Removal of ocular artifacts from the EEG: a comparison between time-domain regression method and adaptive filtering method using simulated data

We recently proposed an adaptive filtering (AF) method for removing ocular artifacts from EEG recordings. The method employs two parameters: the forgetting factor lambda and the filter length M. In this paper, we first show that when lambda = M = 1, the adaptive filtering method becomes equivalent to the widely used time-domain regression method. The role of lambda (when less than one) is to deal with the possible non-stationary relationship between the reference EOG and the EOG component in the EEG. To demonstrate the role of M, a simulation study is carried out that quantitatively evaluates the accuracy of the adaptive filtering method under different conditions and comparing with the accuracy of the regression method. The results show that when there is a shape difference or a misalignment between the reference EOG and the EOG artifact in the EEG, the adaptive filtering method can be more accurate in recovering the true EEG by using an M larger than one (e.g. M = 2 or 3).