VME-EFD : A novel framework to eliminate the Electrooculogram artifact from single-channel EEGs.

Abstract

The diagnosis of neurological disorders often involves analyzing EEG data, which can be contaminated by artifacts from eye movements or blinking (EOG). To improve the accuracy of EEG-based analysis, we propose a novel framework, VME-EFD, which combines Variational Mode Extraction (VME) and Empirical Fourier Decomposition (EFD) for effective EOG artifact removal. In this approach, the EEG signal is first decomposed by VME into two segments: the desired EEG signal and the EOG artifact. The EOG component is further processed by EFD, where decomposition levels are analyzed based on energy and skewness. The level with the highest energy and skewness, corresponding to the artifact, is discarded, while the remaining levels are reintegrated with the desired EEG. Simulations on both synthetic and real EEG datasets demonstrate that VME-EFD outperforms existing methods, with lower RRMSE (0.1358 versus 0.1557, 0.1823, 0.2079, 0.2748), lower ΔPSD in theαband (0.10 ± 0.01 and 0.17 ± 0.04 versus 0.89 ± 0.91 and 0.22 ± 0.19, 1.32 ± 0.23 and 1.10 ± 0.07, 2.86 ± 1.30 and 1.19 ± 0.07, 3.96 ± 0.56 and 2.42 ± 2.48), and higher correlation coefficient (CC: 0.9732 versus 0.9695, 0.9514, 0.8994, 0.8730). The framework effectively removes EOG artifacts and preserves critical EEG features, particularly in theαband, making it highly suitable for brain-computer interface (BCI) applications.