Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI1−30. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions

Age-Related Alterations in Electroencephalography Connectivity and Network Topology During n-Back Working Memory Task

The study of the healthy brain in elders, especially age-associated alterations in cognition, is important to understand the deficits created by Alzheimer's disease (AD), which imposes a tremendous burden on individuals, families, and society. Although, the changes in synaptic connectivity and reorganization of brain networks that accompany aging are gradually becoming understood, little is known about how normal aging affects brain inter-regional synchronization and functional networks when items are held in working memory (WM). According to the classic Sternberg WM paradigm, we recorded multichannel electroencephalography (EEG) from healthy adults (young and senior) in three different conditions, i.e., the resting state, 0-back (control) task, and 2-back task. The phase lag index (PLI) between EEG channels was computed and then weighted and undirected network was constructed based on the PLI matrix. The effects of aging on network topology were examined using a brain connectivity toolbox. The results showed that age-related alteration was more prominent when the 2-back task was engaged, especially in the theta band. For the younger adults, the WM task evoked a significant increase in the clustering coefficient of the beta-band functional connectivity network, which was absent in the older adults. Furthermore, significant correlations were observed between the behavioral performance of WM and EEG metrics in the theta and gamma bands, suggesting the potential use of those measures as biomarkers for the evaluation of cognitive training, for instance. Taken together, our findings shed further light on the underlying mechanism of WM in physiological aging and suggest that different EEG frequencies appear to have distinct functional correlates in cognitive aging. Analysis of inter-regional synchronization and topological characteristics based on graph theory is thus an appropriate way to explore natural age-related changes in the human brain

A regression method for EEG-based cross-dataset fatigue detection

Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of electroencephalogram (EEG) data for training, which is resource-consuming and impractical. Although the cross-dataset fatigue detection model does not need to be retrained, no one has studied this problem previously. Therefore, this study will focus on the design of the cross-dataset fatigue detection model.Methods: This study proposes a regression method for EEG-based cross-dataset fatigue detection. This method is similar to self-supervised learning and can be divided into two steps: pre-training and the domain-specific adaptive step. To extract specific features for different datasets, a pretext task is proposed to distinguish data on different datasets in the pre-training step. Then, in the domain-specific adaptation stage, these specific features are projected into a shared subspace. Moreover, the maximum mean discrepancy (MMD) is exploited to continuously narrow the differences in the subspace so that an inherent connection can be built between datasets. In addition, the attention mechanism is introduced to extract continuous information on spatial features, and the gated recurrent unit (GRU) is used to capture time series information.Results: The accuracy and root mean square error (RMSE) achieved by the proposed method are 59.10% and 0.27, respectively, which significantly outperforms state-of-the-art domain adaptation methods.Discussion: In addition, this study discusses the effect of labeled samples. When the number of labeled samples is 10% of the total number, the accuracy of the proposed model can reach 66.21%. This study fills a vacancy in the field of fatigue detection. In addition, the EEG-based cross-dataset fatigue detection method can be used for reference by other EEG-based deep learning research practices

A Rapid Synchronous Determination Method for Soil Inorganic Carbon Content and its Carbon Isotope Ratio

The accumulation and leaching of soil inorganic carbon (SIC) play crucial roles in the global carbon balance and represent a key research focus in carbon cycling studies. Accurate quantification of SIC content and its stable isotope ratio is critical for identifying the current "missing" carbon sink in terrestrial ecosystems. This study developed a rapid，high-throughput method for synchronous measurement of soil inorganic carbon (IC) content and its carbon isotope ratios using cavity ring-down spectroscopy(CRDS) combined with an automated small-volume gas sampler. A synchronous analysis method for inorganic carbon content and isotope ratios in different types of soils was established by analyzing certified reference materials. Results demonstrated that this method has a measurement range of 0.050−0.500 mg (as carbonate)，with a correlation coefficient ≥0.999. The accuracy of SIC analysis was better than 1 g/kg，and the accuracy of carbon isotope analysis was better than 0.5 ‰，with no observed isotope fractionation. The newly developed method was applied to determine inorganic carbon content and isotope ratios in soils with different types and SIC contents. The results showed that all samples achieved good repeatability，and the results were consistent with those measured using the original method. Moreover，the accuracy of SIC content and isotope ratios in soils of 100 mesh is better than that in soils of 60 mesh. The optimized method is simple to operate，offers a low detection limit，requires minimal processing time，and exhibits excellent repeatability，making it highly suitable for rapid and batch analysis of SIC content and its stable carbon isotope ratio

Laboratory Model Test and Field In Situ Test of Distributed Optical Fiber Monitoring of Seepage in a Karst Depression Reservoir Basin

Karst depressions are ideal places for building reservoirs and stacking wastes, but karst depressions are mostly located in areas with strong karst development, and there is a problem of karst leakage. The study of karst depression monitoring methods can provide technical support for the construction of reservoirs in karst depressions, and leakage monitoring technology based on distributed temperature sensing (DTS) has the clear advantages of a large measurement range, high precision, continuity, wide distribution, large area, etc. In this paper, a temperature-sensing optical cable is first used to conduct leakage monitoring tests in different soil media, and the temperature change curve in different media is obtained, which verifies the feasibility of the heatable temperature-sensing optical cable to identify leakage in soil media with different moisture contents. Then, for the heatable distributed temperature-sensing optical cable, the variation law of the temperature eigenvalues of the sensing optical cable under different seepage velocities is studied using the layout method wrapped with saturated medium-coarse sand, and the relationship between the seepage rate and the temperature eigenvalues was analyzed. A regression formula for quantitative analysis of leakage velocity was established; finally, the manganese slag silo project in Songtao County in Guizhou Province, China, was used to simulate different leakage conditions on site for testing, and the data were compared and analyzed to obtain the route under different conditions. The temperature distribution law of the seepage section verifies the feasibility of the application of heatable temperature-sensing optical cables in the seepage monitoring of karst depressions