An automatic ocular artifacts removal method based on wavelet-enhanced canonical correlation analysis.

In this paper, a new method for automatic ocular artifacts (OA) removal in EEG recordings is proposed based on wavelet-enhanced canonical correlation analysis (wCCA). Compared to three popular ocular artifacts removal methods, wCCA owns two advantages. First, there is no need to identify the artifact components by subjective visual inspection, because the first canonical components found by CCA for each dataset, also the most common component between the left and right hemisphere, are definitely related to artifacts. Second, quantitative evaluation of the corrected EEG signals demonstrates that wCCA removed the most ocular artifacts with minimal cerebral information loss.