56
M. Congedo
characteristics depend on the type (class) of event. Each realization of an ERP is
named a sweep or trial. Important pioneering discoveries of ERPs include the contingent negative variation [96], the P300 [88], the mismatch negativity [68] and the
error-related negativity [31]. Another class of time-locked phenomena are the EventRelated De/Synchronizations (ERDs/ERSs, [77]), which are not phase-locked. In
order to keep a clear distinction between the two, ERD/ERS are referred to as induced
phenomena, while ERPs are referred to as evoked phenomena [89]. Traditionally,
ERPs have been conceived as stereotypical fluctuations with approximately fixed
polarity, shape, latency, amplitude and spatial distribution. Accordingly, the ERP
fluctuations are independent from the ongoing EEG and superimpose to it in a timeand phase-locked fashion with respect to the triggering event. This yields the socalled additive generative model. Several observations have challenged this model
[23], suggesting the possibility that evoked responses may be caused by a process
of phase resetting, that is, an alignment of the phase of the spontaneous neuronal
activity with respect to the event [44, 57, 62]. According to this model, ERPs result
from time/frequency modulations of the ongoing activity of specific neuronal populations. Still another generative model of ERPs was introduced by [65] and [69].
These authors pointed out that ongoing EEG activity is commonly non-symmetric
around zero, as can be seen clearly in sub-dural recordings of alpha rhythms [58].
They proposed that averaging amplitude-asymmetric oscillations may create evoked
responses with slow components.
In this chapter, we consider several major methods currently used to analyze and
classify ERPs. In modern EEG, using a multitude of electrodes is the rule rather than
the exception, thus emphasis is given on multivariate methods, since these methods
can exploit spatial information and achieve higher signal-to-noise ratio (SNR) as
compared to single-electrode recordings. We consider the analysis in the time domain,
in the time-frequency domain and in the spatial domain. We also consider intertrial amplitude and latency variability as well as the case of overlapping ERPs.
We then consider useful tools for inferential statistics and classifiers for machine
learning specifically targeting ERP data. All the time-domain methods described in
this chapter are implicitly based on the additive model, but they may give meaningful
results even if the data is generated under other models. Time-frequency domain
methods can explicitly study the phase consistency of ERP components. We will
show an example analysis for each section. The real data examples in all but the
last figure concerns a visual P300 experiments where healthy adults play a braincomputer interface video-game named Brain Invaders [21]. This experiment is based
on the classical oddball paradigm and yields ERPs pertaining to a target class, evoked
by infrequent stimuli, and a non-target class, evoked by frequent stimuli.
M. Congedo
characteristics depend on the type (class) of event. Each realization of an ERP is
named a sweep or trial. Important pioneering discoveries of ERPs include the contingent negative variation [96], the P300 [88], the mismatch negativity [68] and the
error-related negativity [31]. Another class of time-locked phenomena are the EventRelated De/Synchronizations (ERDs/ERSs, [77]), which are not phase-locked. In
order to keep a clear distinction between the two, ERD/ERS are referred to as induced
phenomena, while ERPs are referred to as evoked phenomena [89]. Traditionally,
ERPs have been conceived as stereotypical fluctuations with approximately fixed
polarity, shape, latency, amplitude and spatial distribution. Accordingly, the ERP
fluctuations are independent from the ongoing EEG and superimpose to it in a timeand phase-locked fashion with respect to the triggering event. This yields the socalled additive generative model. Several observations have challenged this model
[23], suggesting the possibility that evoked responses may be caused by a process
of phase resetting, that is, an alignment of the phase of the spontaneous neuronal
activity with respect to the event [44, 57, 62]. According to this model, ERPs result
from time/frequency modulations of the ongoing activity of specific neuronal populations. Still another generative model of ERPs was introduced by [65] and [69].
These authors pointed out that ongoing EEG activity is commonly non-symmetric
around zero, as can be seen clearly in sub-dural recordings of alpha rhythms [58].
They proposed that averaging amplitude-asymmetric oscillations may create evoked
responses with slow components.
In this chapter, we consider several major methods currently used to analyze and
classify ERPs. In modern EEG, using a multitude of electrodes is the rule rather than
the exception, thus emphasis is given on multivariate methods, since these methods
can exploit spatial information and achieve higher signal-to-noise ratio (SNR) as
compared to single-electrode recordings. We consider the analysis in the time domain,
in the time-frequency domain and in the spatial domain. We also consider intertrial amplitude and latency variability as well as the case of overlapping ERPs.
We then consider useful tools for inferential statistics and classifiers for machine
learning specifically targeting ERP data. All the time-domain methods described in
this chapter are implicitly based on the additive model, but they may give meaningful
results even if the data is generated under other models. Time-frequency domain
methods can explicitly study the phase consistency of ERP components. We will
show an example analysis for each section. The real data examples in all but the
last figure concerns a visual P300 experiments where healthy adults play a braincomputer interface video-game named Brain Invaders [21]. This experiment is based
on the classical oddball paradigm and yields ERPs pertaining to a target class, evoked
by infrequent stimuli, and a non-target class, evoked by frequent stimuli.