1.3 Peak Amplitudes and Latencies Are Measurable Independent of
the SNR
A common argument against measuring peak amplitude and latency at the ST
level is that the SNR is too low to identify the components of interest
(Blankertz, Lemm, Treder, Haufe & Müller, 2011; Chamanzar,
Malekmohammadi, Bahrani, & Shabany, 2015; Jaśkowski & Verleger, 1999).
In other words, there is the assumption that the signal in ST ERPs
representing the brain activity elicited by a stimulus event is
generally smaller than the random background brain noise and, therefore,
cannot be directly seen or measured in the single trial. Although viewed
as a descriptive indication of the quality of the data, there is little
information in the literature specifying the minimum SNR needed for
obtaining reliable and valid measures of brain activity in the averaged
ERP or in single trials. This may be due to the fact that efforts to
understand SNR have often been conducted by combining synthesized
signals with recordings of resting brain signals, then studying
computational methods retrieving the synthesized signal (Hu et al.,
2011; Jaśkowski & Verleger, 1999).
Furthermore, SNR has been calculated through a variety of methods (Luck,
2005; Spencer, 2005). For example, one method is to calculate the ratio
of the largest evoked potential peak-to-peak amplitude and the mean
amplitude of the baseline period prior to the presentation of the
stimulus (Pfurtscheller et al., 1987). Another method is to divide the
standard deviation of the averaged ERP by the standard deviation of the
noise as estimated from amplitudes of oscillatory signals recorded
during the resting states (Ouyang et al., 2015). Alternatively, SNR has
been defined as the signal being the amplitude of a peak in ST ERP,
while the noise, or measurement error, defined as uninformative
intra-subject variability created by neural activity not activated by
the stimulus as well as non-neural artifacts (Blankertz et al., 2011;
Ribeiro et al., 2016). While this latter approach can be viewed as more
compatible with test and measurement theory, this definition of noise
may contain elements of meaningful variability across single trials.
Independent of how SNR is calculated, a low SNR may not be problematic
if the effect of the baseline noise can be shown to be relevant to brain
processing post-stimulus (for review see Rey et al., 2015). Pre-stimulus
brain activity has been shown to consistently influence later ERP
components after the presentation of a task-relevant stimulus in both
young adults and older adults, even when significant age-related changes
in post-stimulus neural processing are found. For example, a greater
pre-stimulus gamma power correlated with larger P300 amplitudes and
longer response times (Reinhart, Mathalon, Roach, & Ford, 2011), and
pre-stimulus alpha power influenced errors on a task (Shou, Dasari, &
Ding, 2015). In another study, McNair, Kayser, and Kayser (2019) found
that the amplitudes of P1 and N1 were significantly larger and the
latencies of N1 and P2 were greater in the older adults compared to
young adults, while the pre-stimulus rhythmic brain activity influence
on post-stimulus brain activity remained constant across age groups.