4 The Analysis of Event-Related Potentials
57
4.2 General Considerations in ERP Analysis
ERP analysis is always preceded by a pre-processing step in which the data is digitally
filtered. Notch filters for suppressing power line contamination and band-pass filters
are common practice to increase the SNR and remove the direct current level [61].
If the high-pass margin of the filter is lower than 0.5 Hz, the direct current level can
be eliminated by subtracting the average potential (baseline) computed on a short
window before the ERP onset (typically 250 ms long). Researchers and clinicians are
often unaware of the signal changes that can be introduced by a digital signal filter, yet
the care injected in this pre-processing stage is well rewarded, since severe distortion
in signal shape, amplitude, latency and even scalp distribution can be introduced by
an inappropriate choice of digital filter [98].
There is consensus today that for a given class of ERPs only the polarities of
the peaks may be considered consistent for a given electrical reference used in the
EEG recording; the shape, latency, amplitude and spatial distribution of ERPs are
highly variable among individuals. Furthermore, even if within each individual the
shape may be assumed stable on average, there may be a non-negligible amplitude
and latency inter-sweep variability. Furthermore, the spatial distribution can be considered stable within the same individual and within a recording session, but may
vary from session to session, for instance, due to slight differences in electrode
positioning. Inter-sweep variability is caused by the combination of several experimental, biological and instrumental factors. Experimental and biological factors may
affect both latency and amplitude. Examples of experimental factors are the stimulus
intensity and the number of items in a visual search task [61]. Examples of biological factors are the subject’s fatigue, attention, vigilance, boredom and habituation
to the stimulus. Instrumental factors mainly affect the latency variability; the ERP
marking on the EEG recording may introduce a jitter, which may be non-negligible if
the marker is not recorded directly on the EEG amplification unit and appropriately
synchronized therein, or if the stimulation device features a variable stimulus delivery delay. An important factor of amplitude variability is the ongoing EEG signal;
large artifacts and high energy background EEG (such as the posterior dominant
rhythm) may affect differently the sweeps, depending on their amplitude and phase,
artificially enhancing or suppressing ERP peaks.
Special care in ERP analysis must be undertaken when we record overlapping
ERPs, since in this case simple averaging results in biased estimations [85, 99, 100].
ERPs are non-overlapping if the minimum inter-stimulus interval (ISI) is longer than
the length of the latest recordable ERP. There is today increasing interest in paradigms
eliciting overlapping ERPs, such as some odd-ball paradigms [21] and rapid image
triage [104], which are heavily employed in brain-computer interfaces for increasing
the information transfer rate [101] and in the study of eye-fixation potentials, where
the “stimulus onset” is the time of an eye fixation and saccades follow rapidly [86].
The strongest distortion is observed when the ISI is fixed. Less severe is the distortion
when the ISI is drawn at random from an exponential distribution [21, 85].
57
4.2 General Considerations in ERP Analysis
ERP analysis is always preceded by a pre-processing step in which the data is digitally
filtered. Notch filters for suppressing power line contamination and band-pass filters
are common practice to increase the SNR and remove the direct current level [61].
If the high-pass margin of the filter is lower than 0.5 Hz, the direct current level can
be eliminated by subtracting the average potential (baseline) computed on a short
window before the ERP onset (typically 250 ms long). Researchers and clinicians are
often unaware of the signal changes that can be introduced by a digital signal filter, yet
the care injected in this pre-processing stage is well rewarded, since severe distortion
in signal shape, amplitude, latency and even scalp distribution can be introduced by
an inappropriate choice of digital filter [98].
There is consensus today that for a given class of ERPs only the polarities of
the peaks may be considered consistent for a given electrical reference used in the
EEG recording; the shape, latency, amplitude and spatial distribution of ERPs are
highly variable among individuals. Furthermore, even if within each individual the
shape may be assumed stable on average, there may be a non-negligible amplitude
and latency inter-sweep variability. Furthermore, the spatial distribution can be considered stable within the same individual and within a recording session, but may
vary from session to session, for instance, due to slight differences in electrode
positioning. Inter-sweep variability is caused by the combination of several experimental, biological and instrumental factors. Experimental and biological factors may
affect both latency and amplitude. Examples of experimental factors are the stimulus
intensity and the number of items in a visual search task [61]. Examples of biological factors are the subject’s fatigue, attention, vigilance, boredom and habituation
to the stimulus. Instrumental factors mainly affect the latency variability; the ERP
marking on the EEG recording may introduce a jitter, which may be non-negligible if
the marker is not recorded directly on the EEG amplification unit and appropriately
synchronized therein, or if the stimulation device features a variable stimulus delivery delay. An important factor of amplitude variability is the ongoing EEG signal;
large artifacts and high energy background EEG (such as the posterior dominant
rhythm) may affect differently the sweeps, depending on their amplitude and phase,
artificially enhancing or suppressing ERP peaks.
Special care in ERP analysis must be undertaken when we record overlapping
ERPs, since in this case simple averaging results in biased estimations [85, 99, 100].
ERPs are non-overlapping if the minimum inter-stimulus interval (ISI) is longer than
the length of the latest recordable ERP. There is today increasing interest in paradigms
eliciting overlapping ERPs, such as some odd-ball paradigms [21] and rapid image
triage [104], which are heavily employed in brain-computer interfaces for increasing
the information transfer rate [101] and in the study of eye-fixation potentials, where
the “stimulus onset” is the time of an eye fixation and saccades follow rapidly [86].
The strongest distortion is observed when the ISI is fixed. Less severe is the distortion
when the ISI is drawn at random from an exponential distribution [21, 85].