Automated ocular artifact removal: comparing regression and component-based methods

Objective: The aim is to compare various fully automated methods for reducing ocular artifacts from EEG recordings. Methods: Seven automated methods including regression, six component-based methods for reducing ocular artifacts have been applied to 36 data sets from two different labs. The influence of various noise sources is analyzed and the ratio between corrected and uncorrected EEG spectra, has been used to quantify the distortion. Results: The results show that not only regression but also component-based methods are vulnerable to overor under-compensation and can cause significant distortion of EEG. Despite common belief, component-based methods did not demonstrate an advantage over the simple regression method. Conclusion: The newly proposed evaluation criterion showed to be an effective approach to evaluate 252 results from 36 data sets and 7 different methods. Significance: Currently, the regression method provides the most robust and stable results and is therefore the state-of-the-art-method for fully automated reduction of ocular artifacts.