Artifact reduction algorithms are often designed according to the fact that the GA and CBA remain relatively stable and independent of the EEG signals. The most widely used (and perhaps still the most effective) algorithms for removing the GA and CBA are based on average template subtraction [153, 154, 164]. The central strategy can be briefly described as follows. A signal-free artifact template is obtained by averaging the raw (or preprocessed) data phase-locked to the fMRI volume onsets or the ECG-derived heart-beat markers. Assuming the signal and the artifacts are additive and mutually independent, the EEG signal should be retained by subtracting the averaged artifact template from the original data. Although these procedures end up with a significant artifact reduction, some artificial residuals often remain present owing to possibly non-phased-locked or drifted GA or instable CBA. These residual artifacts can be further removed by use of independent component analysis (ICA), which allows us to identify and remove the noisy and artificial components, whose spatial, temporal and frequency patterns are in accordance with those of the artifact templates [127].
