Decoding non-linearity for effective extraction of the eye-blink artifact pattern from EEG recordings

Abstract

• Effective extraction of ocular artifact by combining non-linear kernels with EEMD. • Maximize the extracted artifact and minimize the distortion in the brain signal. • The proposed method outperforms wavelet and EEMD with linear kernel based approach. Electroencephalogram (EEG) is a non-invasive measurement of electrical signal on the scalp originated due to neuronal activity. EEG signals associated with cortical activity are orders of magnitude lower in amplitude compared to other parasitic signals such as eye-blink artifacts , which contaminate the recorded EEG data making it imperative for the investigators to adopt an effective artifact suppression strategy. In all the previous studies, suppression of the pattern related to the artifacts was performed based on the assumption of a linear interaction between the source of artifact (eye-blink) and the brain signal (EEG data). This paper presents a novel methodology by considering the non-linear interaction between the artifact signal associated with the eye-blink and the contaminated EEG data using kernel functions . In the present work, adaptive data-driven approach called Ensemble Empirical Mode Decomposition (EEMD) is hybridized with kernel Principal Component Analysis (kPCA) to decode the non-linear interaction. The contaminated segment of EEG data is decomposed by the EEMD technique into a series of basic building blocks called intrinsic mode functions (IMFs). The features of the eye-blink signals are captured by some of these IMFs; subsequently, effective extraction of the ocular artifact from the IMFs is performed by kPCA using non-linear kernel functions (radial basis function, second and third-order polynomial function). In the present study, technique used for artifact suppression relies on the extraction of ocular artifact signal from IMFs based on optimizing the different parameters of the kPCA. Compared with other techniques used in previous studies, the proposed method (based on third-order polynomial kernel function) is capable of effectively extracting the pattern related to the ocular artifacts from the single channel contaminated EEG data with low distortion of the EEG signal.