The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Previous neuroimaging studies based on electroencephalography (EEG) microstate analysis have identified abnormal neural electric activity in patients with psychiatric diseases. However, the microstate information in individuals with different degrees of fear of heights (FoH) remains unknown so far. The aim of the study was therefore to explore the changes of EEG microstate characteristics in different FoH individuals when exposed to high-altitude stimulated by virtual reality (VR). First, acrophobia questionnaire (AQ) before the experiment and 32-channel EEG signals under the virtual high-altitude exposure were collected from 69 subjects. Second, each subject was divided into one of three levels of FoH including no-FoH, mild or moderate FoH (m-FoH) and severe FoH (s-FoH) groups according to their AQ scores. Third, using microstate analysis, we transformed EEG data into sequences of characteristic topographic maps and computed EEG microstate features including microstate basic parameters, microstate sequences complexity and microstate energy. Finally, the extracted features as inputs were sent to train and test an support vector machine (SVM) for classifying different FoH groups. The results demonstrated that five types of microstates (labeled as A, B, C, D and F) were identified across all subjects, of which microstates A-D resembled the four typical microstate classes and microstate F was a non-canonical microstate. Significantly decreased occurrence, coverage and duration of microstate F and transition probabilities from other microstates to microstate F in m-FoH and s-FoH groups were observed compared to no-FoH group. It was also demonstrated that both m-FoH and s-FoH groups showed a notable reduction in sample entropy and Lempel-Ziv complexity. Moreover, energies of microstate D for m-FoH group and microstate B for s-FoH group in right parietal, parietooccipital and occipital regions exhibited prominent decreases as comparison to people without FoH. But, no significant differences were found between m-FoH and s-FoH groups. Additionally, the results indicated that AQ-anxiety scores were negatively correlated with microstate basic metrics as well as microstate energy. For classification, the performance of SVM reached a relatively high accuracy of 89 % for distinguishing no-FoH from m-FoH. In summary, the findings highlight the alterations of EEG microstates in people with fear of heights induced by virtual high-altitude, reflecting potentially underlying abnormalities in the allocation of neural assemblies. Therefore, the combination of EEG microstate analysis and VR may be a potential valuable approach for the diagnosis of fear of heights.

The electroencephalogram (EEG) signals were usually contaminated by electromyography (EMG) signals. By using the multivariate empirical mode decomposition (MEMD), we proposed the MEMD-based method to remove EMG artifacts from the EEG signals. Firstly, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs) with different frequency bands. Then the power spectra were calculated for every MIMF by using the Welch method. Because the power spectrum of EEG and EMG were focused on different frequency ranges, the MIMFs which included the EMG artifacts could be got rid of. Finally, the clean EEG could be reconstructed by the remaining MIMFs. In this study, the MEMD-based method was used to remove the EMG artifacts for different signal-to-noise ratio (SNR). The experimental results indicated that the SNR of EEG signals could be obviously improved in different conditions and the mean square error (MSE) of EEG signals also could be significantly reduced. In addition, by comparing with the existing artifact removal method > it was demonstrated that the proposed method improved the SNR and reduced the MSE both significantly better than the ICA -based method (p<;0.05).

Backgrounds: Functional near-infrared spectroscopy (fNIRS) is widely used for the evaluation of mental workload (MWL), but it is not yet clear whether it is affected by physical factors during cognitive tasks. Therefore, the combined effects of physical and cognitive loads on hemodynamic features in the prefrontal cortex were evaluated.Methods: Thirty-three eligible healthy male subjects were asked to perform three types of cognitive tasks (1-back, 2-back and 3-back). Concurrently, isotonic contraction aerobic exercise of the left upper limb was added. During this compound task, fNIRS signals, workload perception and task performance were recorded. Based on the oxyhemoglobin concentration, Pearson’s correlation coefficient (CORR), coherence value (COH) and the phase-locking value (PLV) were calculated to reflect FC among eight channels.Results: On the basis of effects of cognitive tasks, the concurrent physical activities would further increase National Aeronautics and Space Administration Task Load Index score (p < 0.05) and decrease task performance (p < 0.05). The fNIRS-based results showed that cognitive and physical loads had significant interaction effects on CORR (p < 0.05), COH (p < 0.05), and PLV (p = 0.010), while their main effects were not significant. The results of different channel pairs suggested that the functional connectivity between the right dorsolateral prefrontal cortex and the bilateral orbital frontal cortex was significantly enhanced under the combined effects of high physical and high cognitive load.Discussion: From the perspective of prefrontal functional connectivity, this study supports measurable effects of physical factor on operators’ mental load. The results provide a reference for the real-time (or online) assessment of the MWL level in the natural environment.