Single-trial analyses were conducted separately for power and phase. For power analyses, a robust regression was computed that estimated parameters at each time–frequency–space point for the following linear equation: Y = INT + b1RT + b2LUM + b3RT × LUM + E. Y is the data vector (power values at each time–frequency point across trials), INT is the intercept, b1–3 are regression coefficients, E is unexplained variance, and RT and LUM are trial vectors of the subject's reaction time and the stimulus luminance on each trial. Reaction time and luminance data were z-scored so that the interaction term was not dominated by reaction time, which has values an order of magnitude larger than luminance (note that this means the intercept simply accounts for Power Law scaling across frequencies and therefore is not of interest here). Robust regression was used to 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
