Since its original discovery and the first published paper of Berger (1929), the electroencephalogram (EEG) has become an ubiquitous diagnostic utility and research tool of remarkable impact in clinical praxis (e.g., Shibasaki et al., 2014) and basic neuroscience (e.g., Gevins, 1998) alike. Among the diverse neuroimaging methods that have more recently become available, EEG is unique in its combined ability to represent neuronal activity 1) directly (i.e., without relying on an intermediate response system) and 2) in real time, while also being 3) non-invasive and 4) comparatively inexpensive. Because EEG is a time-varying voltage measure (i.e., potential difference) of electrical fields, it is limited by the fact that measuring potentials always require a point of reference (i.e., the EEG is reference-dependent) and by the circumstance that electrical fields originating from any neuronal structure will influence the electrical potential throughout the brain and surrounding physiological tissue (i.e., the EEG signal is a mixture of sources and is smeared by volume conduction). Both of these limitations can be mitigated by use of the surface Laplacian (SL), which is a simple mathematical transformation applied to the EEG surface potentials.
