative to inpatient monitoring for classifying sei-
and capturing sleep-related epileptiform activity. These multi-day video-EEG
zures
recordings, set within the patients’ homes, are annotated with seizure events reported
by the patient or carer. Alongside these events, annotations are also made pertaining
to the patient’s behaviours, activity, and other potentially relevant environmental
stimuli, allowing to cross-reference this information to ongoing physiological variability (Fig.
27.2).
Thus, this clinical foundation in epilepsy diagnostics highlights
an exciting potential for transferability of such home-based assessments to other
contexts, like sleep and sensory state monitoring. In Sect.
EEG studies investigating sleep, and then in Sect. 27.3.2 highlight the value of
recent
27.3.1 we first review
assessing sleep in home settings. Following this, in Sect. 27.3.3 we explore EEG
studies
of sensory states, particularly chronic pain and interoception. While these
topics are lesser studied using home EEG when compared to epilepsy or sleep, in
Sect.
27.3.4 we
discuss relevant emerging findings and consider how naturalistic
EEG paradigms may unlock novel avenues for assessing these under-recognised
experiences in real-world settings.
27 EEG Applications in Neonatal and Paediatric Clinical Neuroscience 371
“Patient playing with cat.”
60s
Fp1
Fp2
F3
F4
C3
C4
P3
P4
O1
O2
F7
F8
T3
T4
T5
T6
A1
A2
Fz
Cz
Pz
ECG
R Deltoid
L Deltoid
(EMG)
“Sharpened theta burst
while looking at phone.”
“Patient playing video games.”
500uV
“Medication given.”
Fig. 27.2 Examples of annotated events occurring during home EEG monitoring. Annotations
collated from a deidentified research database of multiple home-video EEG sessions carried out in
paediatric populations at King’s College Hospital. For illustrative purposes, annotations are
superimposed onto a 20 min segment of EEG, electrocardiography (ECG), and electromyography
(EMG) activity from a patient of 10 years of age undergoing 24-h home-video EEG monitoring.
The child is awake. Midline central referential EEG montage
27.3.1 Sleep Monitoring in Children and Adolescents
From the neonatal period onwards, sleep is a foundational process in brain development. In juvenile mammals, experimental studies demonstrate that sleep supports
synaptic plasticity and refinement of cortical circuitry (Li et al.,
2012). Consistent with a similar role in human children, sleep quality is associated
with
learning (Dutil et al., 2018), as well as the regulation of affect and physiological
ses. For example, lower sleep quality in otherwise healthy children has been
proces
linked to reduced parasympathetic vagal activity and a resultant baseline sympathetic dominance, a physiological profile associated with increased cardiovascular
risk (Michels et al.,
2013).
In children with neurodevelopmental conditions — such as
autism, attention deficit hyperactivity disorder (ADHD), developmental epilepsies,
and cerebral palsy (CP)—sleep disturbances are common and often deeply
2017; Yang & Gan,
372 J. Mavi and K. Whitehead
interconnected with daytime functioning (Gorgoni et al., 2020; Angriman et al.,
2015; Halstead et al., 2021; Hodge et al., 2014).
From 2 months of age, non-REM sleep can be subdivided into stages 1, 2, and
3 (Grigg-Damberger, 2016). In both healthy (Hoedlmoser et al., 2014; Chatburn
et
al., 2013) and neurodevelopmentally atypical children (Tessier et al., 2015;
Bölsterli et al., 2017), sleep stage-specific features captured by EEG, such as sleep
les and slow-waves, can be used to index sleep quality. In addition to this, their
spind
frequency and spatiotemporal characteristics are associated with various aspects of
cognitive maturation, memory consolidation, and intellectual ability, making them
valuable biomarkers in typical and atypical development (Page et al.,
examp
le, children with Rolandic epilepsy, a condition accounting for up to a quarter
of childhood epilepsies (Ross et al.,
2020), show reduced sleep spindle density in
2021). For
centrotemporal brain regions during non-REM stage 2 compared to healthy controls,
even in the absence of active seizures, and this is associated with impaired sleepdependent memory consolidation (Kwon et al., 2025).
Children with more severe epilepsies, such as epileptic encephalopathies
erised by Electrical Status Epilepticus in Sleep (ESES), frequently experience
charact
acquired regression in language, memory, and behaviour, which even after epileptic
seizures resolve, can persist for life (Arican et al., 2021). ESES is generally defined
near-continuous spike-wave discharges during non-REM sleep (Tassinari et al.,
by
2000). These discharges, therefore, severely disrupt normal sleep architecture—
particula
rly slow-wave sleep (non-REM stage 3)—interfering with the neural processes associated with learning and development. One such process is the homeostatic regulation of network synchrony. During wakefulness, neuronal networks
become increasingly active and interconnected as we interact with the environment,
resulting in greater synaptic strength and higher cortical synchrony. Slow-wave
sleep is thought to reflect the recalibration of this activity via downscaling of
potentiated synaptic connections to an energetically sustainable state (Tononi &
Cirelli,
wave
2006). This recalibration can be indexed with EEG by the slope of slow-
s, calculated by dividing the amplitude change of a wave by the time between
its negative peak and subsequent zero crossing. The slope typically decreases over
the course of the night as synaptic strength is progressively downregulated (Kurth
et al.,
2010). In ESES, these slope decreases are impaired, or even completely absent,
recover after remission (Bölsterli et al., 2017). Inter estingly, children with the
but
highes
t degree of typical slope decrease during active ESES were found to have the
best cognitive outcomes post-remission of ESES. It has therefore been hypothesised
that slope changes in non-REM sleep could be an early prognostic indicator of
developmental outcomes.
Collectively,
these findings exemplify how EEG can index both subtle and
profound changes in cortical activity that persist and evolve throughout sleep,
highlighting its value not just as a diagnostic tool , but in tracking and potentially
even predicting neurodevelopmental health. However, to fully investigate sleep
dynamics and address such hypotheses, it is paramount that the sleep captured is
representative and ecologically valid.
27 EEG Applications in Neonatal and Paediatric Clinical Neuroscience 373
27.3.2 Naturalistic Sleep Monitoring in Children
and Adolescents
Laboratory-based polysomnography (PSG), which assesses sleep via EEG and other
synchronised monitoring, including cardiorespiratory and electromyographic signals, is considered the gold standard for sleep assessment (Rundo & Downey, 2019),
but
is often poorly tolerated by children with sensory sensitivities, anxiety, or
behavioural challenges (Lanzlinger et al., 2023; Coverstone et al., 2014). The
atory environment, restrictive instrumentation, and rigid procedures frequently
labor
provoke arousal and disrupt natural sleep patterns—a phenomenon known as the
‘first-night’ effect (Agnew Jr. et al.,
this effect is particularly pronounced in children and adolescents compared to young
adults (Ding et al., 2022). Consequently, this can render conventional assessments
unrepr
esentative and incomplete.
More naturalistic mobile and home EEG paradigms provide a compelling alter-
, allowing for overnight recordings in a familiar environmen t. Even
native
low-channel density, highly portable systems can reliably detect key features of
sleep architecture, including sleep spindles and K-complexes of stage 2, and slowwaves of stage 3 non-REM sleep (Kwon et al., 2021; Mikkelsen et al., 2019). As
indica
ted in Sect. 27.3.1, these features play a crucial role in understanding the role
of sleep in both developmentally typical and atypical contexts.
In addition, full PSG is possible in the home, allowing sophisticated integration of
with autonomic and behavioural monitoring, and insight into how sleep
EEG
interacts with broader physiological states in a naturalistic environment. For example, a paediatric home PSG study capturing multiple physiological measures, including EEG, cardiorespiratory metrics, pulse oximetry, and leg electromyographic data,
as well as audio recording, demonstrated the feasibility of home sleep assessment. Of
55 PSG sessions, 53 provided sufficient data to support a clinical diagnosis, including cases of obstructive sleep apnoea, central sleep apnoea, and periodic limb
movement disorders (Russo et al.,
studies demonstrating clinical utility of home-based assessments for narcolepsy
(Blackwell et al., 2017) and epileptic encephalopathy diagnosis (Nagyova et al.,
2019; Brunnhuber et al., 2020). In contrast to previous paediatric PSG feasibility
studies
children
no
their typically developing peers. This generalisability is particularly relevant because
in children with neurodevelopmental conditions, sleep disturbances may reflect not
only primary sleep disorders, but also the effect of sensory states, such as discomfort,
pain, or interoceptive imbalance (Onen et al.,
Brindl
importan
(Goodwin et al.,
in Russo et al. (2021) also had a prior neurodevelopmental diagnosis, with
significant difference in PSG success rates observed between these children and
e, 2025), which are more likely to be representatively captured at home. The
ce of sensory states in this population is addressed in the following section.
2001; Marcus et al., 2014), a high proportion (39%) of
1966), with meta-analysis demonstrating that
2021). This supplements previous feasibility
2005; Reid et al., 2023; Bynum &
374 J. Mavi and K. Whitehead
27.3.3 Sensory States Monitoring in Children
and Adolescents
Children with neurodevelopmental disorders or complex medical conditions often
experience states of discomfort that elude not only precise measurement, but even
basic classification (Cassidy et al., 2023). Chronic pain and interoceptive difficulties
are
among the most common and distressing symptoms in this population, yet are
frequently under-recognised, undertreated, or poorly characterised. This is largely
due to an inability to self-report experiences and the lack of truly sensitive and
specific indicators (Barney et al., 2020; Fehlings, 2017; Gomez-Suarez, 2016; Rice
al., 2017). These sensory states often co-occur, fluctuate over time, and manifest in
et
that are shaped by the child’s underlying neurological profile. Following the
ways
same format as we did for sleep in Sects. 27.3.1 and 27.3.2, we first describe sensory
states
monitoring overall (Sect. 27.3.3), and then focus on naturalistic approaches
(Sect. 27.3.4).
Chronic Pain States
EEG has been used to study paediatric pain (Busse et al., 2025), but pain in children
with
complex needs is often chronic as described above. In such contexts, EEG
signatures of pain are harder to study, as it may be difficult to identify the onset and
offset, and to what degree any EEG features relate to the underlying aetiology of the
pain (e.g., CP) rather than the pain itself. However, carefully controlled juvenile
mammalian models of chronic pain have identified a causal relationship between
pain and unique electrographic characteristics. These include decreased coherence
between thalamus and primary somatosensory cortical activity in the 2–30 Hz range
(LeBlanc et al.,
al., 2016), measured via intracranial electrophysiology.
et
2014), and enhanced theta-gamma phase-amplitude coupling (Wang
The pre-clinical literature described above, then, supports the interpretation of
EEG
signatures of chronic pain states that have been reported in the human paediatric literature. For example, compared to healthy controls, adolescents with chronic
musculoskeletal pain showed higher delta and beta power at rest (Ocay et al.,
same study used hot and cold thermal stimuli to investigate EEG activity
The
following acutely painful events. The temperature of such stimuli (i.e., extreme
heat or cold) can be calibrated to induce acute thermal pain through the activation
of a subclass of peripheral C-fibres known as nociceptive C-fibres. These stimuli
showed again unique electrographic profiles between groups, including differences
in EEG spectral power, peak frequency, and permutation entropy (a measure of the
complexity and unpredictabi lity of ongoing activity). Interestingly, the EEG-derived
differences in thermal pain stimulus processing were not reflected in self-reported
pain intensity scores between patients and controls. This indicates that EEG may be
sensitive to neural processes underlying adolescent chronic musculoskeletal painrelated processing that are inaccessible through verbal report alone (Ocay et al.,
2022).
Furthermore, machine learning paradigms have been leveraged to success-
fully distinguish between rest and the onset of cold thermal pain in both adolescent
2022).
27 EEG Applications in Neonatal and Paediatric Clinical Neuroscience 375
chronic pain patients and controls, with theta-band permutation entropy features
being the largest contributor to machine learning model accuracy (Teel et al.,
These
results, in combination, highlight the potential capability of EEG to not only
detect chronic pain states, but also the onset of additional acute pain experience in
the absence of self-report.
Interoceptive States
Pain overlaps with interoception, defined as the brain’s representation of internal
bodily
(Erkin et al., 2010; Mannion et al., 2013). This overlap is highlighted by the shared
distrib
anterior cingulate, and somatosensory regions (Wager et al., 2013; Barrett &
Simmons
demon
neurodevelopmental conditions experience pain alongside difficulties with
interoception. Chronic constipation, gastro-oesophageal reflux, and irregular autonomic arousal are common but difficult to assess, especially in children who have
difficulties externally expressing their internal sensations (Barney et al., 2020).
(HEP)
2018). This, in addition to evidence from other functional imaging modalities
(Klabunde et al., 2019), suggests that EEG could quantify interoception and its
dif
ADHD, and other neurodevelopmental conditions exhibit altered resting-state connectivity and atypical cortical responses to visceral cues (Hechler,
2024). While direct EEG applications in paediatric populations are notably sparse,
prelimi
recognition task, there is a positive relationship between the amplitude of the HEP
and ADHD symptoms (Rapp et al.,
arousa
attentional weighting of task-irrelevant stimuli (i.e., one’s own heartbeat). Though
challenging to interpret, this early evidence supports the feasibility of EEG-based
interoceptive measures in younger age groups and highlights their potential application to more clinically complex and neurodevelopmentally atypical populations.
states, particularly related to the gastrointestinal and autonomic systems
uted networks that serve them involving (but not limited to) the insular cortex,
, 2015 ), with functional connectivity between these regions in adults
strated with EEG (García-Cordero et al., 2017). Many children with
To access/monitor interoceptive processing, the heartbeat-evoked potential
, an EEG-derived marker, has been validated in adolescents (Mai et al.,
ficulties in children. Indeed, there is growing evidence that children with autism,
2021; Yang et al.,
nary evidence in adolescents has shown that, during an emotional face
2023). Potential explanations include increased
l, atypical neural processing of internal bodily processes, and misaligned
2022).
27.3.4 Naturalistic Sensory States Monitoring in Children
and Adolescents
Naturalistic sensory states monitoring can be split into the stimuli themselves, and
the environment in which monitoring takes place. Addressing the stimuli first,
sensory states in children are generally studied using a mix of artificial
(e.g. thermal) and naturalistic endogenous stimuli (e.g. HEP). Overall, though,
376 J. Mavi and K. Whitehead
there is a trend towards greater utilisation of more naturalistic experimental paradigms in research. While still conducted in controlled settings, as with the neonatal
research cited above (Maitre et al.,
investigating the effects of meaningful sensory input (Maallo et al., 2022), such as
on
affective
touch and parent-child interaction that are so prevalent in children’s
2017), there has been growing importance placed
everyday lives, as opposed to more ‘manufactured’ stimuli. For instance,
neurotypical, CP, and autistic children all display elevated gamma power following
affective touch (slow, ‘caressing’ brush strokes) when compared to non-affective
touch (faster, more frequent brush strokes) (Sabater-Gárriz et al.,
strokes
occur at speeds to optimally engage C-tactile afferents—a subclass of
2025). Slow brush
C-fibres distinct to those described in the context of pain—which are associated
with the social and emotional components of tactile perception (McGlone et al.,
2014). Interestingly, affective touch was also correlated with better proprioceptive
accuracy
in CP patients, compared to non-affective touch. While the underlying
mechanisms are incompletely understood, the emotionally meaningful qualities of
affective touch may enhance attentional and sensory integration processes (Sacchetti
et al.,
impaired
2021), potentially supporting proprioceptive processes in children with
body awareness, such as those with CP. These results highlight the
potential utility of naturalistic paradigms to investigate more complex, sensoryaffective interactions children may have with carers.
Unlike sleep, even naturalistic sensory paradigms in children are typically
recorded exclusively in laboratory rather than community settings. However, the
infrastructure for this to extend into the home is building, which has been echoed
throughout the precedi ng sections. Feasibility of mobile and home EEG has been
demonstrated across not only healthy paediatric populations (Troller-Renfree et al.,
2021), but also in children who are neurodevelopmentally atypical (Milne-Ives et al.,
2023; Russo et al., 2021), arguably the populations who benefit most from such
technologies’ capabilities. Review of existing examples of mobile and home EEG
further highlights the potential of naturalistic EEG for sensory states monitoring. The
events depicted in Fig. 27.2 are not only environmental, but also complex, multimodal
sensory stimuli that may influence and modulate any states of pain and
discomfort. By leveraging early evidence of unique neural signatures of sensory
states, for example, via utilising automatic detection algor ithms which have shown
promise in adult EEG (Rockholt et al.,
clinicians may be able to continuously
2023),
monitor and better understand fluctuations of chronic pain or autonomic sensory
distress in everyday settings. Furthermore, adoption of home EEG in epilepsy
diagnostics demonstrates that such services are not only feasible, but ecologically,
economically, and logistically advantageous (Brunnhuber et al.,
lay
the groundwork for the integration of naturalistic EEG in other domains of
2023). These factors
paediatric care.
27 EEG Applications in Neonatal and Paediatric Clinical Neuroscience 377
27.4 Future Directions and Conclusion
Looking forward, the ongoing coalescence of numerous factors relevant to naturalistic EEG may be early indicators of an exciting paradigm shift in paediatric EEG.
One key driver is the rapid development of portable EEG devices that are less
obtrusive and restrictive, and better-tolerated. For example, the aforementioned
study distinguishing chronic pain patients from healthy children at rest and following
painful stimulation utilised a dry-electrode wearable EEG headset (Teel et al., 2022),
can be recording-ready in under five minutes. Wireless systems have also
which
been used in everyday environments (Wang et al., 2023) and in contexts relevant to
medic
ally complex populations, such as assessing preoperative anxiety in children
(Xu et al.,
In parallel, the longitudinal integration of neonatal and paediatric EEG may yield
further
point, but also to identify developmental risk and vulnerability across multiple stages
of childhood. For instance, reduced sleep spindle activity in infancy has been shown
to predict impaired motor function and CP diagnosis at pre-school age (Berja et al.,
2024). Similarly, across adolescence, increased cortical arousability during sleep in a
paedia
autonomic imbalance at follow-up seven years later (Rahawi et al., 2025). Together,
these findings highlight the utility of EEG-derived biomarkers in tracking developmental trajectories at the intersections of sleep, sensorimotor function, and autonomic activity, and the potential to guide supportive intervention strategies to
improve long-term outcomes.
Here we have explored how EEG can be used to examine sleep and sensory
processing across early development, beginning in the neonatal period and
extending through to childhood and adolescence. In infants, EEG combined with
physiological signals such as heart rate, respiration, and muscle activity enable
detailed investigation of emerging brain–body interactions. Naturalistic paradigms,
including feeding and self-generated movement, offer insight into how meaningful
sensory experiences are represented in the cortex from early life. In older children,
particularly those with neurodevelopmental conditions or complex medical needs,
EEG continues to provi de valuable information. When used in everyday settings,
EEG allows for the assessment of sleep architecture in ways that are both feasible
and ecologically valid. Preliminary findings also suggest that EEG possesses the
capability to explore less accessible domains such as chronic pain and interoceptive
function, highlighting promising directions for future research.
While often
research and clinical practice, both neonatal and paediatric EEG research are following common trends of shifts towards ecologically valid approaches. Together,
these two strands illustrate a developmental continuum in which EEG serves not
only as a tool for observing brain activity crudely/on the whole, but as a method for
probing, investigating, and ultimately understanding how neural functions shape,
and are shaped by, physiological states, sleep, and sensory experiences over the
course of early life.
2023).
insights. EEG can be used not only for direct observation at a single time
tric cohort (median age ¼ 9), was found to be a significant predictor of cardiac
considered as distinctly separate populations in neuroscience