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21 Online Processing 273
classification. Depending on the platform, the options can be numerous. Available platforms include OpenViBE (Renard et al.,
Jolla,
CA), or BCI2000 (Schalk et al., 2004).
2010), NeuroPype (Intheon, La
. If none of the above options provide the right composition, the ultimate option
be to ask the manufacturer for a software development kit (SDK) or
would
application programming interface (API) that allows a knowledgeable software
developer to create their own applications. This facilitates configuring the EEG
amplifier, acquiring data from the amplifier, and processing data immediately in
the application to generate feedback online. A typical example of how such an
SDK can be used might just be to create an LSL con nector.
21.2.2 How to Work with Raw Data Online
The chosen method for getting raw data might already pre-determine how you work
with the data. However, there might still be options available to you.
. If the manufacturer’s own software package is used for accessing the raw data,
chances
example, when using BrainVision Recorder with RDA, the complementary
BrainVision RecView can be used to work with the data online and to select
different filters for further processing.
. When using one of the openly available protocols, such as LSL, there are often
plugins
example, can be easily incorporated in Python, MATLAB
these languages, it is then possible to write signal processing code or rely on
readily available code from the community. The specifics of the individual
programming languages should be considered with respect to the planned
processing steps. For example, if a very high processing speed is required, C++
might be better suited than Python or MATLAB
other factors playing a role in the final decision.
. Stand-alone platforms often provide signal processing nodes and may even
include
are not enough.
. Developers who are using the SDK typically also have the means to write their
own
are high that there is also a tool available for online data processing. For
available for the major programming or scripting languages. LSL, for
®
®
, C++, or C#. Using
, but of course there may be
options for running user’s custom code in case the provided algorithms
code following data acquisition.
21.2.3 What Factors to Consider for Online Processing
Depending on the requirements, different factors can play a role in deciding which
kind of hardware and/or software to use. Here, we focus mainly on factors that
influence online processing rather than providing a recipe that could bias the reader
274 A. Kreilinger and A. Ojeda
toward an EEG amplifier, accessory, or software to use. To learn more about
hardware and software, refer to Chap.
Periph
eral Physiology and Chap. 13: Software for Recording EEG and Peripheral
logy. Asking yourself the following questions will help you determine what is
Physio
12: Hardware for Recording EEG and
right for your study:
. Is it a mobile or stationary recording? This affects the decision whether to choose
a wireless or cable-bound recording setup. While it is possible for participants to
carry around portable computers, it is more convenient to go for a wireless
connection in such cases. Factors relevant to the online processing are the
potential additional delay that is caused by using wireless transmission, such as
Bluetooth
®
. An obvious drawback is the potential loss of the connection between
transmitter and receiver. Wireless transmission also limits the capacity of data
that can be sent. This can be dictated by the technology itself (e.g., Bluetooth
low energy with a potent ial data rate of ~1 Mbps) or by the necessary amount of
power to increase the WiFi data rate, which would drain the batteries of a mobile
amplifier quickly. That said, high channel counts with high sampling rates are
usually only provided by stationary, cable-bound amplifiers.
. How fast and how precise are the timing requirements? Before deciding on a
piece
of equipment, it is important to know what kind of latencies and jitters are to
be expected when accessing the data streams online. Depending on the drivers,
the use of cables versus wireless transmission, and data protocol, these parameters
can have severe effects on the further signal processing pipeline and feedback
loop. For offline analyses, it usually does not matter if the signal is recorded with
a large latency, as long as the relevant event markers are synchronized and come
with the same latency. For online processing, this can become a big problem
depending on the goals of the experiment. For example, for closed-loop systems,
where the feedback must be provided within a time-critical constraint, it is
important to have access to the data as fast as possible. Here, the word realtime can become relevant if a real-time operating system or environment is
available. If that is not the case, the alternative must be that data can be typically
accessed within a very short amount of time. Examples are the BrainVision
TurboLink that sends data from an actiCHamp Plus EEG-amplifier in under
1.5 ms. This can be linked to a bossdevice from sync2brain (Lieb et al.,
is running on a real-time operating system and uses an estimate of the next
that
2023)
alpha wave cycle to trigger TMS pulses accordingly. Requirements can be a bit
looser for less timing-sensitive scenarios, but typical criteria include a recommendation for low latency and low jitter. It is good practice to test such parameters before running measurements or at least to acquire the information
beforehand.
. What is the nature of the acquired signal? In some cases, signals can only be
accessed in an altered manner, for example, with mandatory temporal and/or
spatial filters. Depending on the signal of interest, this can be problematic when
important signal components are already attenuated by the filters. Ideally, signals
can be ac cessed as raw as possible.
®
21 Online Processing 275
. How well can contextual information and other measurements of interest be
synchronized with the brain signals online? This question is a bit dependent on
previous questions and on how the raw data is accessed in the first place. For
example, when event markers need to be stored together with the EEG, a trigger
solution that connects on a hardware level might already be good enough, but as
soon as other modalities or markers need to be supplied on a software level, other
considerations might be necessary. For example, it is worth checking whether
there are LSL connectors already available for the other modality (e.g., reading
just the mouse cursor or even other biosignals such as functional near-infrared
spectroscopy (fNIRS)). For more information on how to synchronize signals and
events, refer to Chap.
14: Triggers.
21.3 Designing an Online Processing Experiment, an Example
We summarize the chapter by presenting considerations for a hypothetical experiment conceived to use online processing. In this experiment, the user will try to
control a BCI based on motor imagery using EEG. The BCI shall use two classes and
be able to control different actions in a computer game depending on the current
context. In the steps that follow, we provide guidance on selecting appropriate
hardware and software, as well as general considerations for conducting the BCI
experiment. See also Fig. 21.2 for more information.
1. Choosing the hardware: in this case, the criteria include channel count, coverage
of
the relevant area of the brain, data quality, and sampling rate, and importantly,
how raw data can be accessed online. In this specific case, the requirements are
not very exclusive. One should just make sure to use a solution that provides good
quality data, a full coverage of the whole head (~32 channels) or the possibility of
a flexible montage for covering the motor area required for motor imagery. Make
sure you have a high enough sampling rate. This should not be an issue for motor
imagery where a typical 500 Hz sampling rate is high enough to record all
frequency bands of interest. The raw data access feature is also relevant for the
next part.
2. Choosing
the software. Often, the selected hardware comes with different options
for accessing the raw data. Depending on the further steps and how the feedback
needs to be created, these details become relevant here. The software not only has
to be able to read the data but also needs to analyze and classify the signals before
any kind of feedback can be reported in any modality (visual, acoustic, or tactile).
Nowadays, one can rely on a large community that provides an abundance of
information on signal processing for all kinds of different purposes. For example,
code packages can be used based on Python or MATLAB
®
, but also other
software platforms provide many options for online processing with the focus
lying more on the feedback part, such as Unreal Engine (Safikhani et al., 2024) o
r
276 A. Kreilinger and A. Ojeda
Fig. 21.2 Selection of possible choices when designing an experiment and deciding what kind of
hardware and software to use. The choices can be affected by many different factors (e.g., projectrelated requirements, personal experience, or simply by having access to specific equipment). In this
example, the selected setup for the experiment is driven by practical considerations such as
reputation and availability in the lab, but also the fulfillment of certain requirements, such as the
electrode and amplifier type, data quality, sampling rate, and number of channels. Raw data can be
accessed via an LSL connector provided by the manufacturer, and a mix of E-Prime
MATLAB
EEG and Peripheral Physiology and Chap.
Physiology for more information
®
is used to train, visualize, and analyze data. Refer to Chap. 12: Hardware for Recording
13: Software for Recording EEG and Peripheral
®
and
Unity (for example, via LSL3 ). Of course, it is also an option to use an all-in-one
solution such as OpenViBE.
3. Once these decisions are finalized, the preparation of the experiment can begin.
Ideally, the first training sessions are simulated and analyzed. Then, participants
can be brought in for the first pilot sessions. If classifiers need to be created, it
must be considered that these cannot be applied to the data in the same way as
offline. Again, it might be necessary to change filter settings and make sure to be
aware of the constraints when only short amounts of data can be analyzed and
that, ideally, the processing can be repeated frequently. If the processing power is
insufficient to do this at every sample, it may be necessary to run the process in
frames where data is only analyzed in steps of multiple samples.
3
https://github.com/labstreaminglayer/LSL4Unity
21 Online Processing 277
21.4 Conclusion
In this chapter, we introduced the concept of online EEG data processing and
discussed its relevance in scenarios where offline analysis is insufficient. We
outlined various methods for access ing raw EEG data during acquisition, including
manufacturer-specific solutions, open-source data streaming tools, and integrated
platforms that combine data access with processing, visualization, and storage
capabilities. Key considerations for selecting appropriate hardware and software
components were highlighted, particularly in relation to the demands of (near)
real-time experimental setups. Throughout, we emphasized the importance of precise terminology— clarifying distinctions between online, real-time, and near real-
time processing—and discussed how these terms relate to system capabilities and
constraints. Furthermore, we examined the limitations imposed by online processing
on analysis pipelines, such as the inability to apply bidirectional filters or to reference
future data. These considerations were illustrated through the design of a BCI
paradigm, demonstrating the practical steps and trade-offs involved in planning an
online experiment.
References
Arvaneh, M., Ward, T. E., & Robertson, I. H. (2015). Effects of feedback latency on P300-based
brain-computer interface. In 2015 37th annual international conference of the IEEE Engineer-
ing in Medicine and Biology Society (EMBC) (pp. 2315–2318).
2015.7318856
Breitwieser, C., Daly, I., Neuper, C., & Müller-Putz, G. R. (2012). Proposing a standardized
protocol
59(3), 852–859. https://doi.org/10.1109/TBME.2011.2174637
Enriquez-Geppert, S., Huster, R. J., & Herrmann, C. S. (2017). EEG-neurofeedback as a tool to
modulate
https://doi.org/10.3389/fnhum.2017.00051
Kothe, C., Shirazi, S. Y., Stenner, T., Medine, D., Boulay, C., Grivich, M. I., et al. (2025). The lab
streaming
10.1162/IMAG.a.136
Lieb, A., Zrenner, B., Zrenner, C., Kozák, G., Martus, P., Grefkes, C., & Ziemann, U. (2023).
Brain-oscillation-synchronized
s12883-023-03235-1
Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., et al. (2010). OpenViBE:
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. https://
doi.org/10.1162/pres.19.1.35
Safikhani, S., Gattringer, V., Schmied, M., Pirker, J., & Wriessnegger, S. C. (2024). The influence
of
Multimodal Technologies and Interaction, 8(11), 104.
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004). BCI2000:
A
Engineering, 51(6), 1034–1043. https://doi.org/10.1109/TBME.2004.827072
Sherlin, L.
Neurofeedback and basic learning theory: Implications for research and practice. Journal of
Neurotherapy, 15(4).
for raw biosignal transmission. IEEE Transactions on Biomedical Engineering,
cognition and behavior: A review tutorial. Frontiers in Human Neuroscience, 11.
layer for synchronized multimodal recording. Imaging Neuroscience. https://doi.org/
stimulation. BMC Neurology, 23(204). https://doi.org/10.1186/
realism on the sense of presence in virtual reality: Neurophysiological insights using EEG.
general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical
H., Arns, M., Lubar, J., Heinrich, H., Kerson, C., Strehl, U., & Sterman, M. B. (2011).
https://doi.org/10.1080/10874208.2011.623089
https://doi.org/10.1109/EMBC.
https://doi.org/10.3390/mti8110104
Chapter 22
Quantifying EEG and ERP Data Quality
Steven J. Luck
Abstract We can record brain activity (the electroencephalogram or EEG) from
electrodes
the brain. However, the signals we record this way are often contaminated by noise
and artifacts that are an order of magnitude larger than the EEG signals of interest. It
is therefore essential to pay close attention to the quality of the data in order to obtain
stable, meaningful, and statistically significant effects. Until recently, howe ver, there
was no widely accepted method for quantifying data quality for EEG signals,
especially for the event-related potentials (ERPs) embedded within the EEG. In
this chapter, I describe a new metric of ERP data quality, called the standardized
measurement error (SME). I provide an intuitive description of how it works and
then I provide examples of how it has been used to quantify data quality across
several common ERP paradigms and to determine which EEG and ERP processing
operations do the best job of maximizing data quality. I also describe how the SME
can be extended to time-frequency analyses and how psychometric measures of
reliability can be used to assess data quality in studies that focus on individual
differences.
on the scalp, even though the skull and scalp separate the electrodes from
Keywords ERPs · Event-related potentials
· Analysis methods
22.1 Introduction
It seems like a miracle that we can record meaningful brain activity from electrodes
placed on the scalp, with the meninges, skull, fat, and skin between the neurons and
our electrodes. The brain signals of interest are often only a microvolt or two on the
scalp, and these signals are embedded within noise that is typically an order of
magnitude larger. For example, Fig.
participant in an oddball experiment (Fig.
single
S. J. Luck (*)
Center for Mind & Brain and Department of Psychology, University of California, Davis, Davis,
CA, USA
e-mail:
sjluck@ucdavis.edu
© 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_22
22.1 shows the averaged ERP waveform from a
22.1a) along with the 20 single-trial
279
280 S. J. Luck
A Averaged ERP from a single subject
Mean = 7.66 μV
P3
aSME / = 1.28 µV
B Single-trial EEG epochs
SD = 5.73 μV
C Precision, Accuracy, & Bias
High
Precision
Low
Precision
High Accuracy
(Low Bias)
Weight
Weight
Low Accuracy
(High Bias)
D P3 amplitude scores from 10,000
repetitions of the experiment
}
distribution
= 1.3 μV
True
Weight
True
Weight
=
of this
Fig. 22.1 (a) Averaged ERPs for the oddball trials for one participant in an oddball experiment in
which each participant pressed one button for frequently occurring digits and another button for
rarely occurring letters (or vice versa). Time zero is stimulus onset. (b) Single-trial EEG epochs
from the oddball trials that were averaged together to create the waveform in panel A. If the
amplitude of the P3 wave is scored as the mean voltage between 300 and 500 ms in the averaged
ERP waveform, the analytic standardized measurement error (aSME) can be computed by measuring the mean voltage between 300 and 500 ms in the single-trial EEG epochs and applying the
equation shown between panels A and B. (c) Illustration of the concepts of precision, accuracy, and
bias for measuring the weight of an object. (d) Conceptual approach for understanding the SME, in
which the same participant is tested in 10,000 different replications of the oddball experiment
(assuming no fatigue or learning). For each replication, the oddball trials are averaged together, and
the mean voltage between 300 and 500 ms is measured from the averaged ERP waveform. The
histogram shows the P3 amplitude scores obtained from each of these 10,000 replications. The SME
is the standard deviation (SD) of these 10,000 scores.
EEG epochs that were averaged together to create the averaged ERP waveform for
the oddballs (Fig.
EEG
that is not obvious from the averaged ERP waveform, with a clear P3
22.1b). There is clearly tremendous trial-to-trial variability in the
component in the averaged ERP that is difficult to see in the single-trial EEG epochs.
When I first started running EEG/ERP experiments about 40 years ago, my data
looked a lot like the waveforms shown in Fig. 22.1, but I had no idea whether the
amount of trial-to-trial EEG variability I was seeing was “normal.” And I had no idea
whether I had enough trials to obtain a stable averaged ERP waveform given this
amount of EEG variability. I desperately wished that I had a metric of data quality
that would allow me to determine whether my EEG and ERP waveforms were
sufficiently clean. I kept looking in the EEG/ERP literature for data quality metrics,
22 Quantifying EEG and ERP Data Quality 281
but I never found one that seemed very useful. After several decades, I gave up on
looking for an existing metric of data quality, and I worked with a set of collaborators to develop one (Luck et al.,
(Lopez-C
quality
psychophysiological measures.
use them in your own research. A good metric of data quality can quantify how much
statistical power you have lost because of the noise in your single-trial EEG epochs
and how much power you could gain by increasing the number of trials. It can help
you decide which trials to reject and which channels to interpolate. It can tell you
which preprocessing techniques work best to increase your data quality and improve
your statistical power. And it can provide you with information about whether the
data from a given participant or a given dataset has too much noise to be believable.
alderon & Luc k, 2014). In the process, I learned about additional data
metrics that can be used for ERPs, time-frequency analyses, and other
The goal of this chapter is to explain these metrics of data quality so that you can
2021) and implement it in ERPLAB Toolbox
22.1.1 Defining Data Quality
Before we can discuss metrics of data quality, we need to spend a moment considering what we mean by the term data quality. When we measure a simple physical
quantity, such as the weight of an object, the quality of the measurement is
classically defined in terms of accuracy (or bias) and precision. This is illustrated
graphically in Fig. 22.1c for the measurement of weight; we assume that the true
weight does not change across measurement attempts. A meas urement is accurate if
the average measurement across many measurement attempts is close to the true
value (e.g., the true weight in Fig. 22.1c). Bias is the converse of accuracy: a
meas
urement is biased if it is systematically shifted in a specific direction away
from the true value. Precision reflects the spread of values across measurement
attempts. A measurement method is precise if we get similar values on each
measurement attempt. A measurement method can be accurate without being precise
(e.g., a broad spread of values, but with an average near the true value), and it can be
precise without being accurate (e.g., a narrow spread of values, all of which are
systematically shifted away from the true value). Or it can be both accurate and
precise, or both inaccurate and imprecise.
In this chapter, I will define data quality in terms of precision, not accuracy or
bias, because precision is more related to the concept of how “noisy” a dataset
is. Accuracy and bias are important, but they usually become an issue during the
process of scoring the amplitude or latency of an ERP component. Rather than trying
to quantify accuracy/bias, a common approach is to use amplitude or latency
quantification algorithms that are mathematically guaranteed to be unbiased (see
Chapter 9 in Luck,
d also like to distinguish between a measurement and a score. I like to
I woul
define a measurement as a quantification of a simple physical quantity, such as the
raw EEG voltage at a particular moment in time. I then define a score as a value that
2014).
282 S. J. Luck
we compute by applying an algorithm to a set of measurements, such as the peak
amplitude of the voltage between 300 and 500 ms in the averaged ERP waveform or
the power between 8 and 12 Hz in a Fourier transform of a single-trial EEG epoch. In
this chapter, we will focus on the data quality of scores, not measurements, because
the scores are what you use as the dependent variables in your statistical analyses and
use to test your scien ti fic hypotheses. The precision of the measureme
used to compute a score contributes to the precision of the score, so the precision of a
score re flects measurement precision as well as higher-order factors such as artifacts.
Thus, for the purposes of this chapter, data quality is defined as the precision of a
score.
nts that are
22.1.2 Chapter Overview
Now that I have defined what I mean by data quality, we can move on to discuss how
data quality can be quantified. The next section will provide a detailed description of
the metric of data quality that my lab developed for averaged ERPs, called the
standardized measurement error or SME (Luck et al.,
by
examples of SME values from actual experiments, a discussion of how SME is
related to statistical power, and concrete examples of how the SME can be used to
improve ERP research. The SME is appropriate only when scores are obtained from
averaged ERPs, but later sections of the chapter will describe how the SME could be
readily extended to time-frequency analysis and other psychophysiological measures
based on averages, and how metrics of reliability can be used to quantify data quality
in research on individual differences.
2021). This will be followed
22.2 Quantifying Data Quality in Averaged ERPs
with the Standardized Measurement Error (SME)
22.2.1 A Simple Case: Analytic SME for Time-Window Mean
Amplit
I’m going to start with a simple but common case, in which the amplitude of an ERP
component is scored from an averaged ERP waveform as the mean voltage within a
measurement window, as illustrated in Fig.
The data
Luck (2010), with 80 standard trials and 20 oddball trials. Panel A shows the
averaged
the 20 EEG epochs that were averaged together to compute this averaged waveform.
In this experiment, the amplitude of the P3 wave was scored as the mean voltage
between 300 and 500 ms in the averaged ERP waveform, yielding a value of
7.66 μV.
ERP waveform for the oddballs in one participant, and Panel B shows
ude Scores
22.1a, b.
in the figure were taken from the oddball experiment of Kappenman and
22 Quantifying EEG and ERP Data Quality 283
When we score an amplitude from an averaged ERP waveform as the mean
voltage across a time window, this is typically called the mean amplitude scoring
method. However, this term can be confusing, because it involves both a mean
across trials (when creating the averaged ERP waveform) and a mean across time
points. As a result, I like to refer to it as the time-window mean amplitude scoring
method to emphasize that it is a mean across time points.
This scoring method is unbiased (Luck et al., 2021). Thus, if we averaged
er an infinite number of trials and measured the time-window mean amplitude
togeth
from this averaged ERP waveform, we would obtain the true value for this participant. But how close to the true value is the score of 7.66 μV that we obtained from
an average of only 20 trials? Informally, we can think of data quality as being an
indicator of how much confidence we have that the score obtained from a given
participant is close to the true score (i.e., the score we would obtain if we had an
infinite number of trials).
Consequently, the data quality is inversely related to the amount of trial-to-trial
varia
bility in the single-trial EEG epochs. If the time-window mean amplitude in the
single-trial epochs is similar from trial to trial, then the value we get from an average
of 20 trials should be pretty close to the value we would get from average of an
infinite number of trials. But if the time-window mean amplitude varies a great deal
from trial to trial, then the value obtained from an average of 20 trials might be quite
different from the true value. So, our first step in quantifying the data quality for the
time-window mean amplitude from the averaged ERP waveform is to obtain timewindow mean amplitude from each single-trial epoch and quantify the variability as
the standard deviation (SD) of these single-trial values.
I did this for the 20 single-trial EEG epochs in Fig. 22.1b, and I obtained an SD of
μV. This is a useful value that tells me something important about the data
5.73
quality of the EEG, but my ultimate goal is to quantify the precision of the timewindow mean amplitude score I obtained from the averaged ERP waveform. I would
have a perfectly precise score if the averaged ERP was computed from an infinite
number of trials, and it seems obvious that the precision will decline as I have fewer
and fewer trials. Thus, we need to take into account the number of trials that are
being averaged together (which we denote N ). Mathematically, the precision of a
time-window mean amplitude value obtained from an averaged ERP waveform is
directly related to the square root of N.
This brings us to how we compute our measure of data quality for averaged ERPs,
the
standardized measurement error (SME). The SME for a time-window mean
amplitude score obtained from an averaged ERP waveform is simply the SD of the
time-window mean amplitude values obtained from the single-trial EEG epochs
divided by the square root of N. For reasons I will describe late r, we call this the
analytic SME or aSME, and it is defined as
aSME ¼ SD
p
N
22:1Þ
ð