Single-trial multiple regression has been applied to EEG data. For example, multiple regression of visual stimulus properties demonstrated early responses to noise and feature processing during face viewing (Rousselet et al., 2008), which is affected by aging (Rousselet et al., 2009). Eichele et al. (2010) recently used single-trial multiple regression to remove variance due to factors less relevant to the hypotheses (e.g., P300 modulation). Regression is also one method for removing blink and other artifacts (Schwind and Dormann, 1986). In these studies, regressions were performed on time-domain EEG data. However, because EEG data are driven largely by oscillatory cortical processes, considerable information in EEG may be contained in the frequency domain and therefore lost in the time domain (Cohen, 2011b). Therefore, in this study we conducted single-trial multiple regression over time–frequency estimates of power, and we introduce a method to link non-linear phase distributions to trial-varying behavioral and experimental variables.
