An Efficient Framework to Automatic Extract EOG Artifacts from Single Channel EEG Recordings

Abstract

In health care applications portable electroencephalogram (EEG) systems are frequently used to record and process the brain signals due to easy of use and low cost. Electrooculogram (EOG) is the major high amplitude low frequency artifact eye blink signal, which misleads the diagnosis activity of decease. Hence there is demand for artifact remove techniques in portable single EEG devices. In this work presented automatic extraction of EOG artifact by integrating Fluctuation based Dispersion Entropy (FDispEn) with Singular Spectral Analysis (SSA) and Adaptive noise canceller(ANC). The proposed model successfully identifies artifact signal component based on entropy values at different SNR and remove it with ANC for better performance. This method avoid the dependency on threshold to identify artifact subspace unlike previous existed DWT,SSA and Adaptive SSA methods combined with ANC. Proposed method is evaluated on synthetic data and real EEG data set and eliminate eyeblink artifact by preserving the low frequency EEG content. The performance of proposed method shows superiority in performance metrics over existing algorithms.