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14 Triggers 169
Value
range
Decimal
value
0
Bit
weight
Bit
number
Value
range
Decimal
value
0
Bit
weight
–127
–15 0 2
–1
16
32
64
4
–15 4 2
252
10
0
1 5
2 6
3 0
Table 14.1 Three examples of how to divide and group 8 bits: One single group of 8 bits to decode 0–255 values, two groups of 4 bits to decode values of 0–15
each, and a 7-bit group with a single bit to decode 0–127 values and an on/off state
Bit
number
–255 0 2
Value
range
Decimal
value
Bit
weight
Bit
number
422422
0
1 1 1 2 2 2 2 4 3 3 3 8 4
0210 10 10
22
32832832
12212212
trigger codes can be sent without interfering with each other, but the reduced number of bits results in smaller value ranges
16 0 2
32 1 2
64 2 2
128 3 2
5
6
7
72 80210
62 462
42
52
Single 8-bit group Two 4-bit groups 7-bit and 1-bit group
With more groups, more independent
per group
170 A. Kreilinger and P. R. Bazán
. Sometimes it is not enough to get triggers from one source only. In a typical
experiment, the software tool is used to provide the stimuli and the participant’s
task is to respond. This can be done via a button press on the computer’s
keyboard, but also with a button that can directly create a trigger. The response
can also be directly caused by the brain (neurofeedback or BCI). Alternatively,
changes in the environment can be used as responses as well. For example, after
exceeding a predefined pressure threshold on a force sensor or a brightness
threshold on a photo sensor. As mentioned previously, it is crucial to take care
that these different trigger modalities do not interfere with each other. This has to
be done either on a software or hardware level, or on both.
. Another important decision is how to deliver the triggers on the hardware side.
Triggers
can be sent via a dedicated cable. For example, from the parallel output
of a computer or from a dedicated trigger device to the trigger-in port of the EEG
amplifier system or a dedicated device used for receiving trigger signals.
Connecting cables comes with the burden of less mobility, however, this way
the transmission is most reliable. Triggers can also be sent wi relessly. Here,
triggers can be lost due to the transmitter and receiver going out of range, or
that heavy environmental noise causes interference and bad reception. In this
case, it is good practice to use redundancy when sending trigger codes: instead of
sending only a single trigger, it is better to send the trigger multiple times or in
predefined sequences. This way, if one or more triggers are lost, the information
can be recovered by analyzing the context.
. In some cases, it can become necessary to deal with the underlying characteristics
trigger signal itself. When using an analog signal, it should be verified that
of the
the signal fulfills the requirements for causing a state change at the input if it
toggles between high and low states. When using transistor-transistor logic
(TTL), for example, there are certain thresholds above or below which an input
signal is classified as low or high, but there are variations of TTL where
thresholds may differ. Of course, sending electrical signals is not the only way
to send triggers. Optical signals are also an alternative, for example in environments where long electric cables are not desirable or even prohibited (e.g., in
magnetic resonance (MR) environments). In other words, the user may have to
consider such things when the recording solution does not come as a ready-to-go
complete package.
. One should
also think about how the nature of the trigger signal itself is used to
encode information: the onset of a trigger can be linked to the falling edge when
the previous state is high and then becomes low during the trigger pulse (when the
event happens), and the state returns to high after the pulse width; the idle state
could be low and a trigger could be encoded at the rising edge of a short high
pulse; or, the trigger mode could be configured as a toggle. In this case, each
rising or falling edge is used as a trigger, see Fig.
14.3.
14 Triggers 171
Fig. 14.3 Trigger pulses. From left to right, the triggers shall be generated on: the rising edge,
caused by a stimulus presentation software tool that sets the idle level to low and sends a short high
pulse to indicate an event; falling edge, where a button is pressed, pulling a high level to low for a
short time; on both edges, where each level change is linked to an event
14.7 Using Triggers for Timing Verification
Based on the analys is of the exact timeline of an experiment, we can gain a lot of
information that is directly relevant for assessing ERPs and for making sure that the
setup is working as inte nded. In this final part of the chapter, we will describe an
experiment that can be used to verify the timing between triggers and actual events.
The example can be expanded to include checking timing parameters between
hardware and software triggering (hardware triggers versus software markers). Such
a verification test can be useful if the experiment includes both hardware triggers and
software markers, in which cases the measured latencies can be corrected. It can also
help to decide between hardware and software options when the choice has not yet
been finalized. While we describe an actual experiment, the setup can be adjusted
where necessary to suit your own requirements and the details can be considered as
pure suggestions.
The two main characteristics we want to evaluate in this setup are the latency and
jitter (see Fig.
14.2).
14.7.1 Setup
A stimulus presentation software tool is running on a computer. A participant is
sitting in front of the computer screen and is observing the display. A photo sensor is
placed on the screen to acquire brightness levels. The onsets of events can be
encoded by programming a flashing pattern at the position of the photo sensor.
The sensor can either be used as a trigger source online by generating triggers based
on a brightness threshold, or by analyzing the analog signal later offline. The analog
signal and/or the triggers are recorded with an EEG amplifier, along with the EEG of
the participant, Fig.
A simple ERP paradigm can be used as the foundation of the experiment (just a
flashing pattern or a visual oddball paradigm). The only requirement is that there is at
14.4.
172 A. Kreilinger and P. R. Bazán
Fig. 14.4 Schematic of a timing verification experiment. A stimulus presentation software is
used to: generate a visual event on a display (a), send a trigger to an EEG amplifier (b), and send an
LSL marker to the network (c). Ideally, all of these events should happen simultaneously. The EEG
data stream, including the brain signals from the participant and the photo signal from a photo
sensor, is also sent via LSL and can be synchronized with the LSL marker stream to analyze the
timing between the events. The test can be used with or without the LSL option
least one stimulus related to a visual event, i.e., a specific visual event observable on
the display. Every time the stimulus in question is generated, a trigger is also created
from the stimulus presentation software by inte rfacing a trigger device via USB or
sending signals directly via the parallel port. When we also want to assess the
correlation between software markers and hardware triggers, we can additionally
this, a
send an LSL marker simultaneously. When choosing to do
er
mark
stre
LabRecorder
ams
1
).
need
to be
synchr
onized
and
record
ed
in
ll LSL data and
the network (e.g., in
14.7.2 Analysis
The experiment is started, and all data and marker streams are recorded in one file.
This file now contains the what and when for up to four components:
1. The time when the stimulus is supposed to happen encoded by a trigger.
2. The time when the stimulus really happened encoded by the photo signal, either
analog signal or already encoded as a trigger by applying a specific
as an
threshold.
3. The brain signal recorded from the participant. Either a simple visual ERP or even
00 if using an oddball paradigm.
a P3
4. The time when the stimulus is supposed to happen encoded by a software marker
(if going
1
https://github.com/labstreaminglayer/App-LabRecorder
with the LSL option).
14 Triggers 173
14.7.3 Interpretation
The timing relations between these three or four components can have important
implications:
. If the software markers (LSL) arrive earlier than the other components, most
importantly
the hardware until they are available in the data stream. This is only relevant when
markers (generated by a software) should be used to mark events that are recorded
with an amplifier (hardware). It may be necessary to adjust the latency in this
case. The latency can depend on several factors: the amplifier model, wireless
versus wired, the number of channels, the sampling rate, or the local network.
When changing any of these factors, it is recommended to run the verification
again.
. If the actual onset of the stimulus is delayed significantly, this can hint at a
problem
multiples of the screen refresh cycle time (e.g., 16.67 ms for a 60-Hz display), it
can indicate that the graphics settings are not optimized. Typical reasons can be
connecting too many displays, activating filter settings that buffer multiple cycles ,
not using full screen, or simply running too many programs in the background.
. If there is not only a stable latency, but also high jitter, this can point to potential
problems
Bluetooth
in the programming.
. Sometimes it is simply good to know how different stimulus presentation soft-
ware tools
strengths and weaknesses, it is good to have the tools at hand to evaluate when to
use which software and how to mitigate potential issues.
the triggers, it means that the signals need more time to pass through
in the computer setup. For example, if a visual stimulus is delayed for
in the setup. For example, network difficulties, issues with the
®
transmission caused by interfering environment, or simply an error
work in specific situations. As these different solutions have different
14.8 Conclusion
In this chapter, we highlighted the advantages of triggers over software markers and
provided examples for how to use them to mark important events relevant for
analysis. We showed examples where other options can work as good alternatives
but also indicated potential drawbacks. In addition, we showed an example of how
triggers can be used to verify timing characteristics of an experiment setup.
A clear distinct
serving a specific function in experimental synchronization. While hardware triggers
remain a gold standard for precise timing, alternatives such as software markers
(e.g., via LSL) offer flexibility and can be appropriate depending on the experimental
context. Combining different marker, data, and trigger streams can introduce timing
discrepancies, highlighting the need for systematic testing and validation to ensure
temporal accuracy.
ion can be made between events, markers, and triggers, each
174 A. Kreilinger and P. R. Bazán
References
Kothe, C., Shirazi, S. Y., Stenner, T., Medine, D., Boulay, C., Grivich, M. I., et al. (2025). The lab
streaming layer for synchronized multimodal recording. Imaging Neuroscience. https://doi.org/
10.1162/IMAG.a.136
Miziara, I. M., Fallon, N., Marshall, A., & Lakany, H. (2025). A comparative study to assess
synchronisation methods
Reports, 15, 12816.
Peirce, J., Gray, J. R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., et al. (2019).
PsychoPy2: Experiments
org/10.3758/s13428-018-01193-y
Renard, Y., Lotte,
An open-source software platform to design, test, and use brain–computer interfaces in real and
virtual environments. Presence Teleoperators and Virtual Environments, 19(1), 35–53.
doi.org/10.1162/pres.19.1.35
F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., et al. (2010). OpenViBE:
for combined simultaneous EEG and TMS acquisition. Scientific
https://doi.org/10.1038/s41598-025-97225-7
in behavior made easy. Behavior Research, 51, 195–203.
https://doi.
https://
Chapter 15
Getting Clean Data: Artifacts and How
to Prevent Them
David Kadlec, Shivakumar Viswanathan, and Hannah Kreilinger
Abstract Recording high-quality electroencephalographic (EEG) data is funda-
menta
l for achieving reliable results in research and clinical applications. However,
the EEG signal is highly vulnerable to various artifacts that can arise from physiological sources, such as eye blinks, muscle activity, and cardiac rhythms. In addition,
environmental and technical influences, including electrode impedance, cable movement, or electromagnetic interference, can negatively influence the signal. This
chapter offers a comprehensive overview of approaches to minimize such artifacts
during data acquisition. Central considerations include optimal preparation of the
electrodes, lowering the impedance values on the participant’s head, as well as
maintaining consistent recording conditions. Understanding the source of artifacts,
executing prevent ive measures, and being able to detect different kinds of artifacts as
they occur are often more effective than relying only on post-processing corrections.
This chapter serves as a practical guide to recording clean data for more effective and
reliable analysis.
Keywords Signal quality · Artifact reduction · Physiological artifacts · Technical
s · Best practice · Data acquisition · Lowering impedance
artifact
15.1 Introduction
One unwanted element when recording EEG is the presence of artifacts. As EEG
researchers, the goal is to acquire the highest-quality data possible, and to achieve
this, we must minimize artifacts. A completely artifact-free EEG signal is very
unlikely, so our best approach is to be aware of the sources of artifacts and take
every possible step to prevent or min imize them. This chapte r begins by explaining
the concepts of signal and noise, and why their ratio is essential, especially for
smaller artifacts. We will then take a look at some common artifacts found in EEG
D. Kadlec (*) · S. Viswanathan · H. Kreilinger
Brain Products GmbH, Gilching, Germany
e-mail:
david.kadlec@brainproducts.com; shivakumar.viswanathan@brainproducts.com;
hannah.kreilinger@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_15
175
176 D. Kadlec et al.
signals and consider whether there are any steps that can be taken to avoid or
reduce them.
Some artifacts can have devastating effects on the recorded data and should be
prevented in the first place (e.g., signal saturation, electrode detachment). It is best to
address these issues during the recording process. In contrast, other artifacts are
unavoidable (e.g., blinks) and need to be tackled with appropriate offline processing
strategies. For each artifact example, we will explain its origin and what the EEG
researcher needs to do to minimize its impact on the recorded EEG data, either
during recording or during offline data processing. Handling artifacts offline will be
covered in detail in Chap.
just
provide tips on where to start.
17: EEG Pre-processing and Artifact Handling, here we
15.2 The Idea of Signal-to-Noise Ratio (SNR)
To better understand signal quality, it is an advantage to be familiar with the signalto-noise ratio (SNR). The term signal-to-noise ratio is relevant in almost every
technical field (electronics, audio, imaging, etc.), the field of EEG is no different.
The signal-to-noise ratio indicates how strong the desired EEG signal is relative to
the background noise (particularly electromagneti c interference) and how apparent
the cortical activity is relative to unwanted interference (artifacts). A high SNR
means the desired EEG signal is much stronger than the noise, and the EEG is more
straightforward to record and interpret. In contrast, a lower SNR means there is more
noise, and it will be more challenging to interpret the desired signal. A good example
comes from the audio field: if you watch and listen to a movie during a flight and
have the ‘noise canceling’ functionality turned off on your headphones, it is difficult
to understand the characters because of the humming background noise of the
engines, chatter from passengers, etc. —(low SNR). But once you turn on the
‘noise canceling’ functionality, the SNR becomes higher, and you understand the
desired dialogue in the movie much better.
The interindividual EEG signal varies and so do the artifacts and the background
noise
in a given recording session, therefore we can also expect the SNR to vary.
Ideally, we would have a standard number that represents a good SNR, however, this
has not been established for EEG data. So how do we know if the SNR is high or
low, and whether it’s sufficient for our experiment?
Acceptable
spontaneous EEG recordings, brain signals, such as theta, alpha, beta, or gamma
rhythms, are of relatively low amplitude. This makes the signals of interest more
susceptible to environmental noise, so a high signal-to-noise ratio (SNR) is essential.
In event-related potential (ERP) studies, where averaging across multiple trials to
isolate short time-locked responses to stimuli, it is also crucial to maintain a good
SNR. However, the averaging process improves the SNR because the random noise
is canceled out while the ERP accumulates. It is important to consider your signal of
interest when establishing an acceptable SNR for your study.
SNR might differ for different types of signals and/or data analysis. In
15 Getting Clean Data: Artifacts and How to Prevent Them 177
Visual inspection of the EEG signal remains the most commonly used method in
laboratory settings, especially before and during the EEG recordings.
It is possible to do this in a more quantitative way and some EEG analysis
e allows you to calculate SNR, often simply defined as SNR ¼ power of
softwar
signal/power of noise. Additional tools assessing signal quality are discussed in
Chap. 22: Quantifying EEG and ERP Data Quality. One such metric is the standardi
zed measurement error (SME), which helps to quantify the data quality of an
ERP (Luck et al., 2021). This method can be applied after trial recordings, during
data
analysis, to assess whether further refinements to the paradigm or laboratory
setup are needed.
In general, the SNR improves through good electrode preparation and control of
recording environment, both of which enhance the quality of spontaneous EEG
the
and ERP data. Visual inspection is the most common approach in labs and this is
generally what we look for:
. Good/High SNR: Clean, smooth visible brain activity during resting (i.e., visible
activity and peaks at occipi tal region).
alpha
. Poor/Low SNR: Results in spikes, line noise, and unusual patterns that cannot be
explained by typical brain waves.
15.3 Sources of Artifact
There are two major categories of artifacts: physiological and technical.
Physiological artifacts in EEG originate from the participant’s own biological
activity
, such as eye movements (EOG), muscle activity (EMG), cardiac signals
(ECG), or skin potentials.
Technical artifacts arise from non-biological sources, such as power line inter-
, electrode cable movements, or malfunctioning equipment, as well as elec-
ference
tromagnetic fields from nearby devices.
Both artifact categories arise independently of the brain but can be mistakenly
interpret
physiological and technical artifacts and being able to distinguish artifacts from
signal is crucial. To help you with this, throughout this chapter artifacts are presented
with the aid of images and explained in the corresponding tables with solutions to
minimize these artifacts.
ed as cerebral signals. The recorded waves from the cortex are blended with
15.4 Common Physiological Artifacts
15.4.1 Eye Artifacts
Eye movement artifacts are caused by the movement of the eyeball. The cornea is
positively charged, while the fundus (the inside back surface of the eye) is negatively
charged. These circumstances create a static potential field (dipole). When the
178 D. Kadlec et al.
eyeball rotates, the eye dipole rotates with it, causing a potential change at the nearby
electrodes (corneo-retinal dipole artifact) (Lins et al.,
1993).
Vertical eye movements result from the rotation of the eyeball along the vertical
axis, generating EOG (VEOG) potential primarily visible in the frontal EEG channels (Fp1, Fp2).
Horizontal eye movements, caused by rotation along the horizontal axis (HEOG),
to characteristic saccades detectable at lateral frontal sites (e.g., F7, F8).
lead
Especially in polysomnographic EEG recordings, electrodes are typically positioned
at the outer canthi of the eyes to capture horizontal eye movements with high spatial
sensitivity, facilitating accurate identification of sleep stages, particularly rapid eye
movement (REM) sleep.
Saccades and smooth pursuit movements are eye movements in any direction
(horizo
ntal, vertical, or oblique) that shift the gaze and produce artifacts in the EEG
signal as illustrated in the following Fig. 15.1.
Eyeblinks are predominantly caused by eyelid closure, and slight eyeball rotation,
ing short-duration, high-amplitude artifacts in the EEG.
generat
Blinking is a natural reflex that helps keep the eyes refreshed and moisturized.
This
artifact is unavoidable but can be made predictable.
During a blink, the eyelids act like sliding electrodes, producing an electric field
temporarily increasing the conductivity between the electrodes and the corneo-
and
retinal dipole. This phenomenon is often referred to as the “riding” or “rider artifact”
(Matsuo et al.,
1975).
One can further differentiate between spontaneous (natural) and voluntary (intentional)
blinks. Spontaneous blinks are typically shorter in duration and lower in
Fig. 15.1 EEG recording illustrating artifacts from eye blinks and horizontal eye movements. The
signal is referenced to Fz, and channel order is from frontal to posterior regions. The data are highpass filtered at 0.1–40 Hz to enhance visibility of these characteristic slow deflections. The
topography of a representative saccade exposes the typical spatial distribution and amplitude of
the related artifact, with noticeable activity in frontal and temporal regions due to extraocular
muscle engagement