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10 T. Warbrick
Availability/Accessibility The benefits of EEG are its low cost and potential for
widespread use. However, despite research funding, international collaboration, and
technical developments, a vast majority of people don’t benefit from neuroscience
and technology breakthroughs, including EEG (Bringas-Vega et al.,
s include the concentration of efforts in highly developed countries, the
reason
2022). Possible
marginalisation of low- and mid-income countries due to inadequate research infrastructure, and insufficient links between research and public health needs. These
issues were determined at the World Health Organization (WHO) headquarters in
June 2016 in a meeting of representatives of the US BRAIN project, the European
Human Brain Project, the Japan Brain/MINDS, and the Chinese, Australian, and
Cuban Brain Projects (Bringas-Vega et al.,
metho
d, would be well-suited to solving these problems, yet the problem persists.
2022). EEG, as a low-cost, scalable
Addressing these problems should be a priority for the EEG community.
Diversity Most study participants, especially in psychology, are WEIRD meaning:
Western,
This
Educated, Industrialised, Rich, and Democratic (Henrich et al., 2010) .
introduces a natural sampling bias in many studies conducted in universities
and research institutes, where the WEIRD population is overrepresented. Besides a
general sampling bias, there is a phenotypical bias in neurotechnology. For exa mple,
the exclusion of phenotypes such as skin pigmentation and hair type (Webb et al.,
2022). Consequently, marginalised groups are underrepresented in the body of
scien
tific knowledge acquired using EEG. Addressing these issues is the responsibility of researchers, institutional review boards (IRB), fundin g agencies, and manufacturers, and we must all work together to correct these biases.
Replicability Replicability is the cornerstone of good science and is vital to vali-
findings that link brain activity and cognitive function. This depends on well-
dating
defined and standardised data acquisition and analysis pipelines. However, there is
limited evidence for replicability in EEG research, and this is a symptom of a wider
problem in science (Baker,
published because novel findings are prioritised. For example, there are more
rarely
2016). A contributing factor is that replication studies are
than 6000 EEG publications per year, few of which are replication studies (Pavlov
et al.,
2021).
The #EEGmanylabs (Pavlov et al., 2021) initiative aims to address this by
replicati
ng highly influential EEG studies across multiple labs worldwide. While
findings from this study are not yet published, it’s a step towards replicable,
transparent EEG research.
Another reason to be optimistic is that some journals, such as Aperture Neuro,
explic
itly include replication studies, registered reports, and data papers within their
scope. This emphasis on publishing material that supports open science is reassuring
for the future of neuroscience research. This book encourages the good scientific
practice that underpins transparent and reproducible research, from managing your
lab to conducting your studies and reporting your research.
Ethics We could probably write a whole book on research ethics in EEG. Beyond
the
standard ethical requirement of informed consent and data protection, emerging
1 EEG in Context: Past, Present, and Future 11
technologies present new challenges, for example privacy in open data initiatives,
self-diagnosis using consumer grade devices, and ethical implications of
neuroenhancement. Each of these challenges will require careful attention as the
field continues to evolve.
1.5 Conclusions
EEG is an effective tool for exploring the relationship between brain and behaviour.
In the past 100 years, it has advanced our understanding of brain function, and
EEG’s future is promising as a standalone measure or in combination with other
neuroimaging methods and emerging technologies. However, the future of our
discipline ultimately depends on the choices we make. Mushtaq et al. (
de their paper with a call to action to ‘commit to robust, ethical, inclusive,
conclu
2024)
and sustainable practices’. We encourage anyone using this book as a starting point
for EEG research to keep this in mind, an d we aim to provide you with a strong
foundation for working towards this goal.
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Chapter 2
What Is EEG?
Fernando Cross Villasana
Abstract Since the initial observations by Hans Berger in the 1920s, electroen-
ography (EEG) has opened a window into studying otherwise unobservable
cephal
phenomena of the brain. But how do these electrical traces come to be and moreover,
what do they mean? In this chapter, we go through the physiological origins of the
EEG trace recorded from the scalp, showcase the different signals that are commonly
derived from it, and explore how they are used in different contexts. From sleep
stages to cognitive processes, epilepsy or psychiatric conditions, the EEG has been a
rich source of information on the state of the brain. Understanding these dynamics
can enrich the interpretation of EEG observations, fuel new ideas, and facilitate
communication with other neuroscientists.
Keywords EEG · ERP · Brain oscillation
· EPSP · IPSP
2.1 Physiological Origins of the EEG
In EEG, the voltage recorded by each electrode from the scalp is mainly the result of
synchronized activity from hundred s of thousands of neurons from the cortical sheet
of the brain. For the most part, this activity is related to postsynaptic potentials
(PSPs) from the apical dendrites of pyramidal neurons in the cortex. PSPs produce
extracellular charges whose electric fields can travel through tissue and reach the
electrodes on the scalp (Beniczky & Schomer,
PSPs are generated after a postsynaptic neuron receives excitatory or inhibitory
stimulation from a presynaptic neuron. Correspondingly, PSPs can be excitatory
(EPSP) or inhibitory (IPSP). In the case of EPSPs, a neuron receives excitatory
neurotransmitters at the synapse, mostly into the distal side of the dendrite (Beniczky
& Schomer,
This
F. Cross Villasana (*)
Brain Products GmbH, Gilching, Germany
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026
Warbrick (ed.), The EEG Handbook,
T.
https://doi.org/10.1007/978-3-032-20450-9_2
2020), but can also be close to or at the soma (Jackson & Bolger, 2014).
induces postsynaptic ion channels on the membrane to allow the influx of
2020; Jackson & Bolger, 2014).
15
16 F. Cross Villasana
Fig. 2.1 Representation of the generation of EEG signal during EPSP. (a) A pyramidal neuron
receives excitatory neurotransmitters at synapses on different sites of the apical dendrite’s tuft. This
induces a positive charge inside the neuron due to the entrance of positive sodium ions. The positive
charge is propagated through the apical dendrite towards the negatively charged soma of the neuron.
(b) Negative charges are produced in the extracellular space close to the synapses. The negatively
charged region forms a dipole with the positive areas along the exterior of the neuron. (c) The
dipoles from multiple neurons aligned in parallel summate to act as a larger dipole that can be
detected on the scalp
positively charged sodium ions. Ion influx simultaneously generates a positive
charge inside the neuron and a negative charge outside. The intracellular positive
charge produces a differential with the negatively charged inside of the neuron, so
the charge propagates towards the soma (Fig.
2.1a). Multiple EPSPs can summate at
the soma to depolarize the neuron and facilitate that it generates its own action
potential (Olah et al.,
2025; Stuart & Sakmann, 1995
), but this latter part is not
normally reflected in the EEG. The extracellular negative charge, together with the
relatively more positive areas along the apical dendrite, forms a dipole, that is, a
negatively charged area called “sink,” separated from a positively charged area
called “source,” creating an electric field (Fig.
2.1b). A single extracellular dipole
2 What Is EEG? 17
is too small to be detected by EEG. To generate a strong enough electric field, the
synchronized EPSPs of at least hundreds of thousands of neurons arranged in
parallel to each other are necessary (Fig.
r dipole whose electric field is detectable by EEG electrodes (Beniczky &
large
2.1c). The summated dipoles act as a single
Schomer, 2020; Cohen, 2017; Jackson & Bolger, 2014).
In the case of IPSPs, the process is similar to EPSP but with opposite polarities
(Beniczky & Schomer, 2020), and with the site of stimulation being mostly at the
proximity of the soma (Beniczky & Schomer, 2020). After receiving inhibitory
neurotransmitters from a presynaptic neuron, ion channels in the postsynaptic
neuron let negative chloride ions inside, creat ing an extracellular positive charge.
The intracellular negative charge travels through the dendrite towards the soma,
facilitating the polarization of the neuron and making an action potential less likely
(Fricker & Miles, 2000). Meanwhile, the extracellular positive charge (source) forms
dipole with the relatively more negative region outside the neuron (sink), and the
a
summation of multiple dipoles from neurons acting in synchrony can be detectable
by EEG. EPSPs and IPSPs are in constant interplay in the neurons to regulate the
brain’s activity. However, the positive or negative polarity seen in EEG activity does
not necessaril y correspond to inhibitory or excitatory activity in the brain (Jackson &
Bolger,
location
2014; Luck, 2014). The reason for this is that on top of the kind of PSP, the
of the PSP in the neuron affects the orientation of the extracellular dipole. In
this way, an EPSP generated at a distal site of the dendrite produces the same source
and sink orientation as an IPSP near the soma. Likewise, an IPSP at the distal
dendrite leads to a similar dipole orientation than that from an EPSP at the soma
(Beniczky & Schomer, 2020; Jackson & Bolg er, 2014).
The orientation of the columns of neurons due to cortical folding also affects the
orientation of the dipoles they generate and as a result affects the polarity recorded at
the scalp (Beniczky & Schomer, 2020; Jackson & Bolger, 2014; Olejniczak, 2006).
Radial
dipoles are mainly generated over cortical gyri (Fig. 2.2) in a way that
is perpendicular to the head surface (Olejniczak, 2006; Scherg et al., 2019). For
radia
l dipoles, the recorded polarity is that of the pole facing toward the scalp. The
floor of the sulci also produces radial dipoles, but they are deeper in the brain and are
not normally detectable using scalp EEG. Tangential dipoles are generated within
the walls of sulci and are parallel to the surface above (Fig.
d by EEG as long as they are not neutralized by another dipole from the
detecte
opposing wall (Nunez & Srinivasan,
of
the dipole will detect the corresponding positive or negative pole (Olejniczak,
2006). In this case, electrodes on opposite sides
2.2). They can be
2006; Scherg et al., 2019). Of note, while EEG is sensitive to the electric fields from
radia
l and tangential dipoles, the related magnetoencephalogram (MEG) technique is
only sensitive to the magnetic fields from tangential dipoles (Jackson & Bolger,
2014; Nunez & Srinivasan, 2006).
For the
electrical signal to reach the scalp electrodes, it must go through the
extracellular medium and traverse through tissue in a process called volume conduction (Beniczky & Schomer,
happens
almost instantly as the electric field influences the ions in the medium
2020; Jackson & Bolger, 2014). This conduction
(Luck, 2014). At the point of the meninges, skull and scalp, they act as insulating
18 F. Cross Villasana
Fig. 2.2 Representation of dipoles produced in the cortex shown as double-headed arrows and their
orientation with respect to the surface. (a) Radial dipoles produced in a gyrus are able to reach the
surface, and either their positive or negative side is recorded in EEG. (b) Radial dipoles generated
within the floor of a sulcus are deep and not normally detectable with scalp EEG. (c) Tangential
dipoles produced at the walls of a sulcus that are faced with a dipole from the opposing wall cancel
out and cannot be recorded by EEG. (d) A tangential dipole with the appropriate orientation that is
not opposed by another dipole is able to reach the surface; the two poles would appear on opposite
sides of the scalp
layers so that the electric signal further disseminates through capacitive conduction
(Jackson & Bolger, 2014). In this way, when a dipole presses charged ions towards
the outside of the layer, ions inside the layer will realign themselves with positive
and negative charges in the same d irection as the dipole. This process repeats itself
across each layer of the meninges, skull, and scalp, until reaching the surface. At the
surface, electroconductive gel is used to create a bridge between the scalp and the
electrode. The gel and the electrode act as further capacitive layers until t
reaches the cable and travels to the amplifier (Jackson & Bolger,
he charge
2014). The
processes of volume and capacitive conduction smear and attenuate the signal
(Jackson & Bolger,
2014; Nielsen et al., 2023) and, anatomical differences between
individuals (e.g. skull and scalp thickness, cerebrospinal fluid) have differential
effects on the recording (e.g. Klimesch,
1999; Nielsen et al., 2023; Wendel et al.,
2010).
While PSPs have the largest influence on EEG, other types of brain activity are
known to contribute to the EEG as well (Cohen, 2017; Olejniczak, 2006), but these
are restricted to certain contexts and are less studied, for example, action potentials.
Although they are not usually visible in EEG (Beniczky & Schomer, 2020), action
potentials are thought to contribute to the waveforms in auditorily-evoked responses
(Chertoff et al., 2010; Møller et al., 1995), and in response to somatosensory stimuli
(Luck, 2014). Signals from deep sources of the brain are rare in EEG, and are studied
little. Technically, to be detectable, the deeper source should produce a large enough
dipole that can reach the surface, but it would still appear as a weak signal.
2 What Is EEG? 19
Investigating the possibility of recording these signals and their interpretation is still
ongoing work. Special methodologies are necessary to disentangle the signal of deep
sources from other brain activity and noise. In these studies, EEG is frequently
accompanied by invasive intracranial sensors that were implanted in patients for
clinical purposes (e.g., Fahimi Hnazaee et al.,
2020).
2.2 Signals of the EEG
Understanding the origins of the EEG in the brain can explain a great deal about how
neuronal activity can reach electrodes on the scalp. However, once the brain activity
enters the EEG record, it shows a variety of patterns and dynamics that this model
can only partially account for (Cohen,
emerging signals in the EEG that reflect the different states and processes of the
brain. These signals can be detected through different means, ranging from direct
observation to advanced computational methods. But how do we understand their
meaning? Over the decades, many EEG signals have been studied in relation to
behavioral and neurophysiological measurements in humans and animals to clarify
their significance. This work has greatly advanced the understanding of brain
dynamics and generated diverse practical applications. But the knowledge is still
far from complete, and research on the various EEG signals evolves continuously.
With that in mind, when working with a particular signal from the EEG, it is helpful
to have an overall understanding of its meaning, consider the context of the observation, and stay updated with research on that signal. The following section presents
an overview of the most commonly studied EEG signals.
2017). Such patterns can be considered as
2.2.1 Direct Observation of EEG: Identifying States
of the Brain
Since the early days of electroencephalography, direct observation of brain waves
has shown that different behavioral states correspond to different wave patterns,
suggesting different processes in the brain. The classic observation where eye
closure led to the appearance of large oscillations termed “alpha,” with 8–12 cycles
per second in the visual brain areas (Britton et al.,
flect a state of diminished activation in the visual cortex. This is in contrast to the
re
pattern of mostly faster waves that is prevalent when the eyes are open (Britton et al.,
2016b). More recent research combining EEG with functional magnetic resonance
imaging
with a decrease in cortical neural activity (Murta et al., 2015). This notion is further
reinforced
cortex becomes less reactive to the magnetic stimuli when alpha amplitude is higher
(Taylor & Thut,
has shown that indeed, increments in the amplitude of alpha waves correlate
when EEG is paired with transcranial magnetic stimulation, and the
2012). However, it is important to notice that the relationship
2016b), suggests that these waves
20 F. Cross Villasana
between alpha and subcortical regions or brain networks is more complex (Murta
et al.,
2015).
After characterizing normal brain activity, it did not take long to find abnormal
discharges in patients with epilepsy around seizure episodes. This is called ictal
activity and can show various patterns of spikes and waves with high amplitude
(Britton et al., 2016a; Bromfield et al., 2006). Furthermore, constant monitoring of
patients’ EEG reveals that they also show abnormal spikes and waves outside of
the
seizure episodes, or interictal abnormalities, which provide valuable clinical information (Britton et a l., 2016a). These types of abnormal EEG suggest episodes of
over-s
ynchronization and overspreading of activity in the neurons, which result from
disruptions in the neuronal excitation and inhibition mechanisms (Bromfield et al.,
2006). The scalp location of the ictal and interictal discharges can be used as a good
approxi
2020; Scherg et al., 2019). In the same way, the relation of EEG observations to
concom
patients with epilepsy (Britton et al., 2016a).
that
Kleitman, 2003). This appears in the EEG as two initial stages of gradual EEG
slowin
ment (REM) phase where the EEG resembles the waking state (Britton et al.,
These
populations across the lifespan (Luca et al.,
sleep
Born,
brain
trained professionals in clinical settings for monitoring and diagnostic processes, and
in research. Accumulated knowledge and the assistance of computational analyses of
ongoing EEG have further refined these techniques (Aboalayon et al.,
mation of the foci of epileptic activity in the brain (Beniczky & Schomer,
itant behavior provides useful information for the diagnosis and treatment of
The discovery of sleep stages using EEG showed how sleep is not uniform and
the brain engages in different processes through the night (Aserinsky &
g, a slow-wave sleep stage with high-amplitude EEG, and a rapid-eye-move-
2016b).
stages allow the identification of sleep patterns in healthy and clinical
2015). Further research has linked
stages to learning and memory consolidation processes (Diekelmann &
2010). Overall, the direct observation of the EEG is informative about the
’s ongoing state and its alterations. It is still a valuable tool used today by
2016).
2.2.2 Event-Related Potentials and the Brain’s Reactions
to Stimuli
After observing that EEG fluctuations reflect persistent states of the brain, the next
natural step was to investigate how particular stimuli affected the EEG trace under
different circumstances. This led to the eventual observation of event-related potentials (ERPs) as responses in the EEG that surrounded the presentation of sensory
stimuli (Woodman,
positive
ERP waveform is presented in Fig. 2.3. Sometimes, ERPs can be seen directly on the
EEG,
preprocessing of the signal. Each deflection within the ERP waveform is known as a
and negative deflections that follow a particular sequence. An example
but they are more often imperceptible through direct observation and require
2010). ERPs are wave forms composed of a succession of