Novel approach to remove Electrical Shift and Linear Trend artifact from single channel EEG

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

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.

Comparative Analysis of Single-Channel and Multi-Channel Classification of Sleep Stages Across Four Different Data Sets

Background: Manually labeling sleep stages is time-consuming and labor-intensive, making automatic sleep staging methods crucial for practical sleep monitoring. While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. Methods: In this study, four public data sets-Sleep-SC, APPLES, SHHS1, and MrOS1-are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging. Results: The experimental results show that, for sleep staging using 2-5 classes, a combination of single-channel electroencephalography (EEG) and dual-channel electrooculography (EOG) consistently outperforms single-channel EEG with single-channel EOG, which in turn outperforms single-channel EEG or single-channel EOG alone. For instance, for five-class sleep staging using the MrOS1 data set, the combination of single-channel EEG and dual-channel EOG resulted in an accuracy of 87.18%, whereas the combination of single-channel EEG and single-channel EOG yielded an accuracy of 85.77%. In comparison, single-channel EEG alone achieved an accuracy of 85.25% and single-channel EOG alone achieved an accuracy of 83.66%. Conclusions: This study highlights the significance of combining EEG and EOG signals in automatic sleep staging, while also providing valuable insights for the channel design of portable sleep monitoring devices.