Dictionary Learning and Greedy Algorithms for Removing Eye Blink Artifacts from EEG Signals

Brain activities recorded using electroencephalography (EEG) device are mostly contaminated with eye blink (EB) artifact. This artifact leads to poor performance of brain-computer interface (BCI) systems. Hence, for the better performance of BCI systems, EB artifacts need to be removed from EEG signals without any loss of information. Of several methods that exists in the literature to remove EB artifacts, sparsity-based method is one among them and it proved to be good in removing EB artifacts. In the sparsity-based method, an over-complete dictionary is learned from the EEG data itself using K-SVD-based algorithm and is designed to model EB characteristics. In this work, two different greedy algorithms, namely orthogonal matching pursuit (OMP) and adaptive OMP (A-OMP), have been applied over K-SVD algorithm to check its performance on removing EB artifacts from EEG signals. To prove the efficiency of the greedy algorithms, the experiment is done with real EEG data. The results observed show that A-OMP is computationally more efficient and can accomplish successful sparse representation on EEG signals. Moreover, this sparsity-based algorithm can eliminate EB artifact accurately from the EEG signals.