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2 What Is EEG? 21
Fig. 2.3 Sample EEG of
single trials during visual
stimulation with a
checkerboard pattern
reversal at 2 Hz frequency.
The corresponding ERP
from the average of all trials
is shown at the bottom,
known in this case as the
visually evoked potential
(VEP), with its components
N75, P100, and N145. The
VEP is also shown overlaid
on the single trials to
highlight how each trial
contains the ERP signal
mixed with noise. The
averaging process reduces
the randomly fluctuating
noise in the trials, while
retaining the more
consistent activity related to
the brain’s response to form
the ERP. Modulations in the
amplitude and latency of the
ERP components can be
used for research or clinical
purposes
component. The ERP components seemingly reflect putative stages of cognitive
processing by the brain, elicited by either external stimuli or internal events such as
object recognition or decision-making (Luck,
2014). Changes in the latency or
amplitude of these components can be used for research (e.g., induced by experimental conditions) or clinical purposes (e.g., induced by clinical conditions).
Interestingly, the first formally classified ERP component was the contingent
negative variation (CNV; Luck, 2014), which happens before stimulus presentation.
More precisely, the CNV shows as a slow-going negative drift that appears for a few
seconds when a participant is expecting a stimulus to happen following a warning
signal (Walter et al., 1964). A similar component is the “Readiness Potential,” where
the preparation toward the execution of a hand movement is preceded for a few
seconds by a slow negative drift in the EEG , more pronounced on the side of the
scalp opposite to the moving hand (Kornhuber & Deecke,
2016). Both these
22 F. Cross Villasana
components show that the brain can modulate its state in anticipation of expected
events or planned actions.
The ERP components that follow stimulus presentation are much faster and
smaller than the CNV or Readiness Potential, corresponding to the fast pace of
perception and cognition. As ERPs come wrapped within ongoing EEG activity and
noise, these smaller components are much harder to observe and, for a time, were
less studied. Nonetheless, with the development of techniques to average across
trials, it became possible to isolate poststimulus ERPs more clearly. The idea is
exemplified in Fig.
related
activity has a largely consistent pattern; therefore, by averaging across trials,
the noise cancels out while the event-related activity persists in a way that the ERP
can be isolated. The advent of digital computers further facilitated trial averaging and
analysis of the averaged signal, leading to great leaps in ERP research and the
discovery of numerous components related to sensory perception, stimulus
processing, and cognitive processes. With these tools available, ERPs became the
ideal partner for mental chronometry methods and became a workhorse in the field of
cognitive psychology (Linden,
between these two disciplines has, on one hand, provided a window into mental
processes and, on the other, increased the understanding of what the ERP components represent in the brain and cognition.
The list of known ERP components is too long to mention here. More information
be found in Chap. 19 (“Event-Related Potentials”). Furthermore, various sum-
can
maries
2009). To mention an illustrative example for now, the mismatch negativity (MMN)
appears
(Luck, 2014), which reflects enhanced processing requirements for novel stimuli in
the
coma (Morlet & Fischer, 2014). This not only shows that the initial detection of
novelt
tool in coma cases (Pruvost-Robieux et al.,
allowed
auditory pathways using the sequence of components in the Auditory Brainstem
Response (ABR; Stapells & Oates,
on
of the components are also available in the literature (e.g., Sur & Sinha,
in relation to an auditory tone that differs from a stream of preceding tones
brain. Most strikingly, the MMN can be observed in unconscious patients in
y precedes conscious perception, but also turns the MMN into a prognostic
the use of ERPs in clinical settings, for example, to assess the integrity of
the components in Visually Evoked Potentials (VEP; Leocani et al., 2018).
2.3: Within each trial, noise fluctuates randomly, while the event-
2007; Meyer et al., 1988). The mutual feedback
2022). Technological advance s have also
1997), or the integrity of nerve conduction based
2.2.3 Frequency Analyses and the Brain’s Rhythms
Among the ERP components, there is a special case that connects back to the
ongoing brain waves first observed in EEG. This is the steady state visually evoked
potential (SSVEP). In the SSVEP, the presentation of a visual stimulus with flickering frequencies elicits an oscillation over visual areas with the same frequency as
that of the flicker (Norcia et al., 2015). This ongoing waveform is similar to the alpha
ory activity first observed by Berger. However, the SSVEP represents an
oscillat
2 What Is EEG? 23
adaptation of the brain to the flicker, rather than a naturally occurring rhyth m. The
SSVEP can be subjected to frequency analysis using the Fast Fourier Transform
(FFT) or similar methods to quantify frequency modulations produced by the flicker
on the EEG. Based on this frequency information, the SSVEP can be used for
researching perception, attention, or brain dynamics (Norcia et al.,
ping practical applications such as brain– computer interfaces (Mu et al.,
develo
Norcia
et al., 2015).
2015) or for
2024;
The use of the FFT and other frequency decomposition methods has also allowed
a deeper study of the brain’s natural oscillations, such as the aforementioned alpha
rhythm. The FFT is explained in great er detail in Chap. 18 (“Introduction of EEG
Oscilla
tions and Spectral Analysis”). For now, it suffices to mention that the FFT is
able to decompose the EEG signal into various constituent frequencies, allowing the
quantification of oscillations such as alpha waves, as well as other oscillations that
are not necessarily visible to the naked eye. More advanced time-frequency analysis
techniques are further capable of tracking the evolution of oscillations over time,
either over long periods or around discrete events such as stimuli presentation or
motor actions. This has vastly expanded the context in which oscillatory activity is
observed and analyzed. Over time, canonical EEG frequency bands were defined
(Ahmed & Cash,
–30 Hz), gamma (>30 Hz), delta (1–4 Hz), and theta (4–8 Hz). Examples of
(~12
2013; Arjoonsingh et al., 2024): alpha (8–12 Hz), beta
these band oscillations can be seen in Fig. 2.4. However, the definition of the
freque
ncy bands may vary slightly between authors. Furthermore, bands can vary
between participants (e.g. Klimesch, 1999) so that, for example, one person’s alpha
range
may be 7.5–12 Hz, while another shows a range of 8–13 Hz. On a similar note,
the frequency of oscillations varies through the life span, so that, for example, the
resting EEG of young children shows oscillations in the range of adult theta, which
gradually mature with age to match the frequency of adult alpha (Klimesch, 1999).
Additionally, oscil
lations in the same band can appear in very different contexts. As
an example, oscillations in the 4–8 Hz range are enhanced during concentration, but
are also prominent during drowsiness, yet both cases are recognized as “theta”
despite possibly reflecting different brain processes (Snipes et al.,
reason
s, some authors give less priority to the canonical band classifications, pre-
2022). For such
ferring to report the precise frequencies that they observed in their studies
(e.g. Steriade,
Ibarra-Lecue et al., 2022; Sterman, 1996).
(e.g.,
2000), or name the rhythms according to the context of observation
Much knowledge has been gained about brain oscillations; however, despite
these observations, there is no definitive account of the kind of building blocks
they represent in the whole process of neurocognition, and research is still ongoing
(e.g. Beste et al.,
of quiet wakefulness, and alpha levels decrease with task engagement. As
state
2023). As a broad summary, the waking alpha band is related to a
vigilance fades or tiredness sets in, alpha gradually slows in frequency and enhances
its amplitude (Klimesch,
1999).
In contrast, transient enhancements in alpha seem
related to active inhibition over specific brain areas that are not relevant for particular
tasks (Jensen & Mazaheri,
incre
ases the brain becomes less reactive to stimuli (Taylor & Thut, 2012).
2010; Klimesch et al., 2007). Overall, as alpha amplitude
24 F. Cross Villasana
Fig. 2.4 Filtered EEG
signals in the range of the
different canonical bands
organized in descending
order. The exact band range
can differ between authors
Oscillations in the alpha range may receive different names according to their
location in the brain. So that names like “mu,” “Rolandic,” “wicket” (Niedermeyer,
1997), or “sensorimotor rhythm” (SMR; Sterman, 1996) are used for sensorimotor
areas, while “tau” or “third rhythm” are used for mid-temporal auditory areas
(Niedermeyer, 1997). The Beta band has been mainly observed in contexts of
motor control (Pfurtscheller & Lopes da Silva,
1999), but its exact function is still
unclear. Beta bursts have been proposed as a functional inhibition mechanism
relevant during cognition, motor planning, and motor control (Lundqvist et al.,
2024; Zich et al., 2025). Beta is also observed in working memory retrieval, and is
proposed as a mechanism related to the replay of task-relevant memory contents
(Ibarra-Lecue et al., 2022; Spitzer & Haegens, 2017). The gamma band is mostly
observed over areas involved in active processing over the cortex, such as visual
processing or memory formation (Ibarra-Lecue et al., 2022). The theta band is also
related to cognitive activity, especially over central regions when conscious cognitive control is deployed (Cavanagh & Frank,
2014). Similarly, central theta is used in
the assessment of cognitive workload (Chikhi et al., 2022). It has been proposed that
theta facilitates the communication between different brain regions during cognitive
performance (Cavanagh & Frank, 2014). Generalized Theta is enhanced during
fatigue and sleep pressure (Snipes et al., 2022; Tran et al., 2020). During sleep,
theta is thought to play a role in memory consolidation (Diekelmann & Born,
2010).
2 What Is EEG? 25
The Delta band is classically related to deep sleep, thought to play a role in brain
restoration and plasticity (Assenza & Di Lazzaro,
role
during wakefulness is still being investigated. It has been proposed that it is
2015; Hao et al., 2023). Delta’s
involved in motivational and self-regulatory processes (Knyazev, 2012). Patients
brain injuries show persistently elevated delta levels, especially during the early
with
stages following injury, which has shown prognostic value for clinicians (Franke
et al.,
2023; S
u
tcliffe et al., 2022).
Final
ly,
in
recent
years,
aperiodic
an
compo
nent
has gained increased attention as a modulator of brain activity alongside oscillations
(Donoghue et al.,
2020) whose exact role is still being elucidated (Brake et al.,
2024).
Despite the accumulated observations and the available models of the various
EEG oscillatory bands so far, it is important to stay updated with the latest research at
the psychological and physiological levels. Research on brain oscillations evolves
continuously in the quest for understanding the mecha nisms that generate them and
their functions in neurocognitive processes.
2.3 Concluding Summary
The electrical trace s of brain activity recorded in EEG are a rich source of information on the brain’ s processes. The EEG has made important contributions to our
understanding of the brain, about cognitive processes, and brings valuable information to clinical settings. Here we presented an overview of the physiological origins
of EEG, the diverse signals that can be derived from it, and the insights they offer
about the brain. Having an informed understanding of the different EEG signals can
enrich their interpretati on and facilitate the generation of new hypotheses for science
or applied fields. Many more applications and analysis possibilities exist, and
research on the physiology of the EEG, its physical properties, and use of its signals
continues to evolve. Further chapters in this book offer a closer look at the various
aspects of the acquisition, analysis, and interpretation of EEG.
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Chapter 3
Basic Anatomy: Central Nervous System
Shivakumar Viswanathan
Abstract An integrated understanding of brain function requires both neurophys-
and neuroanatomy. Electroencephalography (EEG) is an important measure
iology
of neurophysiology. However, linking EEG measurement to neuroanatomy to
understand nervous system function is often viewed as challenging and complex.
In this chapter, we provide an overview of the benefits of including a neuroanatomical perspective in your EEG research. Additionally, we briefly survey relevant tools
and resources to understand brain neuroanatomy using digital brain atlases. These
tools can help researchers become familiar with neuroanatomy, the brain’s role
within the broader nervous system, and stimulate possible applications to their
EEG research.
Keywords EEG · Central nervous system · Neur
oanatomy · Brain atlas · 10–20
system
3.1 Introduction
Electroencephalography (EEG) is a classical measure related to neurophysiology,
namely, the functioning and activity of the nervous system. However, linking
measured EEG to its physiological relevance requires us to also consider
neuroanatomy—the structure of the nervous system. Broadly speaking, neuroanat-
omy is a description of the nervous system’s structure ranging from its macro-level
structure down to its micro-level cellular organization. This description of what the
various structures are (i.e., neuroanatomy) provides the foundation to understand
what these structures do (i.e., neurophysiology).
Including neuroanatomical considerations is crucial for EEG studies. However, it
can
seem unfamiliar and of uncertain value for researchers who are beginning their
EEG journey, especially without a background in neuroscience, medicine, or
S. Viswanathan (✉)
Brain Products GmbH, Gilching, Germany
e-mail:
shivakumar.viswanathan@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_3
31