1. Event-related Potential (ERP)
EEG Course
Translational Neuromodeling Unit
Frederike Petzschner

2. Let’s start with a concrete example…

3. Let’s start with a simple experiment: variant of the oddball task
80%
20% X X O O X X
Marker
Also called continuuous performance task
Luck, 2005

4. Simple experiment 80% 20% X X O O X X
Also called continuuous performance task
EEG from one electrode site midline over parietal lobes
Luck, 2005

5. Simple experiment ERP Components: P = Positive N = Negative P1 = P100
etc.
Also called continuuous performance task
ERP components have labels like P1 and N1 refer to polarity and position in the waveform. These labels are NOT linked to the nature of the underlying brain activity!
Luck, 2005

6. ERP Components – P1 P1: Luck, 2005
Sensory peak, elicited by visual stimuli no matter which task is used
strongly influenced by stimulus parameters (luminance)
Onset: ms, peak: ms
Latency varies with contrast
Early portion may come from middle occipital gyrus, late portion from fusiform gyrus
Also called continuuous performance task
Luck, 2005

7. ERP Components – P3 group
Depends on which task is performed
no clear consensus about what neural or cognitive process the P3 wave reflects. (‘context-updating’)
P3 amplitude gets larger as target probability gets smaller.
Also local probability matters, because the P3 wave elicited by a target becomes larger when it has been preceded by more and more nontargets.
know a great deal about the effects
of various manipulations on P3 amplitude and latency, but there is
Moreover, it is the probability of the
task-defined stimulus class that matters, not the probability of the
physical stimulus. For example, if subjects are asked to press a button
when detecting male names embedded in a sequence containing
male and female names, with each individual name occurring
only once, the amplitude of the P3 wave will depend on the relative
proportions of male and female names in the sequence (see
Kutas, McCarthy, & Donchin, 1977). Similarly, if the target is the
letter E, occurring on 10 percent of trials, and the nontargets are
selected at random from the other letters of the alphabet, the target
will elicit a very large P3 wave even though the target letter is
approximately four times more probable than any individual nontarget
letter (see Vogel, Luck, & Shapiro, 1998).
e.g. here much larger for the rare O then for the frequent X stimuli
Luck, 2005

8. ERP Components –Mismatch Negativity (MMN)
observed when subjects are exposed to a repetitive train of identical stimuli with occasional mismatching stimuli
negative-going wave that is largest at central midline scalp sites and typically peaks between 160 and 220 ms.
Several other components are sensitive to mismatches if they are task-relevant, but the MMN is observed even if subjects are not using the stimulus stream for a task
thought to reflect a fairly automatic process that compares incoming stimuli to a sensory memory trace of preceding stimuli.
However, the MMN can be eliminated for stimuli presented in one ear if the subjects focus attention very strongly on a competing sequence of stimuli in the other.

9. ERP Components –Error-related negativity (ERN)
Can be elicited by
Being aware of an error
negative feedback following an incorrect response
- observing someone else making
an incorrect response
Most investigators believe that the ERN reflects the activity of a system that either monitors responses or is sensitive to conflict between intended and actual responses.
Source could be ACC.

10. Why are ERP Components interesting?

11. Advantage over pure behavior
1. Provide a continuous measure of processing between stimulus and response
Research Question: Are slowed responses (RTs) due to slower perceptual processes or slower response processes?
Latency of the P3 wave becomes longer when perceptual processes are delayed, no increase in latency in the Stroop Task
 ERPs might proof useful for determining which stage of processing is influenced by a task
Luck, 2005

12. Advantage over pure behavior
2. Can provide an online measure of the processing of stimuli even when there is no behavioral response
Attended versus ignored stimuli
Language comprehension can assess processing of a word embedded in a sentence at the time the word is processed
Luck, 2005

13. ERP changes in Psychiatric Disorders
Alcohol (N1, P2, N2, P3, …)
Schizophrenia (N1, P2, N2, P3, MMN, …)
Bipolar Disorder (P50, P3)
Depression (P3)
Phobia (P3)
Panic disorder (P3)
Generalized anxiety disorder (P3)
OCD (P3, N2, ERN)
Posttraumatic stress disorder (P50, P3)
Dissociative disorder (P3)
Personality disorder (N2, P3)

14. Disadvantage over behavior
1. Very small signal, requires a large number of trials to measure them accurately. (50, 100 or even 1000 trials)
2. Functional significance: we don’t know the specific biophysical events that underlie the production of a given ERP
Luck, 2005

15. What are ERPs? Evoked Model
EVOKED MODEL: The fact that ERPs consist of a sequence of components
seems to imply a sequential activation process. As an
example, the processing of a visual stimulus can be
characterized by three early components, the C1 with a
latency around 80ms which is generated in the striate cortex
(area 17), the P1 with a latency around 110ms which is
generated in the extrastriate cortex (areas 18 and 19) and the
N1 with a latency around 160ms which probably is generated
in the temporal cortex (cf. Clark et al., 1995 ; Di Russo et al.,
2002 ; Allison et al., 2002 ). These findings invite the
interpretation that a visual stimulus is first processed in the
primary visual cortex and then in secondary areas before it
can be identified as an object, a process that is related with
areas in the temporal cortex. Thus, the timing for the
generation of ERP components appears as sequential neural
processes that results solely from anatomical properties.

16. What is an ERP?
Two main types of electrical activity in the brain: action potentials and postsynaptic potentials
Action potentials are discrete voltage spikes that travel from the beginning of the axon to the cell body to the axon terminals where neurotransmitters are released
Postsynaptic potentials are voltages that arise when the neurotransmitters bind to receptors to open or close and leading to a graded change in the potential across the cell membrane
If an electrode is lowered into the intercellular space in a living brain both types of potentials can be recorded. Its fairly easy to isolate the action potentials arising from a single neuron by inserting a microelectrode into the brain but it is virtually impossible to completely isolate a single neuron’s postsynaptic potential in an in vivo extracellular recording. Single unit recordings thus measure action potentials rather than postsynaptic potentials.
When recording many neurons simultaneously it is possible to measure either tehor summed postsynaptic potential or their action potentials. Recordings of action potentials from large populations of neurons are called multi-unit recordings and recordings of postsynaptic potentials from large groups of neurons are called local field potentials
Luck, 2005

17. ?
Which of the two, action potentials or postsynaptic potentials, do you think we see reflected in EEG?

18. What is the ERP?
Mostly surface electrodes can not detect action potentials due to the timing and physical arrangement of axons. Will most likely cancel each other out
When an action potential is generated,
current flows rapidly into and then out of the axon at one
point along the axon, and then this same inflow and outflow occur
at the next point along the axon, and so on until the action potential
reaches a terminal. If two neurons send their action potentials
down axons that run parallel to each other, and the action potentials
occur at exactly the same time, then the voltages from the two
neurons will summate and the voltage recorded from a nearby
electrode will be approximately twice as large as the voltage
recorded from a single action potential. However, if one neuron
fires slightly after the other, then current at a given spatial location
will be flowing into one axon at the same time that it is flowing out
of the other axon, so they cancel each other and produce a much
smaller signal at the nearby electrode.
Luck, 2005

19. What is an ERP? - Postsynaptic Potential
If an excitatory neurotransmitter is released at the apical dentrides:
current will flow from extracellular space into the cell, yielding a net negativity on the outside in the region of the dentrides.
Current will also flow out of the cell body and basal dentrides yielding a net positivity in this area
Action potential duration: 1ms
Cortical pyramidal cell
Luck, 2005

20. Best guess of a biophysical event that gives rise to a scalp ERP
This creates a tiny dipole
dur: ms
location: largely dentrides & cell body
delay: occur instantaneously and do not travel down the axon
size: Can summate under certain conditions rather then cancel each other out and then be recorded at great distance (scalp)
Luck, 2005

21. Best guess of a biophysical event that gives rise to a scalp ERP
The dipole of a single neuron is tiny
But under certain conditions the dipoles from many neurons will summate 1. occur approximately at the same time across 1000 – neurons
2. be spatially aligned
3. receive all excitatory or all inhibitory input
This is most likely in cortical pyramidal cells, which are aligned perpendicular to the surface of the cortex
Luck, 2005

22. ?
Purkinje cells in the cerebellar cortex are beautifully aligned with each other and oriented perpendicular to the cortical surface. Can we measure them in EEG?

23. Assume we have many aligned dipoles now..
…then the signal still needs to reach the scalp
The brain is conductive material (volume conduction).
The voltage on the surface will thus depend on
The position and orientation of the generator dipole (equivalent current dipole)
The resistance and shape of the various components of the head (brain, skull, scalp, eye holes)
Luck, 2005

24. Assume the signal has reached the skull…
…what does it look like?
Electricity spreads out through the conductor  blur
Tends to follow the path of least resistance
The skull has high resistance
Travels laterally when reaching the skull
More blur *
BUT nicely electricity travels nearly as fast a light. So the signal is instantaneous!
Luck, 2005
* There are clever algorithms that calculate the ‘skull’-blur and reduce it.

25. How do I find out where the signal comes from?
Forward problem: If you knew the location and orientation of the dipoles and the conductance of the volume, the you could compute the distribution of voltage.
Inverse problem: ‘ill-posed’. An infinite number or different dipole configurations can produce any given voltage.  use specific constraints
No perfect tool out there yet
 talks on source reconstruction
Luck, 2005

26. Sum: ERP components in the Evoked Model
tx = xth point in time
i = trial index
k = total number of single trials
- reflect neural activity in rather localized brain regions that are involved in the processing of a stimulus and/or task.
- reflect a sequential process
independent of the background activity
- ERP components do not interact with prestimulus EEG
Central assumption is that the noise component apporximates zero or many trials
Klimesch et al., 2007; Luck, 2005

27. Critique ERP Components in the Evoked Model
(e1) EEG oscillations do not serve a specific function  Negative evidence
(e2) No correlation/ interaction between preand poststimulus EEG  Negative evidence
(e3) ERP components and (power of) ongoing EEG are additive  Preliminary negative evidence
(e4) ERP components do not interact with prestimulus EEG  Negative evidence
(e5) ERP latencies/ interpeak latencies and evoked power are not associated with frequencies of dominant EEG oscillations  Negative evidence
(e6) Dipole source analysis yields meaningful results  Preliminary positive evidence
(e7) ERP components are generated along a pathway of localized neural activation  Preliminary positive evidence
Klimesch et al., 2007

28. Oscillations in EEG Klimesch et al., 2007
Brain oscillation theory offers an alternative explanation
for the generation of ERP components that is completely
different from that of the evoked model. The interpretation
of the event-related EEG response is a logical consequence
from results obtained for the ongoing EEG: Oscillations
reflect different sensory and cognitive processes and play
an important role for the timing of neural processes also
for the event-related EEG response.
Klimesch et al., 2007

29. Event-Related Phase Reorganization (ERPR) Model
a= amplitude
w = frequency
tx = xth time point
y = trial index
No noise term
An ERP generated by ERPR can be understood as the
sum of instantaneous amplitudes (a) of different task
relevant frequencies o at time point t , averaged over trials k :
Unlike the evoked model, ERP components generated
by ERPR lack an additive noise component. Noise may be
due to the influence of two different factors: (i) to the
number of frequencies that are not task relevant and/or (ii)
to the extent task relevant frequencies do not align in
absolute phase within certain time windows. Both factors
will reduce the amplitude of the ERP.
Thus, for optimal processing of a
stimulus, phase reorganization is obligatory.
For optimal processing of a stimulus, phase reorganization is obligatory.
This doesn’t mean a phase reset, but instantaneous phase alignment (IPA)
This means: Event-related alignment in phase between (task relevant) frequencies
Klimesch et al., 2007

30. How is noise embedded in the ERPR Model?
Input from non-task relevant and thus not aligned oscillations
Imperfect alignement between phases of task relevant oscillations

31. Oscillations and ERPs Klimesch et al., 2007
oscillatory model assumes that evoked components are generated by a combination of three processes
Phase reorganization of ongoing oscillations,
evoked oscillations
IPA
Klimesch et al., 2007

32. ER Components
Early processing of a stimulus in subcortical regions, oscillatory activity might be induced in the cortex (much earlier then the first event-related components)  widely distributed neuronal process
Evoked components are localized processes. Distribution can be explained through volume conduction
It should also be noted that the alignment of phase of different assemblies may lead to a large and transient increase in neural activity during the excitatory period of alignment that may resemble at least in part an evoked component. But in a physiological sense there would be a large difference in the
functional meaning of evoked components and ERPRdriven components. Although ERPR-driven components might also appear as a serial sequence of components, they
are most likely generated by parallel processes. As an example, after very early processing of a stimulus in
subcortical regions (such as the reticular formation and thalamus) oscillatory activity might be induced in the cortex much earlier than the first event-related components
appear. Dynamic interactions between oscillations and respective cell assemblies may be important for the latency and anatomical site of IPA and the appearance of an eventrelated component. As illustrated in Fig. 3 , oscillatory activity might be a very distributed neural process, and
some components such as the P1 might be generated simultaneously—or with a latency shift—at different sites (cf. sites 1 and 2 in Fig. 3(A) ). Recent empirical evidence, showing that the P1 can be described as traveling alpha wave propagating from occipital to parietal sites suggests that early ERP components may be generated by a widely distributed neural process (Klimesch et al., 2007b ). Evoked components, on the other hand, are more compatible with a localized process and the appearance of this component at different scalp sites might be explained by volume conduction due to a large dipole at a particular region in the brain.
Klimesch et al., 2007

33. What would you consider to be positive evidence for the ERPR Model?

34. Implications of the ERPR Model
(p1) EEG oscillations are associated with specific functions Positive evidence
(p2) Interaction between pre- and poststimulus EEG  Positive evidence
(p3) Phase reset of task relevant ongoing oscillations, generation of task relevant evoked oscillations, IPA between task relevant oscillations  Positive evidence
(p4) ERP components are determined by IPA; reset and/or IPA takes place not necessarily at pos. or neg. peak  Positive evidence
(p5) ERP latencies/ interpeak latencies and evoked power reflect frequency characteristics of functionally relevant oscillations  Positive evidence
(p6) Dipole source analysis may not be considered an adequate method  Not investigated
(p7) Evoked components reflect parallel distributed neural processes Not investigated

35. Pitfalls

36. Voltage Peaks are not special
Rule 1. It doesn’t make sense to measure
peak amplitude and peak latency to measure
the magnitude and timing of ERP components.
Luck, 2005

37. Peak Shapes Are not the Same as Component Shapes
Rule 2. It is impossible to estimate the time course or peak latency of a latent ERP component by looking at a single ERP waveform.
Rule 3. It is dangerous to compare an experimental effect (i.e.,the difference between two ERP waveforms) with the raw ERP waveforms.
Luck, 2005

38. Peak amplitudes are different from component sizes…
Rule 4. Differences in peak amplitude do not necessarily correspond with differences in component size, and differences in peak latency do not necessarily correspond with changes in component timing.
Luck, 2005

39. Averaging changes your data
Rule 5. Never assume that an averaged ERP waveform accurately
represents the individual waveforms that were averaged together.
In particular, the onset and offset times in the averaged waveform
will represent the earliest onsets and latest offsets from the individual
trials or individual subjects that contribute to the average.
Luck, 2005

40. The way to go… Strategy 1. Focus on a Specific Component
Strategy 2. Use Well-Studied Experimental Manipulations
Strategy 3. Focus on Large Components
Strategy 4. Isolate Components with Difference Waves
Strategy 5. Focus on Components That Are Easily Isolated
Strategy 6. Component-Independent Experimental Designs
Avoiding Confounds and Misinterpretations

41. THANK YOU
References:
Luck, 2005
Klimesch, 2007