Comparing Different Methods for Analyzing ERP Signals

Event-Related Potential (ERP) signals obtained from EEG recordings are widely used for studying cognitive processes in spoken language processing. The computation of ERPs involves averaging over multiple participants and multiple stimuli. Especially with speech stimuli, which also evoke substantial exogenous excitation, even averaging within conditions results in pooling many sources of variance. This raises questions about the statistical processing needed to uncover reliable differences between conditions. In this study we investigate differences between ERPs when participants listened to full and reduced pronunciations of verb forms in Dutch, in isolation and in mid-sentence position. Conventional statistical analysis uncovers some (but not all) differences between full and reduced forms in isolation, but not in mid-sentence position. In this paper, we show that linear mixed models (lmer) and generalized additive models (gam), which are able to account for participantand stimulus-related variance may uncover more effects than conventional statistical models. However, depending on the complexity of the data, lmer and gam models may not be able to fit the data closely enough to warrant blind interpretation of the summary output. We discuss opportunities and threats of these approaches to analyzing ERP signals.