STUDY OF OCULAR ARTRIFACT REMOVAL METHODS FROM EEG

Abstract

Electroencephalography (EEG) signal is the recording of spontaneous electrical activity of the brain over a small period of time. Electroencephalography (EEG) signals are widely used in early diagnosis of brain diseases. During the data acquisition and transmission of EEG signal, signal gets corrupted by various types of noises e.g. electrode contact noise, ocular noise, electromyography noise etc. This work focuses on the removal of ocular artifact that strongly appears in frontal electrode. In this project the ocular artifact is removed from EEG signal using Ensemble Empirical Mode Decomposition(EEMD) and Spatial Constraint Independent Component Analysis (SCICA). Here, EEMD is used to disintegrate the artifactual EEG signal to find Implicit Mode Functions (IMFs). The artifactual IMFs are take out from the IMFs by separating the electrooculogram (EOG) related IMF signals. Now, the artifactual IMFs considered as the source to independent Component Analysis (ICA) as a result Independent Components (ICs) and mixing matrix are obtained. The artifactual ICs are separated using kurtosis and modified Mean Sample Entropy (mMSE). The mixing matrix is modified using spatial constraints. The restored IMFs are obtained by using inverse of modified mixing matrix and ICs. Finally, the artifact-free EEG signal is reconstructed by summing up artifact-free IMFs and restored IMFs. The proposed method is compared with other advanced methods in terms of mutual information, correlation, and coherence. The results show that the proposed method has better performance as compared other latest methods of ocular artifact removal from EEG.