minimize the contribution of potential outliers via iterative reweighted least squares that minimizes the impact of outliers with large leverage (O'Leary, 1990). In this regard, robust regression has a significant advantage over trial averaging. Specifically, during standard trial-averaging, outliers may affect the averaged data. However, with robust regression, outliers are de-weighted and therefore have minimal effect on the overall result. Ultimately, this procedure resulted in a time × frequency × space (electrodes) × condition matrix of b values for each subject. The regression was conducted separately for each condition rather than including condition as a covariate because the four conditions are categorical. Because these b values are normally distributed under the null hypothesis, they can be entered into standard parametric statistics such as t-tests and repeated-measures ANOVA. Before averaging across subjects, b values were standardized by scaling the coefficients by their SDs; this ensured that the coefficients were in the same scale and thus directly comparable across time, frequency, electrodes, and subjects.
