An Effective and Robust Framework for Ocular Artifact Removal From Single-Channel EEG Signal Based on Variational Mode Decomposition

TLDR: This paper proposes a robust framework for the detection and removal of OAs based on variational mode decomposition (VMD) and turning point count and demonstrates that this framework outperforms few existing OAs removal techniques in removing OAs from single-channel EEG signal.

Abstract: Removal of ocular artifacts (OAs) from electroencephalogram (EEG) signal is crucial for accurate and effective EEG analysis and brain-computer interface research. The elimination of OAs is quite challenging in absence of reference electro-oculogram and in single-channel EEG signal using existing independent component analysis based OA removal techniques. Though few of the recent OAs removal techniques suppress the OAs in the single-channel significantly, these techniques introduce distortion in clinical features of the EEG signal during artifact removal process. To address these issues, in this paper, we propose a robust framework for the detection and removal of OAs based on variational mode decomposition (VMD) and turning point count. The proposed framework exploits the effectiveness of VMD in two stages denoted as VMD-I and VMD-II respectively. The proposed framework has four components: EEG signal decomposition into two modes using VMD-I; rejection of low-frequency baseline components; processed EEG signal decomposition into three modes using VMD-II; rejection of mode containing OAs based on turning point count based threshold criteria. We evaluate the effectiveness of the proposed framework using the EEG signals in presence of various ocular artifacts with different amplitudes and shapes taken from three standard databases including, Mendeley database, MIT-BIH Polysmnographic database and EEG during mental arithmetic tasks database. Evaluation results demonstrate that proposed framework eliminates OAs with minimal loss in valuable clinical features in both reconstructed EEG signal and in all local rhythms. Furthermore, subjective and objective comparative analysis demonstrate that our framework outperforms few existing OAs removal techniques in removing OAs from single-channel EEG signal.