Previous reports suggest advantages to conducting single-trial analyses using data from independent components (Debener et al., 2005, 2007; Eichele et al., 2009). In some cases, independent components and other methods (e.g., stable topographical maps based on clustering) provide converging results (De Lucia et al., 2010). Because independent components analysis results in a set of electrode weights that maximize temporally independent processes, this analysis might improve signal-to-noise. However, because each component is a weighting of all electrodes, specific maps must be selected for each subject. For example, in cognitive control experiments, maps are often selected based on a medial frontal topographical distribution (Debener et al., 2005; Eichele et al., 2010; Wessel and Ullsperger, 2010), although more sophisticated approaches are available based on higher-dimensional clustering (Onton et al., 2005). Therefore, in the interest of comparison with other approaches for analyzing single-trial data, we performed robust regression on the time–frequency representation of independent components. As described in the methods, components for each subject were selected according to a medial frontal and left/right lateral frontal topography (Figure 7).
