Removal of blink artifacts in single channel EEG

Blinks are one of the main sources of distortion in electroencephalographic (EEG) data. Discarding blink-contaminated segments of EEG data would result in considerable information loss when interpreting and analyzing data. This study presents a simple method of blink filtering using a Savitzky-Golay (SG) smoothing filter and compares it to Independent Component Analysis (ICA), a widely accepted method of blink removal. The SG-based blink filtering method arose from the need for blink removal in EEG systems with a low number of channels and limited processing power, specifically reading from the forehead location where the blink disturbance is severe. Real and simulated data were investigated with respect to the method's performance. Using correlation and mutual information to measure performance, the results reveal that the SG-based method can effectively remove blink artifacts and produces results comparable to those obtained using ICA.