The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the ‘clean’ EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.
Uncovering the alterations of neural interactions within the brain during epilepsy is important for the clinical diagnosis and treatment. Previous studies have shown that the phase-amplitude coupling (PAC) can be used as a potential biomarker for locating epileptic zones and characterizing the transition of epileptic phases. However, in contrast to the θ-γ coupling widely investigated in epilepsy, few studies have paid attention to the β-γ coupling, as well as its potential applications. In the current study, we use the modulation index (MI) to calculate the scalp electroencephalography (EEG)-based β-γ coupling and investigate the corresponding changes during different epileptic phases. The results show that the β-γ coupling of each brain region changes with the evolution of epilepsy, and in several brain regions, the β-γ coupling decreases during the ictal period but increases in the post-ictal period, where the differences are statistically significant. Moreover, the alterations of β-γ coupling between different brain regions can also be observed, and the strength of β-γ coupling increases in the post-ictal period, where the differences are also significant. Taken together, these findings not only contribute to understanding neural interactions within the brain during the evolution of epilepsy, but also provide a new insight into the clinical treatment.
ObjectiveTo systematically review the association between acid suppressive drug use and fracture risk in children and adolescents. MethodsThe PubMed, Web of Science, EMbase, Cochrane Library, CNKI and WanFang Data databases were electronically searched to collect observational studies on the association between acid suppressive drug use and fracture risk in children and adolescents from inception to October 1, 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using R4.1.2 software. ResultsA total of 6 studies involving 1 886 423 children and adolescents were included. Meta-analysis results showed that the use of proton pump inhibitors (PPIs) increased the risk of fracture (RR=1.19, 95%CI 1.10 to 1.29, P<0.01), whereas the use of histamine H2 receptor antagonists (H2RAs) did not increase the risk of fracture (P>0.05). Subgroup analysis showed that PPIs use increased risk of fracture in the lower limb and other sites (P<0.05). ConclusionCurrent evidence shows that PPIs can increase fracture risk in children and adolescents, but no association has been found between the use of H2RAs and increased fracture risk in this group. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.