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19 Event-Related Potentials 243
trials is considered a prerequisite for ERPs, so that all trials share the same baseline
(i.e., mean amplitude level). The usual procedure is to take the mean of a dedicated
baseline interval and subtract that value from all data points in the trial. This will
shift the baseline of each trial to a level around zero. The pre-stimulus period is the
obvious choice of baseline interval as activity in the time before the stimulation
should be relatively low. This is another reason w
so that the reaction to the preceding stimulus has waned. In cases when increased
activity in the pre-stimulus period is expected, the full trial could be used instead as a
baseline interval.
As for the trial length, you also need to decide the length of the baseline interval.
It should not be too short so that extreme values could bias the calculation of the
mean. But it should also not be too long to keep it limited to a period of little
variability. Many published ERP studies use an interval of 200 ms. The reasoning is
that this is equal to two periods of an alpha oscillation which is the dominant
oscillation in human background EEG. Using multiples of an alpha cycle might
therefore reduce the influence of alpha on the ERPs as positive and negative parts of
a cycle should cancel out (Luck,
19.2.1.4 Averaging
The last step in calculating ERPs is averaging the trials. All included trials should be
of artifacts. Otherwise, if you average only a small number of trials, even a
free
single larger artifact can compromise the ERP. Conversely, this is less of an issue if
you have many trials that are largely artifact-free. Usually, all trials of one condition
are averaged together. However, in some cases, multiple averages could be created
for subsections of the experiment. For example, if you have a very long experiment,
and you want to compare the responses for early and late trials. Other common
approaches to check the reliability of an ERP could be to contrast odd and even trials,
or to sample trials at random.
The processing steps discussed so far are required for an ERP analysis. However,
there
are further optional steps. A common example is artifact handling on the trial
level. In fact, it is not unusual to do a coarse artifact screening early on to remove
only large-scale artifacts that would otherwise interfere with other pre-processing
steps. In this case, it is likely that some subtler artifacts will survive, and a more finegrained artifact rejection can be done before averaging trials.
2014).
hy the ISI should be long enough
19.2.2 Interpreting ERPs
Now that you know how to compute ERPs, let’s take a step back and think about
what you might want to investigate with an ERP experiment. A classical research
question could be that you have two conditions A and B, and you expect a difference
in the ERP waveform between the conditions due to some experimental
244 M. Hoppstädter
manipulation. Coming back to the oddball example, the two conditions could be
frequent and rare tones. Another approach is to explore differences in the same
experimental condition between experimental groups, for instance, between healthy
controls and patients. Thus, the next step after creating the ERPs would be to
compare them to assess the research quest ion.
A common way to explore differences between conditions is to simply overlay
both waveforms and compare the differences visually. This can be extended to
computing their difference wave. If you subtract two waveforms from each other,
the result shows how the difference between two conditions (or groups) varies over
time. This can help you determine when exactly an ERP effect of interest emerges.
You might not only be interested in when but also where an ERP appears. To inspect
this, you can plot the difference wave as a topography. This can help you locate the
spatial focus of an effect (see Sect.
19.3 for more information).
19.2.3 Group Analysis
The average ERP response across all datasets of a study is often called the grand
average ERP. This is usually calculated for each condition separately. Through the
additional averaging step, the SNR will again improve with every added dataset. The
grand average ERP can be investigated between conditions and between groups in
the same way as the single-subject ERPs (see Sect.
the
analysis is to extract features for statistics but this will be discussed later in this
chapter (see Sect.
19.5).
19.2.2). The last step to complete
19.2.4 Single- Trial Analysis
An alternative to a classical ERP analysis is to stop before averaging the trials and
investigate the single trials instead. This can be useful, for example, when you are
interested in the variability of a signal over time. However, the consequence of
looking at single trials is that they are based on only one observation and therefore
the SNR will be much lower due to the higher noise level.
19.3 Character istics of the ERP and Its Components
Since EEG is generally an oscillating signal, the ERP also presents itself as a
waveform pattern of alternating positive and negative-going deflections (see
Fig.
19.2). These individual deflections are usually referred to as ERP components.
However
specific physiological or cognitive response.
, not every peak is necessarily a component if it has not been related to a
19 Event-Related Potentials 245
Fig. 19.2 A simplified
overview of the waveform
pattern of ERP components.
The figure is based on an
auditory oddball paradigm
and the ERP at CPz is
displayed. Sensory
components and a P3 are
visible. Note that negative is
plotted downward. The
approximate time ranges in
which the different classes
of ERP components fall are
indicated at the bottom
Each time point of the ERP waveform can be described by several parameters.
While not every point in the ERP is of equal interest, the start and the peak of specific
deflections usually are. They can be characterized by their onset and peak latency
with respect to the stimulus presentation. The polarity refers to the direction of the
deflection, meaning if it is positive or negative going. Finally, the amplitude (i.e., the
current value of voltage) and phase (i.e., the position in the ongoing oscillation) can
be extracted at each time point.
Naming of ERP Components The characteristic shape of alternating positive and
negative deflections also inspired the nomenclature of ERP components. The logic is
simple: if the component is going in a negative direction the name starts with N, or if
positive-going with P. Then the time around which the component usually peaks is
added. Thus, a positive-going potential peaking around 300 ms after a stimulus
would be called P300. Many ERP components are named following this scheme,
though sometimes a short form may be used (i.e., P3 instead of P300). This focuses
more on the position of the component in the overall sequence of deflections rather
than on its peak time which can vary. However, some components have been named
by using more phenomenological or functional descriptions, like the contingent
negative variation or the mismatch negativity (see Sect.
19.4 for examples). It
makes sense to add a word of caution here: you should always check if an ERP is
plotted with negative polarity pointing upward or downward as both options are used
in the ERP literature.
Topographies Another characteristic of ERP components is that they usually have
a typical localization. Hence, it is very common to look at topographies which map
the voltage distribution on the scalp. These can be created for single points, like the
onset or the peak of a component, and you can follow the change in spatial
246 M. Hoppstädter
distribution over time. You can also map the mean amplitude of an ERP component.
This type of plot is commonly found in the results section of any ERP paper (for
more information on typical EEG figures, see Chap.
Research
Variability Importantly, these characteristics are not absolutely fixed. While an
ERP component has a typical onset, peak latency, and topography, those parameters
can vary with many experimental variables. The specifics of the paradigm, the
stimulus timing, the type of stimuli, and sensory modality are just a few examples.
Also, the characteristics of the tested participant group can affect ERPs, for example,
their age or if they have a psychiatric condition. Some aspects of data processing
such as the referencing scheme or the number of trials contributing to the ERP can
influence the appearance of the final ERPs as well.
Paper, and Fig. 36.2 for an example of a topography).
36: How to Evaluate an EEG
19.4 Commonly Investigated ERP Comp onents
This section provides some examples of commonly investigated ERP components.
The list is not exhaustive, b ut it mentions many ERP components that you are likely
to encounter in the ERP literature. If you are looking for more in-depth information
about a component, there are some great books focused on ERP components (see for
instance Luck, 2014; Handy, 2005; Luck & Kappenman, 2011).
One way of classifying ERP components is by looking at the time window in
they occur. First, there are the early sensory components occurring within
which
roughly the first 100 ms after the stimulus that are elicited in response to auditory and
visual stimuli. Then follows a range of long-latency sensory components within the
first 200 ms. These react to the stimulus characteristics, and they vary in their shape
depending on sensory domain. Later potentials can be described as more cognitive
components. They are task-dependent and correlate with higher cognitive functions.
These components can also be labeled as endogenous, while those components that
are automatically triggered through a sensory stimulus are exogenous (Luck,
Earli
er components have a rather small amplitude but a high frequency, while the
later components usually show larger amplitudes at a much slower frequency.
2014).
19.4.1 Early Sensory Components
Auditory brainstem responses (ABRs) are the earliest auditory component and will
be automatically and reliably evoked when auditory stimulation with clicks is used.
They reflect information processing on the auditory pathway from the cochlea
through the brainstem to the thalamus. There are usually up to seven peaks in
roughly millisecond distance labeled I to VII.
19 Event-Related Potentials 247
Table 19.1 Examples of early sensory ERP components
Timing
w.r.t.
Component
ABR Within 10 ms. Usually recorded from
C1 Peaks ~100 ms.
stimulus
onset
overlap
Might
with P1 if C1 is
positive.
Distribution Common experimental observations
with mastoid
vertex
reference.
Strongest at posterior
midline sites (e.g., Oz,
Pz).
Measured to assess integrity of auditory pathway.
Usually sampled at 20 kHz.
Shape depends on participant and
stimulus characteristics, e.g., higher
intensity leads to shorter peak latencies, higher frequency stimulation
results in longer peak latencies (Pratt,
2012).
Originates in V1.
Elicited by any visual stimulus.
Can be affected by sample characteristics, e.g., smaller amplitude and
longer latency in schizophrenic
patients (Schechter et al.,
2005).
The earliest visual response is the C1 wave which originates from processing in
the primary visual cortex (V1). It can be of either polarity (i.e., negative or positive)
depending on where in the visual field a stimulus is presented (Jeffreys & Axford,
1972).
Refer to Table 19.1 for a summary of early sensory components.
19.4.2 Long-Latency Sensory Components
These components are related to the processing of stimulus characteristics. They are
not strictly specific to a sensory domain, but I focus on examples from auditory and
visual stimulation since they are most commonly used.
The P100 relates to early visual processing (like the C1) of a stimulus, but further
stream of V1 in the visual pathway. Both the visual and auditory N100 are
down
widely studied in early sensory processing and have been linked to the detection of
change.
The N170 can be considered a special version of a visual N100 linked to the
proces
sing of human faces, or to other stimuli for which the participant has acquired
expert knowledge, e.g., birds (Tanaka & Curran,
The P200 has been linked
to processing basic visual target features of a stimulus,
and it reacts similarly to a P300 (see Sect. 19.4.3).
In the N200 range, you can find several sub-components, like the N2a which is
also
known as Mismatch Negativity (MMN) and the N2b, which has been linked to
response inhibition.
Refer to
Table 19.2 for a summary of long-latency sensory components.
2001).
248 M. Hoppstädter
Table 19.2 Examples of long-latency sensory ERP components
Timing
w.r.t.
Component
P100 Peaks
N100 Peaks ~100 ms.
N170 Peaks
P200 Peaks ~200 ms. Fronto-central
N2a/MMN Peaks ~200 ms. Strongest at fronto-
N2b Peaks ~200 ms. Anterior
stimulus
onset
–130 ms.
100
Might overlap
with C1 (if C1 is
positive).
Anterior
precedes
posterior peak.
150
peak
–200 ms.
Distribution Common experimental observations
Lateral occipital
maximum.
Widespread with
anterior and posterior peaks.
Parieto-occipital
maximum and
bilateral.
maximum.
midline
central
sites.
maximum.
Elicited through any visual stimulation.
Sensitive to stimulus characteristics.
Investigated with basic stimuli, like
pure tones or a flickering checkerboard.
Suffers from refractory effects, i.e.,
smaller amplitudes for shorter ISIs.
Increased amplitude to the perception of
(Eimer,
faces
2012).
Usually accompanied by central positivity (vertex positive potential).
Originates from activity in the fusiform
and occipital face areas (Haxby et al.,
2000).
Can be investigated with oddball or
visual
More pronounced for target features in
infrequent visual stimuli.
Mismatch detection in a sequence of
standard stimuli, usually tested with
pure tones.
Elicited automatically without the need
to consciously attend the stimulus.
Amplitude increases with stronger
deviation from standard stimulus
(Näätänen & Kreegipuu,
Measure of response inhibition, classically
adigm.
More negative amplitude with increasing frequency of Go stimulus (Bruin &
Wijers,
2011; Rossion & Jacques,
search paradigms.
2012).
investigated with Go/No-Go par-
2002).
19.4.3 Later Cognitive Components
Numerous components fall into this category as ERPs have been used to investigate
many different cognitive processes. I have selected a few prominent examples.
The P30
a marker of change detection in stimulus sequences, however, the task has to be
relevant (contrary to the MMN).
0 (or more precisely the P3b component) is used similarly to the MMN as
19 Event-Related Potentials 249
Two components have been investigated intensively in language research. While
the N400 is considered a proxy for semantic congruency, the P600 serves as a
marker of syntactic violations.
ERPs have also been related to the recruitment of resources for processing
emotional
tion of additional attention to emotionally relevant stimuli (Nordstrom & Wiens,
2012).
In the learning phase of long-term memory experiments, one can investigate the
Dm
involves contrasting the ERP in response to items that were later successfully
remembered minus the ERP in response to later forgotten items and yields a
widespread positivity.
There are some ERPs related to motor responses, which are typically slow waves
that
potential, also known by its German name Bereitschaftspotential) is a slowly
varying negative deflection in anticipation of a motor response.
The ERN (error-related negativity) is different
components because it is related to a response rather than the stimulation. Hence,
it is a response-locked potential. The ERN is thought to reflect error monitoring and
cognitive control (see Gehring et al. (
Refer to Table 19.3 for a summary of later cognitive components.
stimuli. The LPP (late positive potential) is thought to reflect the alloca-
effect (difference due to memory, also known as subsequent memory effect). This
evolve over longer time intervals. As an example, the LRP (lateralized readiness
from all aforementioned ERP
2012) for an overview).
19.4.4 ERP Components in Multimodal Recording Scenarios
ERPs are not always recorded by themselves, but also in multimodal recording
scenarios. This enables you to create ERPs based on events other than experimental
triggers. Many scenarios are possible, but I want to mention three examples:
EEG and ECG If you add an electrocardiogram (ECG) to the EEG recording, you
investigate heartbeat-evoked potentials, a marker of neural interoceptive
can
processing of cardiac activity (Schandry et al., 1986). The idea is to detect each
R-peak of the QRS complex and use this to align the EEG signal with the heartbeat.
This ERP peaks between 250 and 450 ms after the heartbeat in frontal regions.
EEG and TMS With transcranial magnetic stimulation (TMS) of the motor cortex,
you can provoke a motor response and use the EEG to record motor-evoked
potentials. However, you can also align the EEG with the trigger of the TMS
pulse to generate transcranial-evoked potentials. These produce a complex waveform, and they are considered a measure of cortical reactivity (Hernandez-Pavon
et al.,
2023).
EEG an
participant is looking. You can use this information to exclude trials in which a
stimulus was not attended, thus improving the validity of your ERP. It is also
d Eye Tracking Eye trackers allow you to check where exactly your
250 M. Hoppstädter
Table 19.3 Examples of later cognitive ERP components
Timing
w.r.t.
Component
P300/P3b Peaks ~300 ms. Widespread centro-parietal
N400 Peaks ~400 ms. Centro-parietal maximum,
P600 Peaks ~600 ms. Posterior maximum. Investigated with either written
LPP Similar time
Dm-effect Wider positivity
LRP Starts ~1 s
stimulus
onset
window
but more
extended.
between
and 800 ms.
400
before
the motor
response.
to P300
Distribution
maximum.
shifted to the right
usually
hemisphere.
Centro-parietal maximum. Observed in response to emo-
Centro-parietal maximum,
influenced by stimulus category (e.g.,
pictures vs. words).
Fronto-central maximum. Lateralization and polarity
Common experimental
observations
Classically investigated with
oddball paradigm, both in
the
auditory (e.g., pure tones) and
visual (e.g., X’s and O’s)
domain.
Larger amplitude with increasing deviancy and decreasing
frequency of targets compared
to standards (Polich,
Typically examined using linguistic materials (written or
spoken or combined).
The effect can also be demonstrated with non-linguistic
materials like meaningful line
drawings (Kutas & Federmeier,
2000).
Amplitude
tance of the target word from
the semantic context.
or
usually full sentences.
Larger for sentences with
non-grammatical structure, e.g.,
words appearing in incorrect
position or use of the wrong
verb form.
tionally arousing stimuli (usually complex visual scenes).
Responses to both positive or
negative emotional scenes show
increased amplitudes compared
to neutral ones (Liu et al.,
2012).
Connected to successful
encoding of items to long-term
memory (Wilding &
Ranganath, 2012).
Replicated
stimuli (e.g., spoken or written
words, visual objects).
depend on which motor
response is carried out, e.g.,
left vs. right hand, or
scales with the dis-
spoken language materials,
for a variety of
2004).
(continued)
19 Event-Related Potentials 251
Table 19.3 (continued)
Timing
w.r.t. stimulus
Component
ERN Starts before the
onset Distribution
response.
~100 ms
Peaks
after the
response.
Midline fronto-central
maximum.
Common experimental
observations
hand vs. foot (Brunia et al.,
2012).
Considered
ment preparation but it can also
be observed when the movement is inhibited (Smulders &
Miller,
Time-locked to the response.
Usually observed in difference
wave between correct responses
minus incorrect responses.
For partial errors (e.g., wrong
button press inhibited and
corrected), the amplitude falls
in between full errors and correct trials.
Corrected errors show a larger
amplitude than
uncorrected ones.
a marker of move-
2012).
possible to obtain fixation-related potentials. This means to align the EEG with the
fixation marker from the eye tracker instead of using the stimulus trigger. This
provides a better time estimate of the beginning of stimulus processing (Hutzler
et al., 2007).
19.5 Extraction of ERP Features
Once your ERP analysis is complete, you usually want to investigate the effects
statistically. This means checking for significant differences between two or more
conditions, groups, or EEG channel locations. Features of interest differ across
studies, and I will discuss a few common examples below (see also Fig.
Peak-Related
Features It is possible to base the comparison on the peak of an ERP
component, i.e., the single point with the maximum amplitude of the deflection. This
peak amplitude (Fig.
of
interest and statistically tested. Note that the peak of an ERP component is likely
19.3a) can be extracted for all conditions, groups, and sensors
to fall into a similar time range across electrodes or compared datasets, but it is
unlikely to coincide perfectly. The peak latency (Fig.
ble of interest.
varia
19.3b) can therefore also be a
19.3).
252 M. Hoppstädter
Fig. 19.3 Overview of
typical measures extracted
from ERPs based on a P3
component. From the peak
of a component, both the
peak amplitude (a) and the
peak latency (b) can be
determined. Aggregated
measures like the mean
amplitude (c) or the area
under the curve (d) can be
computed within a defined
time window around the
component. S Stimulus,
A Amplitude, t Time
ERPs can still contain some higher frequency oscillations, which means that the
data can fluctuate around the peak. To avoid picking just one of many small “peaks”,
it is common to average across neighboring data points around the maximum (see
also next section). This filters out the high-frequency noise but still does not
guarantee that you selected the “true” peak among many small fluctuations (Luck,
2014; see also Chap. 22: Quantifying EEG and ERP Data Quality, specifically Sect.
22.2.5).
Sometimes, the distance or amplitude difference between two points could be of
interest, for instance between two neighboring peaks (peak-to-peak) or between a
peak and the preceding depression (trough-to-peak).
Mean Amplitude and Area Measures Another common way to extract features of
an ERP is to aggregate values over time. This could be the mean amplitude
(Fig. 19.3c) of a specific time interval related to an ERP component. However,
this works best for shorter and well-defined components (thus not optimal for slow
waves). Ideally, the time range should not be selected based on the visually apparent
difference in the data itself as this would be circular reasoning (Keil et al., 2014).
You could define it based on prior observation, for instance, by running a pilot study,
or consider other studies on the same topic. Another way to optimize this is to apply
data-driven methods. Fo r instance, you can use non-parametric permutation tests to
find time windows and locations to probe afterwards with more classical statistical
tests (Maris & Oostenveld,
2007).
The benefit of using mean amplitudes is that you counteract the high-frequency
noise issue mentioned above. However, a mean amplitude can be biased by extreme
values within the time window. Also, if the time window contains both negative and
positive amplitudes, they will cancel each other out when computing the mean. A
way around this issue is to use area measures, like the area under the curve
(Fig.
19.3d). This refers to the area between the curve and the x-axis which can be
considered as always being positive so that negative and positive parts of the curve
would not cancel out.