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452 H. I. Stecher et al.
31.1 Introduction
Over the last two decades, transcranial electrical stimulation (tES) has been
rediscovered as a valuable tool for both fundamental neuroscience and medical
therapy (Guleyupoglu et al., 2013). Directly influencing the brain activity via electric
fi
elds provides a critical approach to intervention that shifts our understanding of the
relationship between measurable brain activity and observable brain function from a
purely correlative perspective to a causal framework (Bergmann et al., 2016;
Miniuss
early transcranial electrical stimulation studies by Merton and Morton (1980) relied
on
protocols use weak, voltage-limited currents that are informed by neuroimagingbased anatomical insights. These currents are generally considered subthreshol d,
because they do not directly trigger neuronal firing. They are also well tolerated,
producing only mild sensations during stimulation (Turi et al.,
2007). These weak currents (usually in the range of 1–2 mA at the scalp) subtly alter
the
the momentary polarity of the applied electrical field, they alter the neuron’s natural
probability to fire (firing is more likely during anodal stimulation and less likely
during cathodal stimulation). This approach allows for the suppression or excitation
of a targeted region when a constant electrical field is applied, as in transcranial
direct current stimulation (tDCS), or to rhythmically alternate between excitation
and inhibition, as in the family of alternating current protocols such as tACS.
Rhythmic stimulation can then be used to alter endogenous brain oscillations via
synchronization or desynchronization.
tions,
link between neural activity and function. Because the brain is a highly complex
system, stimulation can have many unforeseen effects on brain activity. Therefore, it
is important to confirm that the stimulation has achieved the intended modulatory
effect on brain activity and, in turn, on perception or cognition. This chapter will
explore how electrophysiological measures can be used to demonstrate the effects of
tES
i et al., 2012; Herrmann
painful, supra-threshold high-voltage stimulation with limited focality, today’s
membrane potentials of neurons in the stimulated cortical regions. According to
However, applying stimulation and observing subsequent changes in brain func-
such as alterations in perception or cognition, does not alone establish a causal
et al., 2016; Herrmann & Strüber, 2017). Whereas
2013; Poreisz et al.,
31.2 Types of Electric Brain Stimulation
Electric brain stimulation dates back to Roman times when electric fish were used to
treat headache (Wexler, 2017b). Between the 1740s and 1930s, many experiments
on
electric stimulation of animal and human tissue were carried out with precursors
of the modern battery, the Leyden jar and the voltaic pile (Cohen Kadosh & Elliott,
2013). These experiments were carried out by Luigi Galvani, inventor of the frog leg
experi
ment, and Alessandro Volta, inventor of the voltaic pile. These experiments
31 Combining EEG and Transcranial Electric Brain Stimulation 453
ultimately led to the development of medical devices for therapy referred to as
medical batteries. These batteries were used to stimulate various tissues, including
the human brain (Wexler,
ssance with the development of transcranial direct current stimulation or tDCS
renai
2017a). In 2000, electric brain stimulation experienced a
(Nitsche & Paulus, 2000).
In its simplest form, tDCS delivers a direct current to the scalp via two electrodes:
the anode (+) and the cathode ( ). This current passes through the skull, altering the
membrane voltage of neurons. Depolarization increases the likelihood of neurons
firing in response to incoming postsynaptic currents, while hyperpolarization
reduces this likelihood.
In addition to direct current, tES also employs alternating current, as seen in
tACS,
which can modulate ongoing brain oscillations through entrainment
(Herrmann et al., 2013). Another form of tES, transcranial random noise stimulation
(tRNS
), uses band-limited white noise for stimulation, likely producing neural
effects via stochastic resonance (Antal & Herrmann, 2016). Additionally,
amplit
ude-modulated alternating currents have been utilized in transcranial brain
stimulation, where the amplitude of a high-frequency carrier wave, such as 80 Hz, is
modulated by the phase of a lower frequency, such as 10 Hz (Witkowski et al.,
2016).
Generally, tES methods face the challenge that deeper brain regions cannot be
ated without more intensely affecting superficial brain regions. However, it
stimul
has been demonstrated that applying two sinusoidal currents with different frequencies, f
and f2, through separate electrode pairs can produce a beat frequency at the
1
point where the current pathways overlap. This beat frequency manifests as an
amplitude-modulated oscillation, with a carrier frequency of f
modulation frequency of f
¼jf1 f2j. Known as temporal interference stimula-
m
¼ðf1 þ f2Þ∕2 and a
c
tion (tTIS), this technique was first demonstrated in animals (Grossman et al., 2017)
and
has since been applied also in humans (von Conta et al., 2022).
For all of the aforementioned brain stimulation methods, verifying their ability to
modul
ate brain activity is crucial, with EEG recordings serving as the most effective
method for evaluation.
31.3 Online Effects
Noninvasive brain stimulation aims to modulate neural activity, and the millisecond
temporal resolution of EEG makes it ideal for assessing neural activity during
stimulation (i.e., online effects). However, electrical stimulation using signals in
the range of several volts generates significant artifacts, which are orders of magnitude larger than the endogenous EEG signal, which is measured in microvolts. This
section outlines the challenges of conventional artifact rejection methods in tDCS,
tRNS, and tACS. It then examines phantom-based evaluations, which provide a
controlled approach to characterizing stimulation artifacts and validating artifact
removal techniques. Finally, it discusses innovative strategies for obtaining reliable,
analyzable EEG data during stimulation.
454 H. I. Stecher et al.
31.3.1 Conventional Artifact Removal Strategies
31.3.1.1 Transcranial Direct Current Stimulation (tDCS)
Since tDCS typically does not involve frequency-specific stimulation, artifacts can
theor
etically be removed using a high-pass filter, provided that the EEG signal
remains unsaturated. While artifacts during the ramp-up and ramp-down periods
have been removed successfully (e.g., Roy et al., 2014), persistent low frequency
s remain in the data throughout the stimulation period. These artifacts vary
artifact
significantly across channels and over time, making them challenging to characterize
or eliminate without prior knowledge of the stimulation artifact’s properties (Gebodh
et al., 2019; Mancini et al., 2015; Roy et al., 2014). Furthermore Gebodh et al.
(2019) identified several complex, nonstationary physiological artifacts—such as
cardi
ac, ocular, motion, and myogenic distortions—that are difficu lt to address using
conventional artifact removal techniques. For a detailed discussion on employing
spatial and temporal filters to remove stimulation artifacts, see Gebodh et al. (2019).
31.3.1.2 Transcranial Random Noise Stimulation (tRNS)
TRNS is believed to modulate neural activity through stochastic resonance rather
frequency-specific entrainment. Most research on tRNS effects has focused on
than
motor cortex excitability (for reviews see Potok et al.,
2022). To our knowledge, no systematic studies have characterized EEG artifacts
resultin
g from low-frequency (LF) or full-spectrum tRNS. Several studies suggest
that high-frequency (HF) tRNS may be more effective than LF-tRNS, based primarily on findings from motor cortex research (for discussion see Potok et al., 2022). As
NS employs stimulation frequencies beyond the endogenous EEG range, the
HF-tR
high-frequency component is typically attenuated using low-pass filters (e.g.,
Rufener et al., 2017; Van Doren et al., 2014). Using this approach, Rufener et al.
(
2017) observed reduced peak latencies of the early auditory event-related potentials
(P50
and N1), which was associated with improved detection of near-threshold
stimuli, while stimuli below and above the individual perception threshold were
unaffected.
2022; Van der Groen et al.,
31.3.1.3 Transcranial Alternating Current Stimulation (tACS)
In tACS, the challenge is amplified because stimulation frequencies overlap with the
endogeno
those frequency ranges. Therefore, a key difficulty lies in disentangling true neural
responses from stimulation-induced artifacts.
ltering, template subtraction, principle component analysis (PCA) (Helfrich et al.,
fi
2014b,a), and spatial filtering (Guarnieri et al., 2020; Haslacher et al., 2021).
us EEG frequencies targeted for modulation, resulting in artifacts within
Multiple artifact rejection methods have been proposed for this purpose: temporal
31 Combining EEG and Transcranial Electric Brain Stimulation 455
Fig. 31.1 Grand average power at electrode POz before, during, and after 20 min of 10 Hz tACS
(B) or sham (A) during a visual oddball task. The orange lines depict the average power before
stimulation, the gray lines depict recovered power during stimulation, and the green lines depict the
power after stimulation. While there are no significant differences during sham stimulation around
10 Hz between conditions during sham stimulation, during tACS an increase is visible during (ISI)
and after tACS stimulation (adapted from Helfrich et al. (
Elsevier)
2014b) and published with permission by
Figure 31.1 illustrates how a combination of template subtraction and PCA was used
to recover power increases during and after alpha-stimulation. While the
abovementioned methods are suitable to reduce linear effects of the stimulation,
they are insufficient to remove nonlinear effects. These include artifacts arising from
interactions between stimulation and other physiological processes (Noury et al.,
2016; Noury & Siegel, 2017 ).
At present, there is no consensus on the effectiveness of artifact rejection methods
in fully eliminating stimulation artifacts in tACS studies (for discussions, see
Neuling et al., 2017; Noury et al., 2016; Noury and Siegel, 2018). Incomplete
artifact
removal can result in systematic noise that may be mistaken for evidence
of neural entrainment. Therefore, establishing standardized criteria to evaluate the
success of stimulus artifact removal would significantly advance the assessment of
“online” neural EEG effects of entrainment in humans.
31.3.2 Validating Artifact Removal Methods: Phantom-Based
Evaluations
One strategy for addressing this challenge is the use of a “phantom” head model,
which replicates the conductive properties of the head but lacks neural activity.
Phantom models can be utilized in two complementary ways: (1) to systemat ically
characterize the stimulation artifact and (2) to compare and validate artifact removal
methods in controlled conditions. A range of phantom models have been proposed,
from simple setups such as water melons (Guidetti et al.,
s (Kohli & Casson, 2019) to more sophisticated versions (e.g., Huno ld et al.,
model
2018, 2020) that better approximate the conductivity of the head.
2025) and gelatin head
456 H. I. Stecher et al.
To characterize the stimulation artifact, Roy et al. (2014) recorded EEG from a
melon model during anodal, cathodal, and sham tDCS. In both types of active
stimulation, they observed polarity specific artifacts across the frequency spectrum
in electrodes near the stimulation sites. These artifacts were identified via independent component analysis (ICA) and subsequently compared with ICA decompositions of human EEG to facilitate artifact remo val. Additionally, they detected
low-frequency drift artifacts throughout the stimulation period, prompting them to
filter out frequencies below 2 Hz in further analyses.
A limitation of using ICA alone is the need for visual inspection and matching of
participa
effective for anodal stimulation (Mancini et al., 2015; Roy et al., 2014). To address
this
manua
et al. (
phanto
human EEG data, generating a reference signal correlated with the tDCS artifact. An
adaptive filter then employs this reference signal to minimize the artifact in each
EEG channel. Subsequently, ICs from the cleaned data are inspected to confirm
artifact removal. Unlike the ICA-only approach, this method eliminates the need for
visual IC inspection for each participant once a reference signal is established.
However, validation still relied on visual inspection of the ICs obtained after
adaptive filtering. Although this approach advances artifact rejection, the authors
note the need for more robust validation methods and assessment across a wider
range of anode-cathode configurations to fully validate the technique.
free of stimulation artifacts can be used (e.g., Kohli and Casson, 2019; Vosskuhl
et
using multiple concentric plastic spheres filled with a saline solution to mimic the
conductivity of the human brain and scalp. An artificial dipolar current source within
the phantom head played prerecorded, artifact-free EEG signals, while tACS was
applied. This setup allowed a direct comparison of multiple artifact removal techniques, including simple sine wave subtraction, template subtraction, and single
space projection, with the latter proving most effective.
identifyi
artifacts from human EEG data.
nts’ ICs to reference ICs derived from the phantom, a method particularly
issue, Mancini et al. (2015) proposed an automated filtering approach that avoids
l IC selection and is adaptable to various stimulation types. Similar to Roy
2014), Mancini et al. (2015) begin by capturing tDCS artifacts using a
m model. These ICs are used to identify corresponding artifactual ICs in
To validate artifact removal methods, phantoms with a “ground truth” EEG signal
al., 2020). For example, Vosskuhl et al. (2020) constructed a “phantom head”
This work illustrates how phantom models provide a controlled environment for
ng, classifying, developing, and evaluating strategies to remove stimulation
31.3.3 Innovativ e Approaches to Minimize Artifacts
Beyond developing sophisticated artifact rejection and assessment methods, another
strategy for obtaining analyzable EEG data during stimulation involves using alternative types of stimulation that differ from endogenous neural activity. These
approaches typically focus on generating stimulation waveforms that are distinct
from EEG signals or delivering stimulation at frequencies outside of the physiological EEG range, allowing them to be filtered out effectively.
31 Combining EEG and Transcranial Electric Brain Stimulation 457
31.3.3.1 Non-Sinusoidal Waveforms
One such method involves using non-sinusoidal stimulation shapes that are distinct
from
endogenous oscillations. For instance, Dowsett and Herrmann (2016)
employed a sawtooth-shaped current as the stimulation waveform. Sawtooth waveforms generate harmonics in the freque ncy spectrum, which can be reduced with
artifact removal algorithms while preserving the peak at the fundamental frequency
of interest. This approach leverages harmonics that are distinct from neural EEG
signals, making the artifact predictable and easier to remo ve.
31.3.3.2 Amplitude-Modulated tACS (AM-tACS)
In AM-tACS low-frequency envelopes of the high-frequency carrier signals are used
modulate the endogenous activity. Since the high-frequency carrier frequencies
to
exceed the endogenous EEG frequency range, the artifact can be removed from the
EEG using low-pass filters (Witkowski et al., 2016) or spatial filters (Haslacher et al.,
2021). However, Kasten et al. (2018) demonstrated that there can be nonlinear
s that transfer from stimulation devices and contaminate the data. Therefore,
artifact
further development of the stimulation devices is necessary to minimize these effects
and enhance the effectiveness of this approach.
31.3.3.3 Transcranial Temporal Interference Stimulation (tTIS)
TTIS avoids nonlinear artifacts associated with stimulation devices which are
comm
on in AM-tACS, by using pure sine waves that interact within the brain to
produce a frequency that aligns with the endogenous target frequency (von Conta
et al.,
2022). Although these artifacts can be mitigated with a low-pass filters, von
et al. (2022) emphasized that selecting an EEG system with a larger dynamic
Conta
such as a 24-bit amplifier capable of sampling a wide range of amplitudes
range,
crucial for accurately recovering endogenous EEG signals.
31.3.3.4 Summary of Advantages and Limitations
These innova
tive stimulation techniques aim to either distinguish the artifact from
neural EEG or shift it outside the physiological frequency range enabling the
recovery of neural EEG using conventional artifact rejection methods. Alternative
waveforms introduce complex harmonics, while high-frequency approaches require
EEG amplifiers with a large dynamic range to prevent hardware-related artifacts.
Despite these challenges, such methods provide promising avenues for acquiring
reliable neural EEG signals during tES.
458 H. I. Stecher et al.
31.4 Offline Effects
EEG can also be used to evaluate effects of transcranial brain stimulation that outlast
stimulation offset (also known as offline effects or aftereffects). Studies employing
this method typically compare EEG measures, such as power or phase locking value,
across various time periods and conditions.
Common comparisons include post- versus pre-stimulation, post-stimulation
versus
compared to a corresponding difference in a control condition (e.g., sham, shunt,
or alternative-frequency stimulation). In addition, a within-participants design is
preferable as compared to a between-participants design due to the decreased
variance and thus increased statistical power. This ideal comparison is crucial
because certain EEG measures vary systematically regardless of electrical stimulation. For example, alpha power increases and peak alpha frequency decreases over
time (Benwell et al., 2019). A systematic examination of individual differences is
still
be
Consequentially, studies employing these methods typically examine modulations
of spectral power, phase alignment, or coherence. In contrast, non-oscillatory stimulation forms, which are not expected to produce frequency-specific effects, are
commonly assessed using measures like event-related potentials (ERPs) to detect
changes in cortical excitability that may not directly correspond to oscillatory
activity.
effects.
post-sham, or, ideally the difference between post- and pre-stimulation
needed to determine whether between-participant comparisons are essential.
In stimulation methods involving oscillatory components, spectral changes may
expected in target frequencies, as well as adjacent frequencies and harmonics.
This section reviews EEG measures that are typically used to evaluate offline
31.4.1 Spectral Power
In tACS, Zaehle et al. (2010) were the first to use EEG to examine changes in cortical
excitability induced by stimulation. They applied stimulation at the individual alpha
frequency (IAF) over the occipital cortex and observed an increase in alpha power
increased post-stimulation compared to pre-stimulation in the tACS group relative to
the sham group. Similar increases in spectral power within the stimulated frequency
band have been reported in numerous studies across various tasks (e.g., Aktürk et al.,
2022; D’Atri et al., 2019; Helfrich et al., 2014b; Kasten et al., 2016; Neuling et al.,
2013; Stecher et al., 2021; Vossen et al., 2015). For an overview see Veniero et al.
(2015). Figure 31.2 depicts an example of alpha power increase following IAF
tACS.
However, several studies failed to observe such effects (e.g., Harada et al., 2020;
fleur et al., 2021; Neuling et al., 2013; Stecher and Herrmann, 2018; Strüber et al.,
La
2015; Wischnewski et al., 2019). These inconsistencies likely arise from variations
31 Combining EEG and Transcranial Electric Brain Stimulation 459
Fig. 31.2 Average power at electrode Pz before and after 20 min of IAF tACS (top) and sham
(bottom) during an eyes-open auditory detection task. The dashed lines indicate the pre-stimulation
power, solid lines indicate post-stimulation power, and the shaded areas represent the standard error
of the mean. Even after sham, alpha power increases over time from pre- to post-intervention (blue
spectra). After tACS, this increase is significantly stronger (red spectra). This nicely illustrates why
a sham group is necessary for tACS (adapted from Neuling et al. (
Commons
Attribution License (CC BY 3.0))
2013) under the Creative
in experimental design, including differences in tasks, stimulation parameters, and
statistical comparisons between pre- and post-stimulation. For example, Stecher and
Herrmann (
between
2018) conducted an exploratory analysis suggesting that a mismatch
the stimulation frequency and the participants’ IAF could account for the
absence of aftereffects in their study. Similarly, Kasten and Herrmann (2022) and
Neuling
et al. (2013) propose that the state of the stimulated network may influence
whether or not aftereffects occur.
Spectral power modulations have also been observed in other types of stimulation. For instance, HF-tRNS has been shown to enhance individual gamma acti vity
in the auditory cortex following an auditory task (Rufener et al., 2017) and during a
visua
l task (Ghin et al., 2021). In contrast, studies investigating resting state EEG
have
yielded mixed results. While Ke et al. (2024) observed no frequency specific
of tRNS on resting EEG, Van Doren et al. (2014) reported power increase in
effects
all
frequencies, and (Mohsen et al., 2019) observ ed power increases following both
and HF-tRNS applied over multiple sites. These findings suggest that gamma
LFmodulations may be task-dependent, primarily occurring when tRNS is paired with
sensory or cognitive tasks. However, not all task-related studies support this conclusion. For example, Schoisswohl et al. (
the power of any frequency band in resting state EEG. These mixed results
on
2021) found no effects of LF- or HF-tRNS
underscore the need to clarify the effects of tRNS on spectral power and whether
they depend primarily on task engagement and stimulation parameters.
460 H. I. Stecher et al.
Polarity-specific modulations of spectral power have also been reported with
tDCS. For example, Boonstra et al. (
below
15 Hz and decreases above 15 Hz following anodal tDCS. Similarly, Spitoni
et al. (2013) applied cathodal and anodal tDCS to the posterior parietal areas and
that only anodal tDCS increased alpha power post-stimulation. This finding
found
was replicated by Mangia et al. (2014), who also noted increases in beta power.
Together
polarity and frequency-dependent manner. However, further systematic research is
needed to determine the conditions under which these effects are consistent and
reliable.
stimul
highlight the need for systematic research to explore moderating factors, including
frequency matching, stimulation parameters, state of the network, and task
dependency.
, these studies suggest that tDCS may modulate oscillatory activity in a
In summ ary, although modulations of spectral power persisting beyond the
ation period are frequently reported, the inconsistent findings in the literature
2016) observed increases in spectral power
31.4.2 Phase Locking/Phase Coherence
Matsumoto et al. (2010) reported polarity-specific effects of tDCS on mu-rhythm
desynchronization, with anodal stimulation leading to increased desynchronization
and cathodal stimulation resulting in decreased desynchronization. Beyond local
effects, interhemispheric connectivity has also been modulated using tACS. Helfrich
et al. (
2014a) applied bilateral 40 Hz tACS over the parieto-occipital cortex, either
ase or antiphase. Their findings indicated an increase of interhemispheric
in-ph
gamma coherence for in-phase stimulation, whereas antiphase stimulation impaired
functional coupling.
Although the number of studies examining phase locking and coherence using
EEG
is limited, the results suggest that stimulation can affect network synchrony.
Similar to the effects on spectral power, methodological factors such as frequency
matching, electrode montage, and network state are likely to impact phase locking
and coherence.
31.4.3 ERPs
Studies combining EEG and tDCS have reported polarity-specific stimulation effects
on visual, auditory, and sensory ERPs. An early study by Antal et al. (
analyz
ed the effects of tDCS over the occipital cortex with EEG recordings before
and after electrical stimulation. They found that occipital stimulation increased the
amplitude of visual ERP components N70 and P1, while their latencies remained
unaffected. Zaehle et al. (2011) applied tDCS to the left temporal and
roparietal cortex, observing that anodal tDCS significantly increased P50
tempo
amplitudes, whereas cathodal tDCS increased N1 amplitudes. Similarly, a study
2004)
31 Combining EEG and Transcranial Electric Brain Stimulation 461
assessing medial frontal potentials associated with task performance monitoring,
such as the error-related negativity (ERN) and the feedback-related negativity
(FRN), found that cathodal tDCS reduced the ERN and FRN amplitudes to levels
comparable to the control condition, while anodal tDCS elicited significantly larger
ERNs and FRNs compared to sham stimulation. However, other studies reported no
changes in the ERP amplitudes or latencies following anodal tDCS
2016; Kunzelmann et al., 2018). These
differences, including variations in stimulation parameters, electrode placement, and
experimental tasks. To achieve reliable ERP modulations, these factors must be
systematically investigated and optimized.
HF-tRNS has also been shown to modulate the latency and amplitudes of ERPs.
For example, Rufener et al. (2017) observed reduced peak latencies of P50 and N1
components following HF-tRNS applied to the auditory cortex. While many stimulation studies apply stimulation during a task to evaluate behavioral measures
online and then assess neural meas ures offline, fewer have explored the effects of
stimulation delivered prior to a task on subsequent behavior and neural activity. The
mechanisms underlying tRNS offline effects remain poorly understood, whereas
online effects are mostly attributed to stochastic resonance. Studying pre-task
stimulation effects could clarify whether the same mechanisms operate when stimulation is applied before versus during the task. For instance, Ghin et al. (
applie
d HF-tRNS before a visual motion detection discrimination task, and recorded
EEG, finding no effects of HF-tRNS on visual evoked potentials (P1, N1, and P2).
Only a limited number of studies have directly examined the tRNS effects on ERPs,
with mixed results. More work is needed to determine whether and how tRNS
alters ERPs.
discrepancies likely reflect methodological
(Conley et al.,
2021)
31.4.4 Further Measures
Less commonly assessed measures include peak alpha frequency shifts (see Millard
et al.,
2024, for a meta-analysis), as well as functional connectivity (e.g., Schwab
et
al., 2019; Polanía et al., 2011; Mohsen et al., 2019), and entropy measures (e.g.,
Nascim
ento et al.,
2019) providing insights into network-level dynamics.
31.4.5 Online Versus Offline Measures
Offline measures can provide valuable insights into the persistence of stimulation
effects beyond the simulation period, but they may not reflect the same underlying
mechanisms as those engaged during the stimulation. For instance, in tACS online
effects are likely driven by entrainment, whereas offline effects involve plasticityrelated processes (Vogeti et al., 2022). This distinction is crucial, as relying solely on
fline measures to infer how stimulation affects neural dynamics during stimulation
of
may result in inaccurate conclusions.