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126 P. R. Bazán
10.2.3 Define the Samples Characteristics and Size
for the Pilot Testing
As an initial suggestion, perform your experiment yourself as a participant (Barbosa
et al., 2022). This will provide helpful information and insights. For example, it can
be the case that you imagined that the task would elicit a specific cognitive process;
however, by doing the task, you realize that this is not the case. Also, it is helpful to
consider the experiment from the participant’s perspective, to understand how the
experiment is perceived, and what emotions it can induce. You can get an idea of the
participant’s comfort during the task. Check your comfort when having the EEG
prepared on you, executing the experiment with it, and cleaning up afterwards.
This is just a first step; your personal impression may be very different from that
of
others doing the same experiment. It is therefore useful to have feedback from
research colleagues as they can further discuss the details of the experiment,
considering their scientific proficiency (Ruel et al., 2016). However, it is important
avoid peer pressure. Your colleagues shouldn’t feel obliged to participate in the
to
pilot phase; this should always be voluntary (Barbosa et al., 2022).
You and your lab members are usually more suitable as participants for the initial
steps.
This is valid both for testing the characteristics separately and the first round of
subsequent, more complete steps. After these internal adjustments, the pilot tests
should also involve a small sample of the control and of the experimental
populations to consider their specific context and needs. Then one common question
arises: What should be the sample size for pilot tests? Here it is important to
differentiate a pilot that is meant as a feasibility study of a clin ical trial, for example,
and the iterative pilot testing we are targeting for EEG studies—although in neither
case, it is recommended to have power calculations. In clinical trials, the pilot sample
size can be defined either as a percentage of the sample to be included in the final
study (Eldridge et al.,
2024). However, for internal EEG pilot tests, there isn’t an exact recommendation
sample size. One reason is their iterative nature. The key point is to confirm that
for
all the characteristics of the experiment were tested and to be confident that the
experiment is properly designed. When in doubt, you likely need another pilot
session.
2016) or by using previous studies as reference (Kunselman,
10.2.4 What to Do After the Pilot Testing (Publication
and Preregistration)
Once the study has properly been adjusted and optimized during the iterative pilot
testing, it is time to prepare the publication of the pilot testing results, if it was
designed with that goal. Otherwise, it is a good time to do the preregistration of your
study (Barbosa et al.,
scientific reproducibility. Once the study is preregistered, we are ready to start
ensure
the data acquisition of our study.
2022; Paul et al., 2021). Preregistration is recommended to
10 Pilot Testing 127
10.3 Concluding Summary
In summary, pilot testing is a key phase in any EEG study to improve its quality. It
reduces the risk of improper data acquisition, which would result in useless data. It is
important to bear in mind that pilot testing does not replace other steps in a research
project, but it can be used to optimize the study workflow. Further, a pilot study is
not a small sample version of the main study as its goals are different. The pilot aims
to tune the experiment rather than test hypotheses. Specifically in the context of EEG
studies, pilot testing offers the opportunity to optimize parameters and EEG signal
quality, to ensure that the desired EEG feature can be measured. To do effective pilot
testing, it is important to prepare by considering the characteristics that you want to
test. Aside from checking that the experiments are feasible, it is possible to test a few
different parameters to select the best one. Further, it is important to have clear
criteria to evaluate these aspects. The pilot testing should be seen as an iterative
phase: If an issue is detected, we can go back, adjust the experiment, and test again.
Once the pilot phase is concluded, you will have the tools to do a detailed preregistration of your study, which is highly recommended.
References
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60, 220–232.
Barbosa, J., Stein, H., Zorowitz, S., Niv, Y., Summerfield, C., Soto-Faraco, S., & Hyafil, A. (2022).
A
practical guide for studying human behavior in the lab. Behavior Research Methods, 55,
58–76.
Boudewyn, M. A., Erickson, M. A., Winsler, K., Ragland, J. D., Yonelinas, A., Frank, M.,
Silverstein,
(2023). Managing EEG studies: How to prepare and what to do once data collection has begun.
Psychophysiology, 60, e14365.
Bujang, M. A., Khee, H. Y., & Yee, L. K. (2022). A step-by-step guide to questionnaire validation
research
Eldridge, S. M., Lancaster, G. A., Campbell, M. J., Thabane, L., Hopewell, S., Coleman, C. L., &
Bond,
controlled trials: Development of a conceptual framework. PLoS One, 11, e0150205.
In, J. (2017). Introduction of a pilot study. Korean Journal of Anesthesiology, 70, 601–601.
Kunselman, A. R. (2024). A brief overview of pilot studies and their sample size justification.
Fertility
Lancaster, G. A. (2015). Pilot and feasibility studies come of age! Pilot and Feasibility Studies, 1,
–4.
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clinical
Lewis, M., Bromley, K., Sutton, C. J., Mccray, G., Myers, H. L., & Lancaster, G. A. (2021).
Determining
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Gurr, C., Hilger, K., Jawinski, P., Kulke, L., Lischke, A., Markett, S., Meier, M., Merz, C. J.,
Popov, T., Puhlmann, L. M. C., Quintana, D. S., Schäfer, T., Schubert, A. L., Sperl, M. F. J.,
S. M., Gold, J., Macdonald, A. W., Carter, C. S., Barch, D. M., & Luck, S. J.
. Institute for Clinical Research (ICR), National Institutes of Health.
C. M. (2016). Defining feasibility and pilot studies in preparation for randomised
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research. Journal of Psychiatric Research, 45, 626–629.
sample size for progression criteria for pragmatic pilot RCTs: The hypothesis test
Reutter, M., Baker, D. H., Bölte, J., Domes, G., Gamer, M., Gärtner, A., Gießing, C.,
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Vehlen, A., Lonsdorf, T. B., & Feld, G. B. (2023). Enhancing precision in human neuroscience.
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Mills, N., Moore, G., & Donovan, J. L. (2015). Maximising the impact of qualitative research in
feasibility studies for randomised controlled trials: Guidance for researchers. Pilot and Feasi-
bility Studies, 1, 1– 13.
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Chapter 11
Study Workflow and Lab Management
Tracy Warbrick and David Kadlec
Abstract Running a successful EEG lab relies
on clear communication, documentation, and sharing of resources. This chapter outlines strategies for establishing a lab
management plan to ensure the smooth operation of your lab and that it produces
high-quality research. On an individual level, a researcher needs to be organised,
consistent, and thorough in their approach. We provide tips for planning workflows
for before, during, and after a measurement. We also highlight the importance of
keeping your own lab notebook to accurately and reliably document your work.
Keywords Measurement checklist · Consistent procedures · Replicability ·
Transpar
ency · Lab notebook · Lab logbook
11.1 Introduction
The scientific community has a respon sibility to conduct transparent and reproducible research. Effective lab management and consistent study workflows are essential
to high-quality EEG studies. Establishing repeatable study procedures, welldocumented protocols, and consistently monitoring equipment use benefits not
only individual researchers but also research groups, and ultimately the published
scientific knowledge base. This chapter covers tips for lab management, keeping
your own personal lab notebo ok, and planning study workflows.
T. Warbrick (*) · D. Kadlec
Brain Products GmbH, Gilching, Germany
e-mail:
tracy.warbrick@brainproducts.com; david.kadlec@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_11
129
130 T. Warbrick and D. Kadlec
11.2 Lab Management
To support the effective operation of your lab, it is essential to implement a lab
management plan and to designate a responsible person. This is particularly important when multiple experiments are running and when facilities are shared by many
users. This not only helps general organisation and scheduling, but also facilitates
equipment monitoring, identifying equipment overuse, underus e, and devices prone
to problems.
Effective lab management is also integral to transparent and reproducible
resear
ch. Yet these practices are often overlooked in the scientific community
(Baker, 2016) and as researchers we must strive to improve. While commitment is
needed from the whole community for systemic change, as individuals we can
contribute by establishing clear protocols for our own research. This will enable
better record keeping, project management, data sharing, and information dissemination both within research groups and across the wider scientifi c community
(Monaghan et al., 2023).
If resources permit, appointing a lab manager can optimise the productivity and
efficiency of the lab. In the absence of an official lab manager position, assigning a
responsible person is a good use of resources. Lab users are more likely to follow
procedures if someone is made accountable for running the lab. It also provides a
contact point for all lab users, making them more likely to follow the correct
procedures and report problems.
Below, we outline key points for establishing policies and procedures that will
help your lab run smoothly.
11.2.1 Admin and Organisation
. Use a transparent booking system, including who has priority (if appropriate).
. Manage the budget: who is responsible for this, what is the procedure for
reques
ting equipment?
. Establish methods for sharing information among lab users, e.g. wikis, logbooks,
mailin
g list, chat group.
. Consumables (e.g. gel, syringes, tape): How to monitor use and decide when they
ordering. Who orders them?
need
. Establish data backup procedures.
. Ensure that all users understand and comply with established procedures and
policies.
. Have clear emergency procedures, e.g. what happens if the fire alarm sounds,
who
is responsible for participants, and making sure the lab is cleared?
. Users shoul
hesitation. For example, a broken electrode, interruption of data recording, or
allergic reactions to gel or tape (rare).
d promptly report any system malfunctions or incidents without
11 Study Workflow and Lab Management 131
. Establish a procedure for training new people. New lab users need to be familiar
with the lab procedures as well as how to use the equipment appropriately.
. Encourage all lab users to have a written study protocol for all studies.
. Have a recommended pilot testing procedure for new studies.
11.2.2 Hardware and Software Maintenance
. Keep a usage log to track how often the equipment is being used.
. Establish a procedure for reporting or logging problems and damage. Also
consi
der associated procedures for initiating repairs.
. Schedule regul ar function checks, especially for equipment that is frequently used
or used by multiple users.
. Keep software up to date. Also consider when to do this in relation to running
studies.
If in doubt check with the manufacturer.
. Have standard cleaning and disinfection procedures.
11.2.3 Lab Logbook
Keep a lab book to record all measurements. This should be clear and easy to
complete, e.g. a table of basic information for each measurement. See Table 11.1
for an example of information to include.
Electronic lab notebooks (ELNs) mig ht be more suited to some studies than paper
ns, e.g. those that require increasingly large data volumes, data complexity,
versio
Table 11.1 Example of simple information that could be included in a lab logbook
Date and time
Project name
Principal investigator’s name
Experimenter name and contact details
Recording number in the present study To track how many recordings per study/research group
Equipment used Amplifier: (serial number or lab label)
or lab label)
label)
Equipment status
Consumables used
Consumables status
Has the data
Additional notes
been archived?
Battery: (serial number
EEG cap: (serial number or lab
notes Battery charged after use?
notes Is anything
Any malfunctions or damage?
running out?
Order initiated?
132 T. Warbrick and D. Kadlec
Fig. 11.1 The role an electronic lab notebook (ELN) can play in the research data life cycle.
(Vandendorpe et al.,
2024. Reproduced under Creative Commons CC BY 4.0. Unaltered)
accessibility, and traceability (Higgins et al., 2022). An ELN can be a very useful
tool for the whole research process (see Fig.
11.1). However, implementing one is
not trivial, and you should invest some time in choosing one that meets your
requirements (Vandendorpe et al., 2024).
Regardless of the method chosen, it should be easily accessible for all users;
people are more likely to compl y with simple, transparent procedures. Solutions can
be as easy as having an online booking calendar, a paper lab book, and displaying
procedures in the lab or as complex as implementing an ELN for the whole study
process. The essential requirements are to establish clear working procedures and to
assign a person responsible for their implementation.
11 Study Workflow and Lab Management 133
11.3 Keep Your Own Lab Notebook
Most labs will have a shared lab book or measurement logbook (see Sect. 1.2 ), but it
is also considered good scientific practice to keep your own lab notebook. A lab
notebook can serve as tool for planning and organising your work, recording your
work, and helping to troubleshoot or diagnose recurring problems, especially when
communicating with the manufacturer’s support team. It can even have a role in
protecting any intellectual property that comes from your research.
Traditionally, scientists kept paper notebooks which allow flexibility in content
and
organisation. However, paper notebooks have some drawbacks in terms of data
retrieval and data sharing; they can also be misplaced leading to a complete loss of
the research record. Electronic lab notebooks can avoid these potential problems
through searchable indices, easier data sharing, and automatic backup and archiving
(Wright,
resear
establish a paper or electronic approach that fits your needs and preferences.
ments. You
. Pilot testing plans, results, and adjustments.
. Ideas for your next studies and what stimulated them.
. Possible improvements to the current procedures and workflows.
. Notes from discussions with advisors and peers.
. Measurement information
2009). It could be that your university or lab has implemented an ELN,
chers are advised to check whether such a resource is available. If not, you can
The specific content and structure of your lab book will depend on your require-
could consider including the following:
– Similar information to the lab notebook plus additional details that could be
useful
(impedance okay? noisy channels?), anything unusual during recording
(e.g. excessive movement), anything of note during the debrief?
– Much of this information overlaps with that recorded in your study workflow
checkl
ipant’s data. Your lab notebook provides a central record that you have close at
hand when you’ re back at your desk analysing data or writing your paper.
for you during analysis, e.g. how did the cap preparation go
ist (see below), but those checklists are usually stored with each partic-
11.4 Study Workflow
Although listing tasks and their order might seem obvious, it can play a crucial role
at multiple stages of your study (Boudewyn et al.,
study
workflow establishes good scientific practice, keeps everyone informed, and
promotes accountability for running a study. At the individual level, a study
workflow or checklist supports effective planning and execution. During planning,
it ensures that all necessary steps are covered and helps to estimate how long each
measurement wi ll take. It can also help you identify what can be done before your
participant arrives to save you and them time. When executing the study, having a
2023). At the lab level having a
134 T. Warbrick and D. Kadlec
workflow ensures that no steps are forgotten. This is particularly useful when
multiple researchers are involved; it brings consistency to the measurements by
making sure everyone follows a standard procedure. After the measurement, the
workflow serves as a reminder to ensure that paperwork is complete, equipment is
cleaned and ready for the next recording, and data are stored appropriately.
Below is an example of a workflow checklist that is organised into pre, during,
and post measurement phases. This framework can serve as a starting point for your
own study workflow. We recommend completing a checklist every measurement
and storing with the participant’s data (digi tal or paper).
11.4.1 Pre-me asurement
The pre-measurement part of the study is as important as the measurement itself.
With a little planning you can reduce the likelihood of problems occurring and
ensure a smooth measurement for you and your participant.
. During participant recruitment
– Send screening forms ahead of scheduling a measurement, especially if you
have
any exclusion criteria. It would be unfortunate to have to exclude a
participant once they have arrived at the lab.
– It’s useful to ask for the circumference of the participant’s head so you can
e a cap before they arrive. You should provide instructions for how to do
prepar
this, so they measure it at the correct point. A short document or graphic will
be sufficient.
– Provide full information about the study. Having someone drop out on the day
of
the study because they weren’t fully informed is inconvenient. The participant should know what will happen on the day, what they will have to do, and
how long it will take.
. Day before measurement
– Charge batteries (where applicable).
– Send a reminder email to the participant, including where and when to arrive,
to prepare for their visit to the lab, e.g. no hair products, no coffee or other
how
substances (if relevant for your study).
– Prepare consumables: gel, tape, syringes, etc. If anything is running low, let
your
lab manager know or order some more.
. Day of measurement—before the participant arrives
– Check temperature and humidity in the recording room and adjust when
needed.
can influence your participant’s comfort and perhaps their ability to focus on
the task.
– Prepare digi
Sweating can influence your EEG data and being too hot or too cold
tal or print versions of the consent form and questionnaires.
11 Study Workflow and Lab Management 135
– Prepare cap (if size is known). If you are using a sponge based-electrode
system soak your net. If your cap has removable electrodes, pre-populate cap.
– Start your recording software and open the correct recording workspace.
– Start your experimental software and open your task (where needed).
– Consider having a dummy version of your experiment that you can run to
triggers.
check
11.4.2 Measurement
It’s essential that you follow a standard procedure for your measurements. You want
to avoid forgetting any steps or introducing confounds to the experiment because
you did something different for some of the measurements.
. Participant preparation
– Complete the informed consent procedure.
– Complete pre-test questionnaires (if relevant).
– Put the EEG cap/net/headset on the participant and place any additional
s.
sensor
– Do an impedance check and reduce impedance to the target level.
– Check data quality.
– It can be helpful to show the participant the effect of their behaviour on the
data.
For example, ask them to blink and clench their jaw and point out how
the signal changes.
– Explain the task. Alternatively, this could be done prior to the cap preparation.
– Perform a practice run of the task if appropriate for your study. You can also
this as an opportunity to check the triggers.
use
. During the measurement
– Start the recording! This sounds obvious, but it’s a mistake people do make.
– Check that triggers are arriving when your paradigm is running.
– Monitor participant’s state and whether breaks might be needed.
. Ask how they’re doing in between runs.
. Keep an eye on the data quality, e.g. are there movement artifacts that could
indicate
– Monitor the data and make a note of anything that might affect your analysis or
that
very noisy, perhaps you could check the impedance at the next suitable time.
– Document
was an unusual amount of movement artifact, channel X had a bad signal from
a certain time point.
the participant is uncomfortable?
you migh t want to address immediately, e.g. if an electrode has become
anything unusual. For example, the participant blinked a lot, there