Removal of Eye Blink Artifacts from EEG signals using Sparsity

Sreeja S R, Rajiv Ranjan Sahay, Debasis Samanta, Pabitra Mitra

Abstract:

Neural activities recorded using electroen-
cephalography (EEG) are mostly contaminated with eye
blink (EB) artifact. This results in undesired activation of
brain–computer interface (BCI) systems. Hence, removal
of EB artifact is an important issue in EEG signal analy-
sis. Of late, several artifact removal methods have been re-
ported in the literature and they are based on independent
component analysis (ICA), thresholding, wavelet transfor-
mation, etc. These methods are computationally expensive
and result in information loss which makes them unsuit-
able for online BCI system development. To address the
above problems, we have investigated sparsity-based EB
artifact removal methods. Two sparsity-based techniques
namely morphological component analysis (MCA) and K-
SVD-based artifact removal method have been evaluated
in our work. MCA-based algorithm exploits the morpholog-
ical characteristics of EEG and EB using predefined Dirac
and discrete cosine transform (DCT) dictionaries. Next, in K-
SVD-based algorithm an overcomplete dictionary is learned
from the EEG data itself and is designed to model EB charac-
teristics. To substantiate the efficacy of the two algorithms,
we have carried out our experiments with both synthetic
and real EEG data. We observe that the K-SVD algorithm,
which uses a learned dictionary, delivers superior perfor-
mance for suppressing EB artifacts when compared to MCA
technique. Finally, the results of both the techniques are
compared with the recent state-of-the-art FORCe method.
We demonstrate that the proposed sparsity-based algo-
rithms perform equal to the state-of-the-art technique. It is
shown that without using any computationally expensive
algorithms, only with the use of over-complete dictionaries
the proposed sparsity-based algorithms eliminate EB arti-
facts accurately from the EEG signals.