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Editor and Contributors xlv
Louis Lemieux UCL Queen Square Institute of Neurology, University College
London, London, UK
Yoshua E. Lima-Carmona Department of Electrical and Computer Engineering,
Univers
ity of Houston, Houston, TX, USA
Marie Loescher Laboratoire de Neurosciences Cognitives et Computationnelles,
Dépa
rtement d’Etudes Cognitives, École Normale Supérieure, Université PSL,
INSERM U960, Paris, France
Yin Fen Low Brain Products GmbH, Gilching, Germany
Steven J. Luck Center for Mind & Brain and Department of Psychology, Univer-
sity of California, Davis, Davis, CA, USA
Ramon Martinez-Cancino Brain Products GmbH, Gilching, Germany
Jayvian Mavi Digital Health and Applied Technology Assessment (DHATA),
’s College London, London, UK
King
Rebecca Meagher School of Biomedical Engineering and Imaging Sciences,
King
’s College London, London, UK
Alejandro Ojeda Brain Vision LLC, Garner, NC, USA
Maxine Annel Pacheco-Ramírez Department of Electrical and Computer Engi-
ng, University of Houston, Houston, TX, USA
neeri
Ignacio Rebollo Department of Gut Brain Interactions, German Institute of Human
Nutrition,
Nuthetal, Germany
Mario Rosanova Department of Biomedical and Clinical Sciences, University of
Milan, Italy
Milan,
Anna Sadilova School of Biomedical Engineering and Imaging Sciences, King’s
e London, London, UK
Colleg
Lianne Sanchez-Rodriguez Department of Electrical and Computer Engineering,
Univers
ity of Houston, Houston, TX, USA
Boyuan Song UCL Queen Square Institute of Neurology, University College
London, London, UK
Heiko I. Stecher Experimental Psychology Lab, Department of Psychology, Carl-
ssietzky Universität, Oldenburg, Germany
von-O
Daniel Strüber Experimental Psychology Lab, Department of Psychology, Carlvon-O
ssietzky Universität, Oldenburg, Germany
Masako Tamaki RIKEN Center for Brain Science, Wako, Japan
Shivakumar
Viswanathan Brain Products GmbH, Gilching, Germany
xlvi Editor and Contributors
Sreekari Vogeti Experimental Psychology Lab, Department of Psychology, Carlvon-Ossietzky Universität, Oldenburg, Germany
Cluster for Excellence “Hearing for All”, Carl-von-Ossietzky Universität, Oldenburg, Germany
Tracy Warbrick Brain Products GmbH, Gilching, Germany
Kimberley Whitehead Digital Health and Applied Technology Assessment
TA), King’s College London, London, UK
(DHA
Thorsten
O. Zander Neuroadaptive Human-Computer Interaction, Brandenburg
University of Technology Cottbus-Senftenberg, Cottbus, Germany
Part I
Fundamentals of EEG
Chapter 1
EEG in Context: Past, Present, and Future
Tracy Warbrick
Abstract Our understanding of brain function depends on the measurement and
is techniques at our disposal. To fully understand the value of a technique, we
analys
need to consider its capabilities and limitations and be aware of its place in the wider
landscape of brain imaging. Electroencephalography (EEG) offers a way to observe
brain activity in near real time and is widely applicable across many research and
clinical applications. It can be quantitative, objective, has good test–retest reliability,
and is a rich source of information. In this chapter, we consider how EEG developed
as a research tool, its place within the landscape of brain imaging, and the future
of EEG.
Keywords EEG history · Brain imaging
methods · EEG developments · Emerging
EEG applications · Good practice · Replication · Diversity · Accessibility
1.1 EEG Technology: Past to Present
Electroencephalography (EEG) has a long history, with 2024 marking 100 years
since the first EEG recordings. Its origi ns can be traced back to the late nineteenth
century, when scientists made progress in understanding the neurophysiology and
electrophysiology of the body. In the 1870s, Caton discovered that the brain also
produced fluctuating electrical signals. However, it wasn’t until decades later, in the
1920s, that Berger successfully recorded these signals, and we consider this the
origin of EEG research. Despite this encouraging start, EEG was not widely used
until the 1950s. For a comprehensive account of EEG’s development from its initial
discovery by Caton to its rise in the 1950s, see Niedermeyer (
Since the 1950s, we have seen many technological and theoretical advances.
Berger
’s first recordings were from a single bipolar channel using an analogue
system. In contrast, today’s technologies allow us to record from hundreds of
T. Warbrick (*)
Brain Products GmbH, Gilching, Germany
e-mail:
tracy.warbrick@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_1
2005).
3
4 T. Warbrick
channels digitally. The first EEG systems used delicate electrodes, a differential
amplifier for each channel, and a needle or pen to plot the signal on paper
(Niedermeyer,
e of data, and use variable acquisition parameters thanks to digital signal
volum
acquisition and amplifier development.
Modern EEG systems are smaller and more portable than early EEG devices,
allowing easier lab setup and enabling mobile EEG in more ecologically valid
settings. Electrode technology has also developed significantly. Electrodes are
more robust, and researchers have a range of electrode types, for example gel, saline,
passive, and active, and application methods, for example cap-mounted, headsetmounted, and glued, to choose from. Further information on amplifier and electrode
technology can be found in Chap.
logy’).
Physio
Analysis techniques have also advanced thanks to widespread access to computational
tion of a printed or digital trace, but we are now able to extract and quantify EEG
features using sophisticated signal processing techniques. Part IV of this book is
dedicated to EEG analysis and signal processing techniques. In addition, EEG can be
combined with other emerging technologies such as artificial intelligence (AI),
brain–computer interfaces (BCIs), and virtual reality (VR). EEG researchers are
adapting to the changing technological landscape, and EEG remains a valuable
method within the broader field of brain imaging and cognitive neuroscience.
The following sections will explore EEG and its relation to brain function, where
fits in the wider context of brain imaging methods, and possible future
EEG
directions for the field.
2005). Today, we can measure from many channels, record a larger
12 (‘Hardware for Recording EEG and Peripheral
resources and analysis methods. Early EEG research relied on visual inspec-
1.2 What Do We Know About the EEG Signal?
Recording EEG from scalp electrodes provides a sensitive measure of ongoing brain
activity. Neural activity generates electrical and magnetic fields, and when large
populations of neurons fire synchronously, we can measure the summed activation
as EEG at the scalp. These time-varying signals provide a window into the functional
state of the brain and are used to understand a myriad of functions such as perception, cognition, and arousal state. A detailed account of the physiological origins of
EEG is provided in Chap.
To measure and interpret EEG signals, we need to understand what we measure
and the associated limitations. Here, we focus on EEG’s strengths and weaknesses in
relation to other neuroimaging methods.
EEG is
temporal resolution in the order of milliseconds. This makes EEG uniquely suited
to measuring the rapidly changing temporal dynamics of brain activity. However,
while the temporal resolution of EEG is excellent, its spatial resolution is poor. In
other words, it’s difficult to determine the origin of the electrical potentials we
a direct measure of population-level neural activity and has a high
2 (‘What Is EEG?’).
1 EEG in Context: Past, Present, and Future 5
measure at the scalp. The signal is generated in the cortical surface and must pass
through multiple layers of tissue (cortex, skull, skin) to reach the scalp surface where
we place our EEG electrodes. Furthermore, EEG is limited to measuring large-scale
neuronal populations firing in synchrony. This means that the activity of smaller
populations and asynchronous activity are difficult to measure with scalp EEG
(Cohen,
but what EEG means. What drives changes in EEG featu res? Despite much research
linking the spectral, temporal, and spatial features of EEG to perceptual and cognitive processes and disease states, it’s not always possible to say how these signals
were generated by the underlying neural circuitry (Cohen,
level
neurons, how this changes over time, and its connectivity with other neurons.
Therefore, it’s difficult to interpret a specific EEG feature in terms of corresponding
features of neural activity.
neuros
plete, its functional significance is understood well enough to provide valuable
insights into electrophysiological brain dynamics. Acknowledging a technique’s
limitations doesn’t undermine its value. Critical evalua tion is necessary for its
appropriate use and for understanding its contribution to the field. The remainder
of this chapter considers EEG in the broader landscape of brain imaging methods
and what the future of EEG might look like.
2017).
We need to know how to interpret EEG signals—not only what EEG measures
2017). The macroscopic
of EEG doesn’t allow us to measure the electrical activity of individual
Despite these limitations, EEG remains a powerful tool in the field of cognitive
cience. While our understanding of the drivers of the EEG signal is incom-
1.3 EEG and Other Neuroscience Methods
Understanding EEG’s place in the wider context of brain imaging methods can help
us to appreciate its unique contribution to the field. As cognitive neuroscientists, our
goal is to understand how the brain works, what the measured signals mean, and how
they relate to behaviour. Ideally, we would need measurements at the level of
individual neurons and to track that over time. In human research this is not feasible,
but we do have multiple neuroimaging tools at our disposal: electrocorticogram
(ECoG), electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy
(fNIRs), and positron emission tomography (PET). To use these methods effectively, we need to know what neural events (or proxies thereof) each method can
detect and consider the relative spatial and temporal scales.
Each met
to our understanding of brain structure and function. We will consider what each
method measures, specifically in comparison to EEG, starting at the neuron level and
moving to measures of blood flow. Figure
resolu
hod has advantages and limitations and can make a unique contribution
1.1 shows the temporal and spatial
tion of common neuroimaging methods.
6 T. Warbrick
Fig. 1.1 Comparison of neuroimaging methods. (From Sejnowski et al. (2014). Reproduced with
permission)
Invasive Techniques Local field potentials (LFPs) and elect rocorticography
(ECoG) are both invasive techniques that measure electrical signals close to their
source. LFPs are an extracellular signal measured by microelectrodes implanted in
the brain tissue; they reflect the highly dynamic flow of information across neural
networks (Herreras, 2016). ECoG measures electrical activity using electrodes
placed
over the cortical surface. This provides a spatial resolution that lies somewhere between that of fully invasive (LFP) and non-invasive (EEG) neural signals
(Kim et al.,
the electrodes being closer to the source of the measured signal. Additionally, both
to
2015). Both LFPs and ECoG are less susceptible to noise than EEG, due
techniques have excellent temporal resolution. However, LFPs and ECoG require
surgically implanted electrodes. Therefore, in humans, their use is restricted to
patients who require surgery for clinical purposes.
Magnetoenc
ephalography (MEG) EEG and MEG are closely related and are
often discussed together, as both techniques record summations of electrical activity
elicited by postsynaptic potentials. While EEG measures the electrical potentials,
MEG measures the magnetic fields associated with the electrical activity. Both
techniques benefit from high temporal resolution, but the spatial resolution of
MEG is a little better because the magnetic fields measured by MEG are less affected
by tissue than electrical signals. However, MEG is limited to localising tangential
sources, and since magnetic fields decay quickly, MEG is also limited to recording
from the cortex.
1 EEG in Context: Past, Present, and Future 7
Traditional MEG systems use superconducting quantum interference devices
(SQUIDs), (Singh,
genic
cooling system. This makes traditional MEG more expensive than EEG and
2014) which require a magnetically shielded room and a cryo-
limits it to laboratory settings. Recently developed optically pumped magnetometers
(OPMs) offer several advantages over traditional SQUID MEG, such as increased
sensitivity and resolution, lifespan compl iance, free movement, and lower cost
(Schofield et al.,
ising proposition for MEG research.
prom
2024). While still an emerging technology, OPM MEG is a
Functional Magnetic Resonance Imaging (fMRI) Perhaps the most common
rison among methods in recent decades is between EEG and fMRI. The
compa
neural activity that generates the EEG signal also increases metabolic demand.
This triggers a vascular response known as neurovascular coupling, which leads to
an increase in blood flow in the active brain region. fMRI detects these changes by
using the blood oxygenation level dependent (BOLD) imaging technique (Ogawa
et al.,
1990), which relies on the different magnetic properties of oxygenated blood
and
de-oxygenated blood. The blood flow response to the increased metabolic
demands, known as the haemodynamic response, is very slow in comparison to
EEG. BOLD signal changes occur over several seconds (Logothetis et al., 2001), in
contr
ast to the millisecond timescale of EEG. However, the strength of fMRI is in its
sub-millimetre three-dimensional spatial resolution (Huettel et al., 2008).
With their differing temporal and spatial trade-offs, EEG and fMRI show us
different
aspects of neural activity (or correlates thereof), and which one is ‘better’
really depends on the research question. The two methods can also be complementary and are often measured simultaneously (Warbrick,
away
from the idea that EEG is a cost-effective a lternative if you can’t do fMRI.
2022). We should move
They each make a unique contribution to our understanding of brain function, and
it’s up to us as researchers to use them appropriately.
Functional Near-Infrared Spectroscopy (fNIRS) fNIRS, like fMRI, detects
changes
in the BOLD signal, but using near-infrared light instead of the magnetic
properties of haemoglobin. The near-infrared range (650–950 nm) reaches the
cortical surface but will not penetrate it; therefore, measurement is limited to cortical
regions (Li et al.,
2022).
The spatial resolution of fNIRS is superior to that of EEG but is inferior to fMRI.
temporal resolution of fNIRS (approximately 100 ms) is faster than fMRI but
The
slower than EEG. fNIRS is less susceptible to noise than EEG, and like EEG, it has
the advantage of being portable, so it can be used outside of the lab. Therefore,
fNIRS is well-suited to studies in naturalistic environments (Pinti et al.,
Positron
Emission Topography (PET) PET isn’t competing in the same measure-
2020).
ment space as EEG, but it can be a complementary method. It is considered the gold
standard for metabolic imaging (Shah et al.,
physi
ological and pathophysiological processes by measuring uptake of radioactive
2013). It provides information on
tracers, for example in pathologies such as brain tumours.
8 T. Warbrick
Typically, a tracer bolus is administered, and uptake is measured over, for
example 30 min. It can be complementary to EEG, especially when also combined
with fMRI in a trimodal ima ging approach (Shah et al.,
measure electrophysiological activity, fMRI can map functional connectivity,
can
and PET can quantify energy metabolism, building a comprehensive picture of the
default mode network or sleep states (Shah et al., 2017; Chen et al., 2025).
More recently, developments in functional PET (fPET) allow for better temporal
resolu
tion (Li et al., 2020). Task-related glucose uptake can be meas ured; however,
this is sampled in the order of minutes rather than seconds, so the timescale is very
different from EEG. While EEG and PET differ, being aware of the potential of fPET
enhances our understanding of the scope of available imaging tools.
Multimodal EEG There is a trend towards combining EEG with other imaging and
stimul
ation methods. Multimodal approaches exploit the complementary strengths
of different techniques. The measurement methods described above use different
measurement principles and vary in terms of temporal and spatial resolution. Consequently, combining them can maximise the strengths of each method to yield a
more complete understanding of brain function. The technical developments in
recent decades enable these multiple modal approaches, with some technologies
being developed specifically for multimodal applications, for example amplifiers and
EEG caps that can be used inside an MRI scanner.
EEG can also be combined with brain stimulation methods to investigate causal
relations
modal EEG combinations is beyond the scope of the chapter, but Table 1.1 gives an
overvi
combinations also have dedicated chapters in Parts V and VI of this book.
hips between brain and behaviour. An exhaustive discussion of all multi-
ew of common combinations, the main advantages, and further readi ng. Some
2013). For example, EEG
1.4 The Future of EEG
EEG is a well-established, powerful technique and has a unique place among brain
imaging methods. However, the future of EEG depends on how we choose to
develop and implement EEG as a research community. While it is useful to consider
the future in terms of technological advances and potential new applications, other
factors must also be considered, for example availability, biases in study
populations, and replicability. How we collectively choose to handle these issues
will determine EEG’s future.
Two recent studies surveyed or interviewed EEG practitioners on how they think
the field will develop in the coming years (Hu & Yan, 2024; Mushtaq et al., 2024).
These
papers provide a good starting point for considering the future of EEG, and we
will briefly consider the technical development, analysis, and application themes that
emerged from these papers. We then consider the broader topics of availability,
diversity, and replicability.
1 EEG in Context: Past, Present, and Future 9
Table 1.1 Common multimodal EEG applications
Combination Main advantage(s) Further reading
EEG–fMRI Measures the neural and neurovascular
EEG–fNIRS Complementary temporal and spatial resolu-
EEG–PET/
–fMRI–
EEG
PET/
EEG–TMS Allows the study of cortical reactivity and
EEG–tACS Modulates brain oscillations non-invasively
The main advantages of each combination are listed, along with references for review papers that
can be considered a good starting point for further reading
responses
Exploits the temporal resolution of EEG and
the spatial resolution of fMRI
Uses EEG to identify events for fMRI analysis,
for example sleep and epilepsy
tions
Both
Allows validation of neurovascular coupling
Investigates metabolic changes associated with
brain dynamics
connectivity
Non-invasive brain stimulation
Invest
tions and cognitive function
to the same stimuli
are portable
at high spatiotemporal resolution
igates causal links between brain oscilla-
Warbrick (2022)
Chapter
33: Combining EEG
and
fMRI
Li et al. (2022)
Shah et al. (2013)
Hernandez-Pavon (2023)
and
Tremblay et al. (2019)
Chapter
32: EEG and TMS
Herrmann et al. (2016) and
Cabral-Calderin and Wilke
(
2020)
Chapter
31: Combining EEG
and
Transcranial Brain
Stimulation
Technology Affordable hardware, artificial intelligence (AI) advances, virtual real-
ity (VR), and brain–computer interfaces (BCI) were identified as having significant
potential for advancing our understanding brain–behaviour relationships. While
these technologies already exist, they are considered potential growth areas that
can advance EEG research.
Data Processing The highest priorities identified were improving tools for quanti-
tative
EEG analysis and standardising protocols for data acquis ition and analysis.
While these seem like rather obvious things to have on our wish list, the needs
remain unmet and finding solutions poses significant challenges. This chapter cannot
provide detailed solutions, but highlighting the lack of robust and standardised
protocols is important, and we encourage the research community to continue
striving for these.
Applications
Clinical and diagnostic applications were considered the most likely
developments. Short term predictions were diagnosis of sleep disorders and automated processes for some clinical applications. A slightly longer-term prediction
was the detection of clinical abnormalities (e.g. neurodegenerative dementia). More
speculative aims were decoding dream s and reading the contents of memory. This
shows that EEG is dynamic field of research with a potentially exciting future as well
as a rich history.