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294 S. J. Luck
Table 22.1 Recommended filter settings from Zhang et al. (2024b) for the 7 ERP components in
ERP CORE (in Hz, with a slope of 12 dB/octave), separately for each of four different scoring
the
methods
Mean amplitude Peak amplitude Peak latency 50% area latency
High-
pass
P3 0.2 10 or
N170 0.9 30 or
MMN 0.5 20 or
N400 0.2 10 or
LRP 0.3
ERN 0.4
Lowpass
none
none
none
none
30 o
none
20 o
Highpass Low-pass
0.2 10 (or 10
0.9 30 or none
(or 30
0.5 20 or none
(or 30
0.2 10 (or 10
r
0.3
r
0.4
30 (
a
)
a
)
a
) 0.3 5
20
or
20 o
none 0.4
r
Highpass
a
) 0.2 10
Lowpass
(or 5
a
)
0.9 10–20 0.9 10–20
0.5 10
a
) 0.2 10
(or
5
(or 5
(or 10
b
)
a
)
c
)
10
Highpass
Lowpass
0.2 10
(or
0.5 20 or
none
0.2 10
(or
0.3 5
(or
0.4
10
5
5
10
a
)
a
)
c
)
none
N2pc
0.5 20 or
none
a
For data with moderate amounts of high-frequency noise
b
For data with moderate amounts of broadband noise (if distortion of onset and offset times can be
tolerated)
c
To avoid distortion of onset and offset times
0.5 20–40
(or
20
a
)
0.01 or
none
10 0.01 or
none
5
(or
10
c
)
If you apply our procedure for determining the optimal filter settings to your own
data, it will naturally take into account the component you are measuring, how you
are measuring it, and the types of noise that are present in your data. You might not
want to take the time to apply our procedure to your own data, so we’ve determined
the optimal settings for the seven ERP components in the ERP CORE (Zhang et al.,
2024b). The optimal settings are shown in Table 22.1. If you are measuring these
components or similar components, and you have data with a similar noise level, you
should be able to adapt these filter settings to your own data. The ERP CORE data
were collected from highly cooperative college-student participants using a highquality gel-based EEG system inside an electrically shielded chamber, so the data
were quite clean. However, the table also shows the optimal settings for somewhat
noisier data, which we assessed by adding broadband noise to the ERP CORE data
prior to determining the optimal settings, so you can still use our results if you have
somewhat noisier data. However, if your data have much higher levels of noise,
especially high levels of low-frequency noise, you will probably benefit from more
aggressive filtering (and you should determine the optimal settings using your own
data).
22 Quantifying EEG and ERP Data Quality 295
22.2.8 Other Potential Uses of the SME
The SME can also help you determine the optimal approach for other steps of your
processing pipeline. For example, we have also used the SME to assess the impact of
artifact correction and artifact rejection on data quality, showing that excluding trials
with artifacts will improve the overall data quality as long as the number of excluded
trials is not too large (Zhang et al.,
to
determine the optimal artifact rejection threshold for each participant and to decide
whether a “bad” channel is so noisy that it should be interpolated. And, as shown in
Fig. 22.3, the SME can help you determine which scoring approach will give you the
smalles
t SME.
In principle, you could use the SME to identify participants whose data are so
that they should be excluded from the final analyses of a study (see suppl e-
noisy
mentary section S8 in Luck et al., 2021). However, there are three important caveats.
First,
it is essential to define your criteria for exclusion prior to analyzing the data.
Otherwise, you will likely inflate the rate of false positives in your statistical tests.
Second, you may end up with a non-representative sample of participants, because
the amount of noise may be correlated with other characteristics of your participants.
Third, the benefits of excluding participants with noisy data may be outweighed by
the reduction of degrees of freedom in your statistical analyses. To avoid this
reduction in degrees of freedom, you could instead use the SME as a weighting
factor in your statistical analyses, downweighting contributions from participants
with noisier data rather than completely excluding those participants.
2024c). In principle, the SME could also be used
22.3 Adapting the SME for Time-Frequency Analyses
and Other Psychophysiological Signals
It would be conceptually straightforward to adapt the SME for time-frequency
analyses and for other psychophysio logical signals. The main constraint is that it
is valid only for scores obtained after averaging across trials.
For many standard time-frequency analyses, a wavelet transform is applied to
convert
the time-frequency data are averaged across trials. A score is then obtained from the
averaged time-frequency representation. When the score is the mean across a range
of frequencies and time points (e.g., the mean power between 8 and 12 Hz from
300 to 600 ms), Eq.
compl
as the skin conductance response, as long as the data are scored from averaged
waveforms. There are many advantages to obtaining ERP, time-frequency, and other
psychophysiological scores from the single-trial epochs rather than obtaining the
scores from averaged waveforms, especially if the data are analyzed using multilevel
the single-trial EEG epochs into a time-frequency representation, and then
22.1 could be used to compute the analytic SME. For more
ex scoring algorithms, bootstrapping could be used to obtain the SME.
Similar approac
hes could be used for other psychophysiological measures, such
296 S. J. Luck
models (Bürki et al., 2018; Heise et al., 2022; Volpert-Esmond et al., 2018; Winsler
et al.,
2018). Some other approach would be needed to quantify data quality in these
situati
ons.
22.4 Metrics of Reliability
An alternative approach to quantifying data quality is to compute a measure of
psychometric reliability. In psychometrics, reliability is defined as the ratio of true
score variance to total variance, where true score variance is variance across the
individuals in a sample that reflects their true differences (uncontaminated by
measurement error) and total variance is the observed variance that results from a
mixture of true differences across individuals and measurement error.
The true score variance is not something you can directly measure, but there are
us ways of estimating reliability. Consider, for example, the data shown in
vario
Fig. 22.1, in which P3 amplitude was measured from an averaged ERP created by
averagi
ng together 20 trials. Imagine that you made one average from the
10 odd-numbered oddball trials and another average from the 10 even-numbered
trials. You could then score the P3 amplitude from each of these two averaged ERP
waveforms. If these two P3 amplitude scores are quite similar, this would suggest
that your data quality is high (i.e., that your P3 amplitude scores are relatively
precise). However, you might have simply gotten lucky with the two specific sets
of 10 trials used to create each average. The typical approach is therefore to get these
two scores (i.e., from the averages of the odd-numbered and even-numbered trials)
from each of your participants, and then look at the Pearson r c orrelation between
them. If the correlation between the scores from the odd-numbered averages and the
scores from the even-numbered averages is high, then the data quality must be high.
This is called the split-half reliability. The estimate of reliability that you obtain in
this manner will underestimate the true reliability, because each averaged ERP
waveform has only half the number of trials that will be used in the main analysis,
but the Spearman-Brown prophecy formula can be used to adjust for this (Brown,
2018). Also, you can compu te Cronbach’s alpha instead of split-half reliability,
effectively gives you the reliability averaged across all possible splits of the
which
data (Tavakol & Dennick, 2011).
This approach has two major downsides. One is that it gives you a metric of data
quality
data quality for individual participants. However, Clayson and his colleagues have
developed an approach to reliability that provides estimates of subject-level as well
as group-level reliability (Clayson et al.,
true score variance divided by total variance, it depends on the amount of real
variability across participants and not just the data quality. In general, you will see
greater reliability in heterogeneous groups of participants than in homogeneous
participants. For example, imagine that you have taken great pains to ensure that
only for the entire sample of participants, and it tells you nothing about the
2021b).
The second
and more significant downside is that, because reliability is defined as
22 Quantifying EEG and ERP Data Quality 297
all of your participants have gotten a good night’s sleep the night before, that they are
being tested at their best time of day, that they are not hungry, and they are in a
neutral mood. All of these factors will tend to reduce the true score variance
(assuming that each participant is tested in a single session) and will therefore reduce
the reliability, making it seem as if you have poor data quality. Or imagine that you
have used a difference wave to isolate the effects of a new ex
tion that produces the same effect in all adult participants. Again, you will have low
reliability and might think you have poor data quality. This is sometimes called the
reliability paradox (Hedge et al.,
However, this does not mean that reliability metrics have no value. If you are
studying individual differences—looking at correlations between psychophysiological scores and other measures—reliability is usually the appropriate way to quantify
data quality. This is because you must have substantial true score variance to see
correlations with other measures, and the ratio of true score variance to total variance
will predict the maximum correlation you can expect to obtain given your data
quality. Note that you can convert SME values into true score variance and reliability
with some simple math, so there is no need to create the averages of odd-numbered
and even-numbered trials (see Luck et al.,
A much more advanced and powerful approach to estimating reliability has been
develo
ped by Clayson et al. (2021a, b). Whereas the SME, split-half reliability, and
Cronbach
based on generalizability theory (Briesch et al., 2014). As a result, it can model
facto
analysis, including single trials, averages, and differences between conditions.
Unlike the SME, it could be used to quantify data quality for single-trial analyses.
However, because it is still a measure of reliability, it is still dependent on the
amount of true score variance and is therefore best suited for correlational (individual differences) analyses.
’s alpha are based on classical test theory, this new approach to reliability is
rs that impact the consistency of scores across multiple different levels of
2018).
2021).
perimental manipula-
22.5 Final Thoughts
Given how noisy the EEG can be, it is rather remarkable that rigorous metrics of
ERP data quality have only recently been developed and disseminated. These
metrics can be highly valuable for avoiding problematic data and for determining
the methods that yield the cleanest data. Here I have focused mainly on the SME,
which is now accessible in multiple EEG/ERP software packages. However, the
most important point is that you should pay close attention to data quality and
quantify it using whatever metrics are most sensible for your research. By optimizing
your data quality, you will increase the likelihood that you can publish your research
in top-quality journals and you will also increase the likelihood that the conclusions
of your research are true. And isn’t that what we all want?
298 S. J. Luck
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Part V
What Is EEG Used For?
Chapter 23
EEG in Basic Science and Academic
Research
Fernando Cross Villasana
Abstract The EEG possesses qualities that make it a valuable tool in basic scientific
resear
ch. Thanks to its faster than millisecond temporal precision, EEG can track
fast-paced neurological activity and allow us to investigate its relation to cognitive
processes. Its portability and non-invasive nature allow large samples to be tested,
and facilitate access to populations that may be difficult to test using other techniques. The desig n of EEG studies and their interpretation largely relies on broader
frameworks from experimental psychology and cognitive neuroscience. This
enables a syst ematic approach for relating the brainwave and behavioral observations to neural functions, and for building cognitive models about attention, emotion,
language, or memory, among other functions.
This chapter focuses on EEG in the context of the broader frameworks from
ive neuroscience that are the basis for most academic EEG studies. The
cognit
emphasis is on the process of how such frameworks can be applied to investigate
the links between brain, cognition, and behavior, and how these methods contribute
to clarify the meaning of EEG rhythms. The chapter ends with brief examples of
contributions from other disciplines to EEG, and how EEG-derived knowledge can
contribute to questions in other fields of study.
Keywords Neuropsychology · Mental chronometry · EEG
23.1 Cognitive Neuroscience
Over the decades, cognitive research has enhanced our understanding of faculties
like sensation, perception, memory, attention, language, movement, or decisionmaking, where EEG has played an important role. However, investigating brain
function in relation to behavior began long before the development of EEG. For
example, systematic observations of the deficits shown by patients with different
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_23
303
304 F. Cross Villasana
kinds of neurological damage have been used to attribute mental functions to
different sections of the brain (Vaidya et al.,
part
of the field of neuropsychology. Additionally, in the field of mental chronometry, clever reaction time experiments were devised to make deductions about the
cognitive operations involved in a task. The methods and perspectives of neuropsychology and mental chronometry have provided a useful framework for designing
EEG experiments and interpreting observations (Linden,
des a way
provi
especially with regard to the timing of mental processes (Meyer et al., 1988). These
nes are deeply interconnected and their methods and perspectives continu-
discipli
ously evolve. This chapter presents research examples within the framework of
cognitive research, specifically neuropsychology and mental chronometry, to show
how EEG integrates into the landscape of cognitive neuroscience.
to relate theoretical constructs about cognition to activity in the brain,
2019). This method would become
2007)
Meanwhile, EEG
.
23.1.1 Neuropsychology and EEG
During the nineteenth century, the idea that particular areas of the brain relate to
specific cognitive functions gained traction among the scientific community (Benton
& Sivan, 2007). This notion motivated the systematic observation of cognitive
deficits produced by lesions in different locations of the brain in patients (Vaidya
et al., 2019). Physicians and researchers have used meticulous observations of the
patien
t’s behavior, and developed diverse procedures to test their cognitive function.
From these works, the concept of dissociation appeared (Vaidya et al., 2019), where
impairm
without affecting other functions. Such dissociation would suggest that the impaired
function is independent of other functions, and can be attributed to a specific brain
area. A condition that brings stronger evidence for dissociation is double dissocia-
tion, where two patients with lesions in different brain regions (A and B) each show a
deficit in one cognitive function (X or Y) without effects in the other function
(Ramminger et al.,
are
Aiding such dissociation logic, technologies like EEG can be used to investigate the
involved cognitive functions not only in patients, but also in healthy population. In
this way, EEG enables studying normal neuro-cognitive function beyond the clinical
context. In particular, event-related potentials (ERPs) have been useful in this regard
due to their precise timing. More information on ERPs can be found in Chap.
dent function from the perception of other objects. This is reflected in the clinical
condition of prosopagnosia (Duchaine & Nakayama, 2005; Gerlach & Starrfelt,
2024), where patients are unable to recognize faces, without showing visual or
memo
et al., 2016). Prosopagnosia can occur after a brain injury, mainly to the fusiform
face
ent of a cognitive function would be present after damage to a brain region,
2023; Vaidya et al., 2019). This would imply that these functions
implemented by separate brain sub-systems, and are independent of each other.
19.
An example of a proposed dissociation is the perception of faces as an indepen-
ry impairments, nor impairments in the recognition of other objects (Corrow
area (FFA) but also to the occipital face area (OFA) or on temporal regions
23 EEG in Basic Science and Academic Research 305
like the posterior superior temporal sulcus. Some patients have developmental
prosopagnosia, so they do not acquire face recognition skills despite not suffering
adverse neurological events (Corrow et al.,
flected by the N170 ERP component in the healthy population. The N170 shows
re
2016). In EEG, a similar dissociation is
enhanced amplitude to the perception of faces (Rossion & Caharel, 2011), and is
estimat
ed to originate from the FFA (Gao et al., 2019
prosop
agnosia, N170 modulation is altered when the damage involves both the FFA
). In patients with acquired
and OFA, but preserved if the damage is limited only to OFA or to anterior temporal
regions (Corrow et al.,
ion, while temporal areas are involved in later stages of face identification
percept
2016). This supports the role of FFA and OFA in early face
(Corrow et al., 2016). It further supports the notion that face perception processes are
themselv
al., 2023).
et
es dissociated from later face memory (Dalrymple et al.,
2014; M
In patients with developmental prosopagnosia, the N170 modulation is
ani
ppa
preserved in many but not all cases, suggesting that some patients preserve perceptual face processing (Towler & Eimer, 2012). However, if the faces are inverted or
led, they fail to show additional N170 modulations normally seen in healthy
scramb
participants. This suggests that patients may develop some compensatory strategies
for face perception, but these are not effective under more challenging conditions
(Manippa et al.,
2023; Towler et al., 2017). In relation
to the previous proposal, there
is evidence showing that the N170 improves after therapeutic training interventions
in patients with developmental prosopagnosia (Manippa et al., 2023). Altogether,
examples from prosopagnosia show how EEG can inform and refine ideas
these
inspired by the neuropsychological concept of dissociation.
Double dissociation is classically portrayed in the distinction between speech
production and comprehension reflected respectively in Broca’s and Wernicke’s
aphasia (Ramminger et al., 2023). In Broca’s aphasia, patients with damage to a
network
involving the insula, left inferior frontal gyrus, and adjacent areas have
difficulty generating speech but retain the ability to understand language with some
limitations (Fridriksson et al.,
a, patients with damage involving middle-posterior temporal areas show
aphasi
2015; Pracar et al., 2025; Silva, 2021). In Wernicke’s
impaired language comprehension, while preserving their ability to generate fluent
but incoherent speech, using mixed words and sounds, and neologisms (Matchin
et al.,
2022; Silva, 2021; Thompson et al., 2015). From a language processing
perspe
ctive, these deficits reflect impairments in the syntactic and semantic processes for Broca and Wernicke aphasia patients respectively. However, there is still a
degree of overlap in the symptoms of these two aphasias that calls for a closer
investigation of this proposal. With EEG, the ERP components N400 and P600
related to language comprehension have been used to gain insight into this question.
In healthy participants, larger N400 component amplitudes have been observed
during sentences with semantic violations, such as the appearance of an unexpected
word (Lau et al.,
2009).
Amplitude modulations of the P600 meanwhile, are
proposed to reflect detection of syntactic violations as in incoherent sentences
(Meechan et al.,
disruptions in both the N400 and P600. While the P600 reflects the expected
shown
2021). ERP assessments in patients with Broca’s aphasia have
difficulties with syntax in Broca’s aphasia patients, the N400 disruption represents a