BCG artifacts are more challenging to remove, since they share frequency content with EEG activity. Currently-existing BCG removal algorithms cause loss of signal power in the underlying EEG, so we performed single-trial classification (described in section 2.4) on the data prior to BCG artifact removal. This is a justified practice because our classifier identifies discriminating components that are likely to be orthogonal to BCG. In order to compute scalp topographies of these discriminating components, BCG artifacts were removed from the continuous gradient-free data using a principal components analysis (PCA) method (Goldman et al., 2009; Sajda et al., 2010). First the data were low-passed at 4 Hz to extract the signal within the frequency range where BCG artifacts are observed, and then the first two principal components were determined. The channel weightings corresponding to those components were projected onto the broadband data and subtracted out. These BCG-free data were then re-referenced from the 43 bipolar channels to the 34-electrode space to calculate scalp topographies of EEG discriminating components.
