Statistical analyses have relied on ERP component measures employing a “region-of-interest” approach, in which the topographic ERP signal is reduced to a few spatially smeared sites, and is also subject to experimenter bias in the selection or grouping of electrodes (Kayser & Tenke, 2005). Although the need to systematically identify the olfactory ERP component structure (i.e., how many major components with what temporal, spatial, and functional characteristics) has long been recognized (Lorig, 2000), only preliminary efforts have been made to date. ERP components are classically conceived as an electrophysiologic correlate of the underlying neuronal generators associated with information processes (cf. Kayser & Tenke, 2003). This conceptual definition implies that an ERP component is characterized by (1) temporal (latency), (2) spatial (scalp topography), and (3) functional (task or condition) specificity (e.g., Donchin et al., 1977; Fabiani, Gratton, & Coles, 2000). However, the identification and measurements of “obvious” peaks and troughs in the ERP waveforms as meaningful entities can be misleading. Specifying peaks in noisy waveforms (a problem not resolved but rather aggravated by using an automated computer algorithm) and determining area
