Abstract
Among the different brain imaging techniques, electroencephalography (EEG) is classically considered as having an excellent temporal resolution, but a poor spatial one. Here, we argue that the actual temporal resolution of conventional (scalp potentials) EEG is overestimated, and that volume conduction, the main cause of the poor spatial resolution of EEG, also distorts the recovered time course of the underlying sources at scalp level, and hence degrades the actual temporal resolution of EEG. While Current Source Density (CSD) estimates, through the Surface Laplacian (SL) computation, are well known to dramatically reduce volume conduction effects and hence improve EEG spatial resolution, its positive impact on EEG temporal resolution is much less recognized. In two simulation studies, we first show how volume conduction and reference electrodes distort the scalp potential time course, and how SL transform provides a much better spatio-temporal description. We then exemplify similar effects on two empirical datasets. We show how the time courses of the scalp potentials mis-estimate the latencies of the relevant brain events and that CSD provides a much richer, and much more accurate, view of the spatio-temporal dynamics of brain activity.
Highlights
In the last fifteen years, our understanding of the brain–behavior relationship has dramatically improved, largely thanks to brain imaging techniques
Through this first simulation study, we illustrated how volume conduction and, to a lesser extend, reference electrode can affect the temporal resolution of EEG: by systematically varying the time course of one of two simulated cortical dipoles and measuring the recovered scalp potentials and Current Source Density (CSD) time courses, we showed that volume conduction makes the two time series converge toward each other, leading to an overestimation of the latency of the earliest activity, and an underestimation of the latest one
The actual recovered latencies largely depend on the amplitude ratio: the two peak latencies are always biased toward the largest source
Summary
In the last fifteen years, our understanding of the brain–behavior relationship has dramatically improved, largely thanks to brain imaging techniques (see below). Looking at “the brain in action” while it performs on some tasks allows a direct assessment of its functioning properties. This allows to better constraint functional, cognitive, models. In addition to account for behavioral performances, models must account for additional, intermediate, cerebral indices. The presence of an activity in region R supposed to implement a function F may inform us about the underlying processes involved in the task at hand (with some necessary precautions, though, see Poldrack, 2006; Vidal et al, 2015). The relative timing of two regions R1 and R2, and of functions F1 and F2 can provide essential clues about the underlying architecture
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