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8 Designing Your EEG Study 95
cognition. Multiple trials of such events are required to obtain a stable response and
detect experimental effects. The other kind is the block-design. In this approach,
brain activity is tracked over longer periods of time, allowing the study of persistent
states of the brain over minutes or hours, for example, vigilance levels or resting state
connectivity. Though most studies focus on one of these approaches, event-related
and blocked designs are compatible with ea
ch other.
Multiple standard experimental paradigms have been developed to test faculties
such as memory, perception, attention, vigilance, or motor control. A repository with
freely available scripts for diverse paradigms is the ERP-Core database (Kappenman
et al., 2021). Besides standard experimental paradigms, some studies require a
fic paradigm to be designed from scratch. Examples include current efforts
speci
for experiments to approximate naturalistic stimuli (e.g., video clips), with the aim of
increasing the experiment’s ecological validity. In the same direction, out-of-lab
recordings with real-world tasks have become increasingly common, enabled by
mobile EEG devices (Cruz-Garza et al., 2017). Some examples are sporting activities
(e.g. Cheron et al., 2016 ) or machinery operation (e.g. Borghini et al., 2014).
These
paradigms should be well justified in the theoretical framework to support
their validity for investigating the neurocognitive processes of interest.
8.4.1.2 EEG Index
The EEG index of choice should reflect the psychological or physiological processes
investigation. EEG has the particularity that multiple indexes for analysis can
under
be derived from a single measurement. For example, multiple components can be
analyzed from an ERP waveform, or multiple frequency bands from spectral analysis, all derived from multiple channels (Cohen, 2014; Luck & Gaspelin, 2017). It is
general
ly advised to base the hypothesis on fewer variables that are well supported
by the theoretical framework (Luck & Gaspelin,
to
the study means adding more variables to the analysis, hence increasing the
2017). Adding more EEG indexes
likelihood of spurious findings in one of them by chance. When having more
variables, more stringent statistical criteria would be required during the analysis
phase, which entails the risk of dismissing a relevant observation. Of course, besides
the main hypothesis, further explorative analyses are possible, always using suitable
statistical techniques (Luck & Gaspelin,
2017; Puce & Hamalainen, 2017).
8.4.1.3 Group/Sample
The sample
from the population must be suitable for obtaining the necessary data. In
basic neurocognitive research, participants are most often young, healthy adults,
partly because of their accessibility, but also their suitability to research basic
cognitive phenomena. To reduce influence from external variables, participants are
screened regarding their health and their cognitive and perceptual status (Keil et al.,
2014). Age, gender, and social status of the participants need to be accounted for,
96 F. Cross Villasana
keeping participants in a comparable age range and with balanced gender representation, while also reporting where and how the participants were recruited (Keil
et al.,
2014).
In recent years, numerous calls have been made to increase the diversity of
participants in research samples and improve gender, ethnic, and socia l representation (Niso et al., 2022). It is further argued that since participants most often come
from
communities near universities and research centers, most samples have W.E.I.
R.D. characteristics, standing for Western, Educated, Industrialized, Rich, and
Democratic (Nebe et al., 2023; Niso et al., 2022). Such biased samples can limit
generalizability of the observations to the overal l population. Therefore, calls
the
have been made for greater efforts to increase diversity in the samples and to reach
out to further communities to do research (Niso et al., 2022).
The number of groups and experimental conditions to test is an important factor
to
consider as it affects the kind of statistical tests and the number of trials required to
enhance statistical sensibility (Boudewyn et al., 2018; Puce & Hamalainen, 2017).
different designs can be visualized in Fig. 8.2. A common framework is to test
The
two
or more experimental conditions in a single group, called a within-subjects
design. When two or more groups are compared in a single condition, it is referred to
as a between-subjects design. A combination where more than one group is tested in
more than one condition represents a mixed design. In basic neurocognitive research,
within-subjects desig ns are generally preferred as they reduce intersubject variability. In other words, within-subjects designs have greater statistical power. Withinsubjects designs can further enhance their statistical power by increasing the number
of trials from each participant (Boudewyn et al.,
mixed
designs also profit from having more trials (or otherwise more data), it has
2018). While between-subjects and
been observed that between-subjects mostly enhances statistical power with a greater
number of participants, likely by compensating for intersubject variability
(Boudewyn et al., 2018).
In between-subjects and mixed designs, it is important that the overall demo-
c characteristics of groups such as age, education, and gender balance are
graphi
comparable. This can be visualized in Fig.
match
the participants of the healthy control group with those of the clinical group
8.2. In clinical studies, efforts are made to
(e.g., an educated 30-year-old male patient is paired with an educated male of similar
age in the control group). It must also be reported that the groups do not show
statistically significant differences in these variables to ensure they do not act as a
confound.
The numbe
r of participants in the groups, together with the strength of the
experimental effect, is determinant of statistical power. Power calculators are available to estimate the number of participants required for reliable hypothesis testing
according to an expect ed effect size seen in previous reports. If no previous effect
size is available, power estimation can be based on a conservative estimate of the
expected effect size. Importantly, if EEG is being combined with other techniques or
measures, the effect size to consider for power estimation should be based on the
measure with the weakest effect (Nebe et al.,
2023).
8 Designing Your EEG Study 97
Fig. 8.2 Experimental
designs considering groups
and experimental conditions
and/or points of
measurement
8.4.2 Implementation of the Study
Having identified the strategy and main elements required for testing the hypothesis,
the next step is to implement the strategy efficiently to obtain data of the highest
possible quality. Besides the number of participants, the precision of measurements
is an important contributor to statistical power, especially when the ideal sample size
cannot be reached for practical reasons (Nebe et al.,
tuning of the experimental paradigm and of all settings and procedures for data
collection. Likewise, it is important to keep in mind the requirements of the planned
data processing and statistical analysis, to adjust data collection accordingly (e.g.,
having a suitable baseline length, collecting enough trials).
2023). This requires the fine-
98 F. Cross Villasana
8.4.2.1 Paradigm/Task Implementation
The chosen experimental paradigm needs to be optimized to the current requirements.
The aim is to gather sufficient data for analysis while controlling for confounds. The different parameters that play a role in this are summarized in Table 8.1
and described in more detail below.
In event-related designs, this optimization mainly entails the number of trials. In
iple, more trials improve the EEG signal-to-noise ratio and stabilize random
princ
variability. However, too many trials may exhaust participants and alter the brain’s
responses, introducing a confound. Hence, a balance must be found. One must also
consider that some trials will be lost due to artifacts or due to participant error. The
number of trials per condition is a delicate decision. Useful references for deciding
the number of trials are previous studies with similar designs, as well as specific
literature about the EEG index being used (e.g., P300 component, theta oscillation,
connectivity measures). Some studies have been specifically conducted to assess the
impact of the number of trials on different EEG signals. For example, Boudewyn
et al. (
2018) show complex interactions between the ERP component used, the use of
between-
or within-subjects design, and the effect size to define adequate trial
numbers. But overall, increasing the number of trials improved statistical power.
Borras et al. (2022), with various motor signals, suggest 50 trials as a reasonable
ard, while cautioning that adjustments may be needed for different circum-
stand
stances (e.g., small effect sizes, clinical populations). Both authors advise having a
balance between trial numbers and participant well-being.
The order in which trials from different conditions are presented is also important
to
prevent confounds: If all trials from Condition A are presented first, and all trials
from Condition B come second, it could happen that the state of the participant
Table 8.1 Trial and block checklist
Trial number/amount of data
Suf
ficient to reduce variability and improve signal quality, compensate for loss of trials/data
Not excessive, to avoid overly long sessions
Trial timing
Baseline
Trial length: Captures signal of interest
Intertrial interval: Enough to recede trial aftereffects and jittered to prevent habituation and
expectation
Order of trials
Randomized:
Sequential: If justified by the design: E.g., easy condition first, difficult second
Blocks
Block
Number of blocks: Suitable to collect the required trials or sufficient data
Counterbalancing: Shuffle starting block between-subjects (prevent confounds)
Breaks between blocks: Long enough for the participant to recover
length: Sufficient for the signal of interest
To counter learning and exhaustion confounds
length: Appropriate to keep focus
8 Designing Your EEG Study 99
varies between them, affecting task performance. For example, participants might
not yet be used to the task during Condition A, while they may be tired for Condition
B. Through randomization of the trial order, a balance is achieved where any
learning, exhaustion, or repetition effects affect all experimental conditions equally.
The length of each individual trial should be determined by the task requirements
and the EEG index of interest. This involves the duration of stimulus presentation,
time for the participant to respond, and time for EEG modulations to show. Between
trials or within trials with sequences of stimuli (e.g., cue and target), the timing
between stimuli needs to be considered. The stimulus-onset-asynchrony (SOA) is
the period between the onset points of two stimuli, and the inter-stimulus-interval
(ISI) is the period between the offset of one stimulus presentation and the onset of the
following stimulus. Both parameters are calibrated to prevent the overlap of neural
responses to subsequent stimuli and adjust task difficulty (Cohen,
2014; Luck,
2014). The timing between trials is another factor that affects the measurement
and
the length of the experiment. An intertrial interval (ITI) is necessary for any
aftereffects to recede, and avoid the overlap of neural processes between trials.
Adding a jitter on top of a standard ITI is helpful to prevent habituation and
expectation effects. The precise length of these timing parameters depends on the
paradigm in question, and it is recommended to base these settings on the literature
from the field in question.
In most cases each trial contains a basel ine period before the stimulus or event,
which facilitates quantifying the EEG modulations with respect to a reference
period. The length of the baseline varies depending on the EEG signal. In ERP
studies, baselines of hundreds of milliseconds are common (e.g. Feuerriegel & Bode,
2022), but the ideal baseline length is still debated (e.g. Delorme, 2023; Feuerriegel
Bode,
&
adjus
2022). For time-frequency analyses longer baselines are required, which are
ted according to the wave-length of the oscillation in question, so that baselines
can reach lengths of seconds (Cohen, 2014).
Most neurocognitive experiments are long; therefore, it is necessary to break the
stimul
i presentation into separate blocks with equal trial numbers and introduce
breaks between blocks for participants to rest and recover. During breaks, participants can stop attending to the stimuli, relax, and move freely. Besides individua l
trials, blocks also serve as a way to introduce experimental conditions, for example,
in block designs with continuous measurements, but also in event-related designs.
Counterbalancing refers to a systematic variation in the way participants are
exposed to the experiment’s settings and conditions most often related to the blocks.
For example, in some experimental tasks, the response hand is switched between
blocks as a control measure. Counterbalancing in this case implies alternating the
initial response hand across participants and switching the response in each successive block. Counterbalancing prevents the response hand itself from becoming a
confound. This measure can be used on top of randomization for better variable
control. But in certain designs where randomization is not possible,
counterbalancing can still be used to prevent confounds. For example, an experiment
may require all trials within a block to be Condition A or Condition B without
randomizing, but counterbalancing can still be used to alternate which is the initial
condition for each participant.
100 F. Cross Villasana
8.4.2.2 Measurement Precision
Recording conditions should be arranged to ensure that noise is reduced to a
minim
um, events during the recording are timed with precision, and that the data
contains the signal of interest. This requires setting up the hardware to optimize the
measurement and reduce artifacts, which is described in Chap. 15 (“Getting
Clean
Data: Artifacts and How to Prevent Them). However, the personnel who
operate the devices are just as important. Operators should be well-trained in setting
up the EEG and other devices accurately and in an adequate time frame, so that
suitable data are obtained without exhausting participants (Nebe et al.,
tors also need to monitor all devices during acquisition and make adjustments
Opera
2023).
when necessary, for example, check the cap position and electrode impedance
during block breaks.
The artifact handling strategy must be considered during the study design,
ing the signal of interest, recording situation, and EEG preprocessing pipeline.
includ
To illustrate, one may expect different artifacts from healthy participants in the
laboratory, patients in a clinic, or mobile recordings. According to the situation,
one may consider reques ting healthy young participants to try to reduce their
blinking to avoid this artifact. In contrast, in a clinical study, let patients blink freely
to assure their well-being, and later use blink correction techniques on the data.
Meanwhile, for mobile or patient data, where it is know n that the data will be noisier,
one may opt to record more data/trials compared to standard lab studies, and use
advanced data processing techniques.
Some EEG signals require special controls regarding artifacts, and this must be
consi
dered during design. For example, accurate detection of eye movements is
required when investigating the gamma oscillation, b ecause activation of ocular
muscles generates spikes with energy in the same frequency range (Niso et al.,
2022). Also consider the processing techniques in relation to the signal of interest
recording situation, taking care that the processing applied to the data does not
and
drastically affect the signal (Niso et al.,
2022; Pernet et al., 2020).
8.4.2.3 Experimental Protocol
An experimental protocol specifies the procedures that the operators must follow for
collecti
ng data. It indicates the number of required sessions, and within each session,
it specifies the duration and steps to follow (e.g., welcoming participants, signing
informed consent, recording data, debriefing).
For counterbala
ncing purposes, the protocol must identify factors such as the
experimental conditions that apply to each participant, which block or response
mapping is applied first, or the block order to follow. Requirements for participants
must also be specified. This involves control of substance use, such as caffeine,
alcohol, or cigarettes (e.g., avoid caffeine 2 h before the experiment, in some
laboratories), and accounting for medication status (e.g., on-medication,
8 Designing Your EEG Study 101
off-medication). Other common requirements are having normal or corrected to
normal vision/perception, washing hair, avoiding the use of hair products, and
having good sleep the night before. The time of the day is another important factor,
as circadian rhythms and participants’ routines affect the brain state and performance. As a general rule, working hours between 09:00 and 17:00 are suitable so
participants are not tired or hungry. But optimal timing must be confi
individual participant, considering their individual routines. Chapter
flow and Lab Management”) includes a detailed overview of the nuances of
Work
rmed with each
11 (“Study
implementing an EEG experiment and the day-to-day administration of an EEG
laboratory.
8.4.2.4 Pilot Testing
Running a pilot test that involves all aspects from participant recruitment to data
analys
is is the best way to ensure that everything works as intended before the real
experiment. If a problem was detected at any stage, it is possible to correct and adjust
settings. You can consult Chap. 10 (“Pilot Testing”) for a detailed review of the
g process.
pilotin
8.5 Concluding Summary
Careful study design is critical for the success of a research project. It is always
worth spending sufficient time on ensuring that all elements are in place to guarantee
a successful study. In this chapter, we provided an overview of the most important
factors to consider while designing EEG studies. However, it is always advisable to
review similar studies to the planned study design to get the most detailed information on the requirements specific to the area of study.
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Chapter 9
Applying Statistics in Your EEG Research
Shivakumar Viswanathan
Abstract Electroencephalography (EEG) is used to measure weak electrical poten-
generated on the scalp by the brain’s activity. However, EEG recordings have
tials
several sources of measurement variability due to interindividual differences and the
specifics of measurement. Therefore, the application of statistical inference has a
crucial role in EEG data analysis to detect experimental effects. In this chapter, we
provide an introductory overview of the role of statistical infer ence in an EEG study.
We discuss the sources of uncertainty in EEG measurements and how EEG analyses
are organized to reduce measurement-related error. Finally, we highlight the importance of reducing measurement error, both during acquisition and analysis, and the
value of well-defined hypotheses specific to the EEG experiment.
Keywords EEG · Statistics · EEG analys
is · Measurement error · Hypothesis testing
9.1 Introduction
A major goal of experimental research with electroencephalography (EEG) is to
evaluate hypotheses about brain function. A key element to achieving this objective
involves applying statistics.
This chapter is for readers who are beginning their EEG research journeys and are
faced
with the question: How do I apply statistics in my EEG study? This is an
important question as statistics has multiple roles in a typical EEG study. While
planning a study, statistical considerations help to turn a scientific question into a
structured experiment (see Chap.
data, statistical methods and tests are applied, often extensively, to understand
EEG
observed data patterns and their relevance. The influence of statistics does not end
there. When a study’s findings are published in a scientific article, other researchers
often use the reported statistical details and their graphical visualizations to judge the
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_9
8: “Designing Your EEG Study”). While analyzing
105