Suppression of Eye-Blink Associated Artifact Using Single Channel EEG Data by Combining Cross-Correlation With Empirical Mode Decomposition

Abstract

Eye-blink signals are the major sources of artifacts in the electroencephalogram (EEG). Conventionally, the wavelet-based approach is used for analysis and suppression of these artifacts from single channel EEG data by applying the threshold in the decomposition of the signal in terms of a set of predefined basis functions. Here, we report a novel approach for effective suppression of these artifacts by combining the data-driven technique called empirical mode decomposition (EMD) with cross-correlation. The contaminated EEG signal is decomposed into a series of intrinsic mode functions (IMFs) using EMD; in this decomposition, some of the IMFs capture features corresponding to the eye-blink signals and are termed as noisy IMFs. The artifact suppression proposed in this paper relies on the elimination of noisy IMFs based on cross-correlation with a suitable template extracted from the contaminated segment of EEG. We illustrate the method by applying it for suppressing artifacts corresponding to eye blinks during the measurement of visual evoked EEG response and compare it with conventionally used single channel wavelet technique.