Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_6027_Библиотеки_им_академика_М_И_Перельмана.pdf
7 Basic Time Concepts in EEG Practice 83
Fig. 7.3 (a) Activity based
signal magnitude at
on
different time-points. At
time-points before ~60 ms,
the signal magnitude is close
to zero (examples shown by
arrows) but deviates away
from zero from
~60 to 130 ms (examples
shown by arrows). (b) The
signal magnitude at single
time-points is close to zero
and deviates away from zero
in quick succession
(examples shown with
arrows). (c) The maximum
signal magnitude over short
time-intervals (examples
highlighted in gray) reveals
a coherent pattern across the
entire signal duration (thick
black line)
from ~60 to ~130 ms before returning to zero. This pattern supports a general
interpretation that there is higher activity between ~60 and ~130 ms than at other
time-points.
The above interpretation based on time-points is challenged when we consider
7.3b. The signal deviates away from zero and returns close to zero at multiple
Fig.
time-points over the entire range. The size of these deviations also varies repeatedly.
This scenario does not provide a coherent view of activity as in Fig.
rathe
r than considering the signal values at single time-points, consider the signal
7.3a. However,
changes over time-intervals, that is, across multiple neighboring time-points
(highlighted areas in Fig. 7.3c). When we consider the maximum deviation in each
of
these intervals over the entire signal duration (thick black line), we observe that
the activity shows a coherent pattern that is first high before ~60 ms, with a decrease
from ~60 to ~130 ms, followed by an increase again.
These two
views of activity changes over time are not mutually exclusive, where
one is correct and the other incorrect. Instead, they both expand how the concept of
84 S. Viswanathan
activity is practically analyzed. A time-point specific formulation can be appropriate
in many scenarios and is the basis for the event-related potentials approach
(Chap.
be
signa
19, event-related potentials). However, an interval-based formulation can
powerful when the signal of interest is an oscillation, as in Fig. 7.3b, where the
l periodically completes a full transition from positive to negative (i.e., one
cycle) and completes k cycles every second (a frequency of k Hertz (Hz)) (see
18: “I
Chap.
ntroducti
on to EEG Oscillations and Spectral Analysis” ).
In summary, the flexibility in formulating activity changes over time is possible
due to the high time-resolution of EEG. This high time-resolution supports analyses
based on single time-points (e.g., every 4 ms with a sampling frequency of 250 Hz)
as well as over time-intervals ranging from tens of milliseconds to several seconds.
7.5 Conclusion
In this chapter, we have provided you with an overview of basic time concepts in
EEG with a focus on their practical implications. EEG is often referred to as having a
high time-resolution. However, the full impact of this feature can be underappreciated. A key takeaway from this chapter is how the high immediacy of EEG
measurements can influence practical decisions about how EEG is formulated and
analyzed, such as segmentation and time-locking. Importantly, immediacy is a
property of the measurement modality and is not linked to a particular experimental
design. fMRI also involves continuous recordings and has to address similar concerns about extracting trial-specific measures. However, due to fMRI’s low immediacy, options such as segmentation and time-locking are unavailable. Therefore, in
conclusion, considering the role of time carefully in your EEG study can maximize
the value provided by EEG as an approach to study brain funct ion.
References
Arthurs O, B. S. (2002). Neural origins of the fMRI BOLD signal. Trends in Neurosciences, 25,
27–31.
Blom, T., Feuerriegel, D., Johnson, P., Bode, S., & Hogendoorn, H. (2020). Predictions drive neural
representations
National Academy of Sciences of the United States of America, 117, 7510–7515.
Buzsáki, G., Anastassiou, C. A., & Koch, C. (2012). The origin of extracellular fields and
currents—EEG, ECoG, LFP and spikes. Nature Reviews. Neuroscience, 13, 407–420.
Cisek, P. (2007). Cortical mechanisms of action selection: The affordance competition hypothesis.
Philosophical
1585–1599.
Cohen, M. X. (2017). Where does EEG come from and what does it mean? Trends in Neurosci-
Donoghue, T.,
40, 208–218.
ences,
studying neural oscillations. European Journal of Neuroscience, 55, 3502–3527.
of visual events ahead of incoming sensory information. Proceedings of the
Transactions of the Royal Society of London. Series B, Biological Sciences, 362,
Schaworonkow, N., & Voytek, B. (2021). Methodological considerations for
7 Basic Time Concepts in EEG Practice 85
Falkenstein, M., Hoormann, J. R., Christ, S., & Hohnsbein, J. (2000). ERP components on reaction
errors and their functional significance: A tutorial. Biological Psychology, 51, 87– 107.
Gehring, W. J., Goss, B., Coles,
negativity. Perspectives on Psychological Science, 13, 200–204.
Gratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., Nelson,
M., Coalson, R. S., Snyder, A. Z., Schlaggar, B. L., Dosenbach, N. U. F., & Petersen, S. E.
S.
(2018). Functional brain networks are dominated by stable group and individual factors, not
cognitive or daily variation. Neuron, 98, 439–452.e5.
Hommelsen, M., Viswanathan, S., & Daun, S. (2022). Robustness of individualized inferences
from
Keil, A., Bernat, E. M., Cohen, M. X., Ding, M., Fabiani, M., Gratton, G., Kappenman, E. S., Maris,
Kelly, S. P., & O’connell, R. G. (2013). Internal and external influences on the rate of sensory
King, J. R., & Dehaene, S. (2014). Characterizing the dynamics of mental representations: The
Kornhuber, H. H., & Deecke, L. (1964). Hirnpotentialänderungen beim Menschen vor und nach
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001). Neurophysiological
Luck, S. J. (2005). An introduction to the event-related potential technique. MIT Press.
Pastor-Bernier, A., & Cisek, P. (2011). Neural correlates of biased
Pfurtscheller, G., & Lopes Da Silva, F. H. (1999).
Ploran, E. J., Nelson, S. M., Velanova, K., Donaldson, D. I., Petersen, S. E., & Wheeler, M. E.
Raichle, M. E. (2015). The brain’s default mode network. Annual Review of Neuroscience, 38,
Viswanathan, S., Wang, B. A., Abdollahi, R. O., Daun, S., Grefkes, C., & Fink, G. R. (2019). Freely
Viswanathan, S., Abdollahi, R. O., Wang, B. A., Grefkes, C., Fink, G. R., & Daun, S. (2020). A
Wessel, J. R. (2012). Error awareness and the error-related negativity: Evaluating the first decade of
Williams, D. W., & Sekuler, R. (1984). Coherent global motion percepts from stochastic local
Wolff, M.
longitudinal resting state EEG dynamics. European Journal of Neuroscience, 56, 1– 32.
E.,
Mathewson, K. E., Ward, R. T., & Weisz, N. (2022). Recommendations and publication
guidelines for studies using frequency domain and time-frequency domain analyses of neural
time series. Psychophysiology, 59, e14052.
evidence
temporal
Willkurbewegungen,
P ügers Archiv, 281, 52– 52.
investigation
Journal of Neuroscience, 31, 7083–7088.
desynchronization: Basic principles. Clinical Neurophysiology, 110, 1842–1857.
(2007).
nition processes using fMRI. The Journal of Neuroscience, 27, 11912–11924.
433
chosen
kinematics-informed EEG. NeuroImage, 188, 26– 42.
response-locking
Mapping, 41, 1
evidence.
motions.
working-memory-guided behavior. Nature Neuroscience, 20(6), 864–871.
accumulation in the human brain. The Journal of Neuroscience, 33, 19434–19441.
generalization method. Trends in Cognitive Sciences, 18, 203–210.
of the basis of the fMRI signal. Nature, 412, 150–157.
Evidence accumulation and the moment of recognition: Dissociating perceptual recog-
–447.
and instructed actions are terminated by different neural mechanisms revealed by
protocol to boost sensitivity for fMRI-based neurochronometry. Human Brain
– 19.
Frontiers in Human Neuroscience, 6, 1– 16.
Vision Research, 24, 55– 62.
J., Jochim, J., Akyürek, E. G., & Stokes, M. G. (2017). Dynamic hidden states underlying
M. G. H., Meyer, D. E., & Donchin, E. (2018). The error-related
dargestellt mit Magnetband-Speiche- rung und Ruckwartsanalyse.
competition in premotor cortex.
Event-related EEG/MEG synchronization and
Part II
Experimental Design and Good Scientific
Practice
Chapter 8
Designing Your EEG Study
Fernando Cross Villasana
Abstract Numerous factors influence the
research project. The motivations for doing a study may vary: for example, answering a question left from a previous experiment, exploring the variables involved in a
novel field, or perhaps devising a practical application. These different aims have
profound effects on the way a research question is posed, how the hypothesis is
derived, the experimental protocol, and the methods used for data analysis. In EEG
studies, designing a study also requires consideration of resource availability,
participant welfare, and precision of the measurements. Together, these factors
guide the design of studies that aim to generate new and reliable knowledge. This
chapter aims to present an integral perspective on study design, while highlighting
the various nuances to consider at each stage of the process.
Keywords Experimental design · Study design · Experiment · EEG · ERP
design and implementation of a new
8.1 Introduction
This chapter presents a comprehensive overview to guide the reader through the
various stages involved in designing an electroencephalography (EEG) research
study. However, each of the topics presented is much more extensive than can be
covered here. Readers who require a deeper review of each topic can refer to the
literature cited for further detail. References to other chapters in this book are also
included. A summary of the design process can be visualized in the following
Fig.
8.1.
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_8
89
90 F. Cross Villasana
Fig. 8.1 Overview of the study design process
8.2 Setting a Research Question
The aim of research is to address gaps or uncertainties in the current state of
knowledge in a particular subject. Phrasing the gap in knowledge as a question is
a first step toward narrowing the scope of a research project, defining testable
hypotheses, and identifying the appropriate resources to implement the study
(Malik & Amin,
question is crucial to the success of a study (Farrugia et al.,
2009).
2017; Ratan et al., 2019). Therefore, the proper setting of a research
2010; Thabane et al.,
8 Designing Your EEG Study 91
The source of a research question is influenced by the context and experience of
the researcher. In basic resear ch, the question often emerges from the results of a
previous study or from currently open questions withi n a field (Malik & Amin,
2017). In applied or clinical research, a problem is first identified, then refined
ch questions are devised with the goal of improving practical outputs (Ratan
resear
et al.,
2019; Thabane et al., 2009). The research question can also result from an idea
that
emerged from the researcher’s own experience, personal interests, or from
letting imagination roam (Thabane et al., 2009).
Deep knowledge of the topic at hand is crucial for forming an informed research
quest
ion (Farrugia et al.,
s and put together into a theoretical framework that will specify the concepts
source
2010). This knowledge can be gathered from different
behind the current project (Kivunja, 2018). An important initial step is to perform a
revie
w of the literature to identify the current trends, methods, technologies, and
open questions in the field (Fandino,
to
Evaluate an EEG Research Paper”) contains insights on how to extract the most
2019; Farrugia et al., 2010). Chapter 36 (“How
relevant information from scientific literature. Systematic reviews and overviews of
reviews (meta-reviews) are excellent sources to learn the field’s broad perspective
and current state of the art (Hunt et al.,
original
studies that are relevant to the aims of the current research project. It is
2018). Reviews can also point toward the
important to consider one’s own limitations and biases when collecting and organizing information (Niso et al.,
2022). In this sense, exchange with peers (Niso et al.,
2022) and interviews with experts or mentors can be very helpful for refining the
resear
ch question (Farrugia et al., 2010; Thabane et al., 2009). Experts can provide
orien
tation through the literature and share their firsthand experience on the practical
details when researching a particular subject. In applied and clinical contexts, field
investigation with the population is particularly useful to identify relevant issues
from daily practice that research can address (Farrugia et al.,
2010; Thabane et al.,
2009).
When building the theoretical framework, a critical analysis of the state of the art
will help to refine the research question. How complete is the knowledge to date? Is
there an existing theory that clarifies the current research question, or are there
perhaps competing theories? Are there only isolated observations and disconnected
ideas? What are the current uncertainties? Likewise, when checking singular
research reports, consider methodological concerns to further inform the research
question: Was a particular sample too small? Were there confounding factors? Were
there issues during data collection or processing? Do you detect logical fallacies in
the interpretation of results? Further insights into the critical reading of scientific
literature can be found in Chap.
The setting
of the research question is a dynamic process where an initial question
can be adapted as more information is gathered (Doody & Bailey,
36.
2016; Haynes,
2006). Besides topic-specific information, elements like the feasibility of implemen-
tation,
resource availability, institutional support, or ethical concerns also have
influence in shaping the research question (Fandino, 2019; Ratan et al., 2019).
Formu
lating the research question can be challenging. To aid the question setting
process, various guidelines have been propos ed (e.g. Fandino, 2019; Farrugia et al.,
92 F. Cross Villasana
2010; Ratan et al., 2019; Thabane et al., 2009). For example, the FINER framework
provides criteria for assuring the quality of a research question, so that it is Feasible,
Interesting, Novel, Ethical, and Relevant (Farrugia et al.,
2009). Guidelines often include checklists to ensure that the research questions
y with the criteria (e.g. Fandino,
compl
lines can be a helpful tool for clarifying the research question. It is worth searching
for a suitable guideline that applies to the particular research field at hand.
2019). In this way, question-setting guide-
2010; Thabane et al.,
8.3 Setting a Hypothesis
8.3.1 Defining Hypotheses
Once a research question is in place, it is furt her narrowed into a series of hypotheses. A hypothesis can be defined as a statement that proposes an explanation for a
phenomenon. It is a proposed answer to the research question and serves as a basis
for empirical testing (Bulajic et al.,
esis mostly involves a prediction about how a particular electromagnetic
hypoth
index relates to a specific condition, such as an experimental treatment, group
membership, or site of recording (Keil et al.,
“
Does the resting state alpha power affect subseq uent task performance?” a hypothesis can be derived: “Greater resting alpha power correlates with increased performance in a following task.” During the research study, evidence is collected that can
be in favor of or against the hypothesis. Therefore, the hyp othesis is the main guide
for what data will be collected and how it will be analyzed (Keil et al., 2014;
Thompson & Skau, 2023).
Hypotheses necessarily contemplate a relationship between variables (Thompson
&
Skau, 2023). In experimental research, one variable(s) is manipulated to produce
an
effect on another variable(s) that is measured by the experimenter. The manipulated variable is known as the independent variable (IV), while the affected variable
is the dependent variable (DV). For example, in the hypothesis “Greater task
difficulty produces greater mid-line theta amplitude,” task difficulty is the independent variable that is manipulated by the experimenter. Meanwhile, the amplitude of
midline theta induced by the task is the dependent variable.
Not all studies are experimental, and other approaches to research can be
implemented. In correlational research, no variable is manipulated; rather, the
relationship between co-occurring variables is investigated. A hypothesis of this
type can be: “Alpha peak frequency increases with age between childhood and
adolescence”. In this case, neither alpha peak frequency nor the ageing of the
participants is deliberately manipulated, but both are measured in search of a
correlation between them.
In explor
hypothesis with the aim of identifying relevant variables. Since exploratory studies
involve testing multiple varia bles without a hypothesis, stricter statistical controls
atory studies, measurements can be first performed without a specific
2012; Jeong & Kwon, 2006). In EEG, a
2014). As an example, for the question
8 Designing Your EEG Study 93
are necessary to prevent outputs produced by chance from being perceived as
meaningful observations (Luck & Gaspelin,
times,
incidental findings are encountered during experimental or correlational
2017; Szucs & Ioannidis, 2017). Some-
studies that were not predicted by the hypothesis. Any new hypothesis derived
from observations during the analysis phase should be regarded as exploratory and
receive the corresponding statistical controls. This prevents making an interpretation
of observations that potentially resulted from chance (Luck & Gaspelin,
Hamalai
&
nen, 2017).
2017; P
uce
8.3.2 Testing the Hypothesis
To test a hypothesis using statistics, the most common approach is null hypothesis
testing. In this approach, the experimental hypothesis must be accompanied by a null
hypothesis (H
ables. When paired with the null hypothesis, the experimental hypothesis becomes
the alternative hypothesis (H
evidence against the proposal that there is no relationship between the experimental
variables (Pernet, 2015). Rejecting the null hypothesis does not automatically mean
that
the alternative hypothesis is true (Pernet, 2015; Szucs & Ioannidis, 2017).
However, it indicates that a deeper investigation is needed into the relationship
between the variables. This should be based on the theoretical framework and
statistical analyses (Luck & Gaspelin, 2017; Szucs & Ioannidis, 2017).
Null hypothesis testing is a widely used method for scientific inference in
cience and related fields. However it has limitations and has received criti-
neuros
cism, mostly regarding misunderstandings about the meaning of rejecting the null
hypothesis, and the statistical criteria used for rejection (Szucs & Ioannidis,
This
has led to a series of recommendations and complementary approaches to
strengthen null hypothesis testing. From an experimental design perspective, recent
years have seen an emphasis on pre-registration of experiments (Niso et al.,
this way, the public can identify the original hypotheses, the theoretical background,
and the intended methodology. Great emphasis has also been placed on doing
replication studies to strengthen previous observations and facilitate new predictions
(Luck & Gaspelin,
researchers to confirm and aggregate results from previous studies (Nebe et al.,
other
2023). Having a solid theoretical background with clear predictions is also important
to support arguments in favor of the experimental effects after rejecting the null
hypothesis (Luck & Gaspelin, 2017; Szucs & Ioannidis, 2017). The former proposal
s are accompanied by recommendations for statistics, such as using power
estimation to find an optimal sample size, and reporting the effect size of the results
(Keil et al.,
), which proposes that no relationship exists between the given vari-
0
). Testing the null hypothesis requires gathering
A
2022).
2017; Nebe et al., 2023; Niso et al., 2022). Data sharing allows
2014; Niso et al., 2022; Szucs & Ioannidis, 2017).
2017).
In
94 F. Cross Villasana
8.4 Design of the Study
With the alternative and null hypotheses set in place, it is time to design a study
where they can be put to test. The aim is to fine-tune all factors involved in data
collection to obtain the clearest possible evidence for testing the hypotheses. Importantly, all procedures decided upon must comply with ethical guidelines (e.g.,
country regulations) and be approved by the local ethics committee.
The nuances of experimental design can be complex. However, the key point is to
it possible to attribute the effects observed on the dependent variable to the
make
manipulation of the independent variable. In other words, the potential effects of
other variables, such as noise in the recordings, length of the experiment, participant
age, or random variation, should be minimized through effective experimental
design. These additional variables that affect the measurement are known as
confounds.
In EEG studies, the factors to fine-tune are many indeed. These include elements
such as the criteria for participant selection, sample size, number of trials, hardware
settings, data processing methods, or participant well-being. Often, a decision
regarding one factor will affect other factors, which is why fine-tuning them frequently involves trade-offs. Each research study is unique, requiring its own set of
decisions during its design. A good initial source of information when designing a
study is found in previous publications using similar methods. Additionally, consulting sources on good scientific practice (e.g. Niso et al.,
lines (e.g. Keil et al., 2014), and literature about the EEG index used in the study can
guide
well-informed decisions during experiment al design. In the following, we will
cover the main aspects to consider.
2022), technical guide-
8.4.1 Contextualization of the Hypothesis
The main questions at this point are how to generate the evidence necessary to test
the hypothesis, what strategies to follow to obtain the data, and how the data
generated will be analyzed.
8.4.1.1 Experimental Paradigm
The choice
be recorded, should be suitable to assess the neurocognitive process under investigation. There are two main kinds of paradigms used in human neuroscience. In
neurocognitive studies, the most common kind by far is the event-related-design,
where brain activity is analyzed around the occurr ence of a discrete event, such as
the presentation of a sensory stimulus, the execution of a response, or other actions.
This design is useful to track the fast pace of brain responses during perception and
of experimental task or paradigm, and the situation in which the data will