Deep learning networks are increasingly attracting attention in various fields, including electroencephalography (EEG) signal processing. These models provided comparable performance with that of traditional techniques. At present, however, lacks of well-structured and standardized datasets with specific benchmark limit the development of deep learning solutions for EEG denoising. Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing deep learning-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 clean EEG segments, 3400 ocular artifact segments and 5598 muscular artifact segments, allowing users to synthesize contaminated EEG segments with the ground-truth clean EEG. We used EEGdenoiseNet to evaluate denoising performance of four classical networks (a fully-connected network, a simple and a complex convolution network, and a recurrent neural network). Our analysis suggested that deep learning methods have great potential for EEG denoising even under high noise contamination. Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of deep learning-based EEG denoising.
Single-component monolayers of dendrimers and two-component monolayers consisting of dendrimers and n-alkanethiols immobilized on Au substrates are described. Single-component monolayers are prepared by exposing an Au substrate to ethanolic solutions of amine- or hydroxy-terminated polyamidoamine (PAMAM) dendrimers. The resulting monolayers are highly stable and nearly close-packed for dendrimer generations ranging from 4 to 8 (G4−G8). Electrochemical ac-impedance measurements indicate that the dendrimer surface is very porous toward the electroactive redox couple Fe(CN)63-/4-. Ferrocene-terminated dendrimer monolayers have also been investigated. Exposure of higher-generation dendrimer monolayers to ethanolic solutions of hexadecanethiol (C16SH) results in a dramatic compression of the dendrimers, and causes them to reorient on the surface from an oblate to prolate configuration. The dendrimers originally present on the surface do not desorb as a consequence of this configurational change. Comparison of the extent of adsorption of C16SH in different media (vapor-phase N2, hexane, and ethanol) shows that solvation of the dendrimers is the primary driving force for the structural change. Finally, the reactivity and stability of the dendrimer monolayers is investigated by on-surface functionalization of the dendrimer monolayer with 4-(trifluoromethyl)benzoyl chloride. The physical and chemical properties of the single- and two-component monolayers are evaluated by using reflection infrared spectroscopy, ellipsometry, contact-angle measurements, ac-impedance spectroscopy, cyclic voltammetry, and surface acoustic wave (SAW)-based analyte-dosing experiments.
Human society encompasses diverse social influences, and people experience events differently and may behave differently under such influence, including in forming an impression of others. However, little is known about the underlying neural relevance of individual differences in following others' opinions or social norms. In the present study, we designed a series of tasks centered on social influence to investigate the underlying relevance between an individual's degree of social conformity and their neural variability. We found that individual differences under the social influence are associated with the amount of inter-trial electroencephalogram (EEG) variability over multiple stages in a conformity task (making face judgments and receiving social influence). This association was robust in the alpha band over the frontal and occipital electrodes for negative social influence. We also found that inter-trial EEG variability is a very stable, participant-driven internal state measurement and could be interpreted as mindset instability. Overall, these findings support the hypothesis that higher inter-trial EEG variability may be related to higher mindset instability, which makes participants more vulnerable to exposed external social influence. The present study provides a novel approach that considers the stability of one's endogenous neural signal during tasks and links it to human social behaviors.
Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, β, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and β bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, β ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.
To investigate the prevalence of influenza virus infections in children in 2006 using the real-time PCR method.(1) Consulting the most conserved sequence NP gene of influenza virus, after comparing with the NP gene sequences of influenza virus in GenBank, one pair of specific primers and one TaqMan probe were designed for each subtype of influenza virus by the software Primer Express. The sensitivity of influenza was evaluated by testing known positive samples which had been two-fold diluted. The specificity of real-time PCR for influenza virus detection was assessed by cross testing 60 isolates of influenza A, 16 isolates of influenza B, and by testing a variety of other respiratory viruses positive samples; (2) 281 nasopharyngeal aspirate samples were detected by real-time PCR and virus isolation; (3) the 12 301 specimens from the patients of Guangzhou Children's Hospital were tested by using the real-time PCR method. Furthermore, the real-time PCR reagent was evaluated by comparing with the result of virus isolation.(1) The sensitivity of real-time PCR developed in this study for influenza A detection was 1:2(22) and for influenza B was 1:2(20) in two-fold serially diluted way. (2) No positive results were found in cross testing of other viruses positive specimens. (3) Influenza virus was detected from 1687 cases (13.71%) out of the 12 301 cases, including 773 cases (45.8%) positive for subtype A and 914 cases (54.2%) positive for subtype B; 455 out of 525 (86.7%) of influenza B positive specimens and 70 out of 525 (13.3%) of influenza A (H1N1) positive specimens were from patients seen during January to April; 419 out of 1118 (37.5%) specimens positive for influenza B and 699 out of 1118 (62.5%) specimens positive for influenza A (H1N1) were from patients seen from May to August. Influenza virus could be identified from 1380 samples by the methods of virus isolation, accounting for 81.80% of the 1687 positive samples detected by real-time PCR. All the influenza virus subtype A was H1N1.The real-time PCR method developed in this study was sensitive and specific for detecting influenza A and B in clinical specimens. During 2006, influenza A and influenza B co-circulated. The predominant virus was influenza B from January to April, peaking in April. Influenza A (H1N1) prevailed from May to August, with the peak in June.
The recorded electroencephalography (EEG) signals are usually contaminated by many artifacts. In recent years, deep learning models have been used for denoising of electroencephalography (EEG) data and provided comparable performance with that of traditional techniques. However, the performance of the existing networks in electromyograph (EMG) artifact removal was limited and suffered from the over-fitting problem. Here we introduce a novel convolutional neural network (CNN) with gradually ascending feature dimensions and downsampling in time series for removing muscle artifacts in EEG data. Compared with other types of convolutional networks, this model largely eliminates the over-fitting and significantly outperforms four benchmark networks in EEGdenoiseNet. Our study suggested that the deep network architecture might help avoid overfitting and better remove EMG artifacts in EEG.
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