Mutual Information Coefficient Research Articles

Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device

Background/Objectives: There have been attempts to detect depression using medical-grade electroencephalograph (EEG) data based on a machine learning approach. EEG has garnered interest as a method for assessing brainwaves by attaching electrodes to the scalp to obtain electrical activity in the brain. Recently, machine learning has been applied to the EEG data to detect depression, with encouraging results. Specifically, studies using medical-grade EEG data have shown that depression can be accurately detected. However, there is a need to expand the range of applications by achieving a score with machine learning using simpler consumer-grade brain wave sensors. At present, a sufficient score has not been achieved.; Methods: To improve the score of depression detection, we quantified various EEG indices to train models such as power spectrum, asymmetry, complexity, and functional connectivity. In addition, feature selection was performed to ensure that the model learns only promising EEG indices for depression detection. The feature selection methods were Light Gradient Boosting Machine (LightGBM) feature importance, mutual information, ReliefF and ElasticNet coefficients. The selected EEG indices were learned by the LightGBM model, which is reported to be as accurate as the latest deep learning models. In cross-validation, the independence of test and training data was ensured to avoid excessively calculated score; Results: The results showed that the Macro F1 score was 91.59%, suggesting that a consumer-grade EEG can detect depression. In addition, analysis of the EEG indices selected by feature selection indicated that the Macro F1 score was about 80% for single EEG indices such as differential entropy in the frequency band β and functional connectivity in the left frontal region in the frequency band 1–128 Hz; Conclusions: Although the data were obtained from a consumer-grade EEG, the results suggest that these EEG indices are promising for detection depression.

Robustness estimation for state-of-charge of a lithium-ion battery based on feature fusion

State of charge (SOC) of the lithium-ion battery stands as one of the crucial parameters of the battery management system. To enhance the robustness and accuracy of data-driven methods for estimating SOC, in this study, a novel framework based on feature selection and closed-loop estimation framework is proposed for estimating the SOC of a battery. Firstly, the maximum mutual information coefficient is used to correlate the data collected from the battery experiments. Secondly, an improved least squares support vector machine model (LSSVM) is proposed for SOC estimation using the sine cosine optimization algorithm for parameter optimization of LSSVM. Furthermore, based on the Sage-Husa adaptive Kalman filter, this study constructed a closed-loop estimation model. Finally, we conduct experiments on batteries with variable temperatures and working conditions, to verify its robustness and generalization. The results indicate that the proposed method has great SOC estimation accuracy compared to the control group. Even under simulated measurement noise conditions, the proposed method achieves the root mean square error 1.28 % and 1.12 % respectively, demonstrating strong robustness.

Texture-Based Classification of Fetal Growth Restriction From Intrauterine Neurosonographic Image.

Fetal growth restriction (FGR) is a condition where fetuses fail to reach their genetic potential for growth, posing a significant health challenge for newborns. The aim of this research was to explore the efficacy of texture-based analysis of neurosonographic images in identifying FGR in fetuses, which may provide a promising tool for early assessment of FGR. A retrospective analysis collected 100 intrauterine neurosonographic images from 50 FGR and 50 gestational age-appropriate fetuses. Using MaZda software, approximately 300 texture features were extracted from occipital white matter (OWM) and cerebellum of intrauterine neurosonographic images, respectively. Then 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients, and the mutual information coefficient method. Further, the 10 statistically most significant features were selected from these sets to form the mixed feature set. After nonlinear discriminant analysis was performed to reduce feature dimensionality, the artificial neural network (ANN) classifier was conducted, respectively. For OWM and cerebellum, a total of 11 and 14 statistically significant features were selected. When the mixed feature sets of OWM and cerebellum were applied to ANN classifier, classification accuracy were 90.00% (κ = 0.800; P < .001) and 93.00% (κ = 0.860; P < .001), and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.82 and 0.87. Texture analysis of fetal intrauterine neurosonographic images is a feasible and noninvasive strategy for evaluating FGR fetuses.

Photovoltaic Power Generation Related Physical Quantity Mining Method Based On Historical Time Series Data Analysis

Abstract The impact of distributed photovoltaic grid-connected on distribution network security, power quality and system stability cannot be ignored. In order to better cope with the uncertainty and output of new energy output and understand the characteristics of distributed new energy power output, it is necessary to predict photovoltaic power. The historical time series data of photovoltaic power is large in dimension and quantity. If the data output is not carried out, a large amount of redundant data will affect the accuracy of photovoltaic prediction. Therefore, this paper proposes a feature selection method based on MIC most mutual information coefficient, which filters out the most relevant data of photovoltaic power generation from the original feature variables, and then performs the feature dimension reduction method of linear discriminant analysis (LDA) to map the high-dimensional data to a lower dimension space. Finally, using the prediction simulation example, using the long and short time neural network (LSTM) prediction comparison, the feature dimension reduction method of MIC feature selection and LDA effectively improves the accuracy of photovoltaic power generation prediction.

Suppression of negative transfer in motor imagery brain-computer interface based on mutual information and Pearson correlation coefficient.

The focus of this paper is on the main challenges in brain-computer interface transfer learning: how to address data characteristic length and the source domain sample selection problems caused by individual differences. To overcome the negative migration that results from feature length, we propose a migration algorithm based on mutual information transfer (MIT), which selects effective features by calculating the entropy value of the probability distribution and conditional distribution, thereby reducing negative migration and improving learning efficiency. Source domain participants who differ too much from the target domain distribution can affect the overall classification performance. On the basis of MIT, we propose the Pearson correlation coefficient source domain automatic selection algorithm (PDAS algorithm). The PDAS algorithm can automatically select the appropriate source domain participants according to the target domain distribution, which reduces the negative migration of participant data among the source domain participants, improves experimental accuracy, and greatly reduces training time. The two proposed algorithms were tested offline and online on two public datasets, and the results were compared with those from existing advanced algorithms. The experimental results showed that the MIT algorithm and the MIT + PDAS algorithm had obvious advantages.

Extreme learning machine oriented surface roughness prediction at continuous cutting positions based on monitored acceleration

Surface roughness of mechanical parts plays an essential role in the practical application. Due to the complexity of the machining process, establishing a comprehensive theoretical model that can accurately simulate the real machined surface is challenging. Therefore, data-driven prediction methods are both practically useful and scientifically sound. This paper proposes an approach of using extreme learning machine (ELM) to predict surface roughness based on the monitored vibration signals, which can achieve the prediction of the surface roughness at any position of the machined surface, rather than using a single value to evaluate the processing quality under specific situations. To ensure the accuracy of the vibration intensity representation, the amplitude of monitored signals is compensated, accounting for varying positions and timings. The linear relationship between the spindle speed and acceleration is investigated to mitigate the impact of machining parameters on the monitored acceleration. Combined with grey relational analysis and mutual information coefficient, 18 features substantially linked to the surface roughness are selected from all extracted features and then used as the inputs for an ELM-based predictive decision-making system. The results indicate that the average and minimum errors of the developed method are 5.09% and 1.03%, respectively, demonstrating its accuracy and feasibility. In contrast to other machine learning models of SVR and ANN, the proposed method in this paper exhibits optimal predictive precision and efficiency, suggesting its potential application in the fast response of quality control.

A Novel Spatiotemporal Periodic Polynomial Model for Predicting Road Traffic Speed

Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spatial feature extraction methods, we introduce a maximum mutual information coefficient spatial feature extraction method. In terms of periodic feature extraction methods, we introduce a periodic trend modeling method into the prediction of speed time series, and effective fusion is carried out. Four strategies are evaluated based on the Guangzhou road speed dataset: a univariate polynomial model, a spatiotemporal polynomial model, a periodic polynomial model, and a spatiotemporal periodic polynomial model. The test results show that the three methods proposed in this article can effectively improve prediction accuracy. Comparing the spatiotemporal periodic polynomial model with multiple machine learning models and deep learning models, the prediction accuracy is improved by 5.94% compared to the best feedforward neural network. The research in this article can effectively deal with the temporal, spatial, periodic, and nonlinear characteristics of speed prediction, and to a certain extent, improve the accuracy of speed prediction.

Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention

Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large public buildings often pose challenges in improving prediction accuracy. In this study, we propose a combined prediction model that combines signal decomposition, feature screening, and deep learning. First, we employ the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose energy consumption data. Next, we propose the Maximum Mutual Information Coefficient (MIC)-Fast Correlation Based Filter (FCBF) combined feature screening method for feature selection on the decomposed components. Finally, the selected input features and corresponding components are fed into the Bi-directional Long Short-Term Memory Attention Mechanism (BiLSTMAM) model for prediction, and the aggregated results yield the energy consumption forecast. The proposed approach is validated using energy consumption data from a large public building in Shaanxi Province, China. Compared with the other five comparison methods, the RMSE reduction of the CEEMDAN-MIC-FCBF-BiLSTMAM model proposed in this study ranged from 57.23% to 82.49%. Experimental results demonstrate that the combination of CEEMDAN, MIC-FCBF, and BiLSTMAM modeling markedly improves the accuracy of energy consumption predictions in buildings, offering a potent method for optimizing energy management and promoting sustainability in large-scale facilities.

A Feature Selection Method Based on Graph Theory for Cancer Classification.

Gene expression profile data is a good data source for people to study tumors, but gene expression data has the characteristics of high dimension and redundancy. Therefore, gene selection is a very important step in microarray data classification. In this paper, a feature selection method based on the maximum mutual information coefficient and graph theory is proposed. Each feature of gene expression data is treated as a vertex of the graph, and the maximum mutual information coefficient between genes is used to measure the relationship between the vertices to construct an undirected graph, and then the core and coritivity theory is used to determine the feature subset of gene data. In this work, we used three different classification models and three different evaluation metrics such as accuracy, F1-Score, and AUC to evaluate the classification performance to avoid reliance on any one classifier or evaluation metric. The experimental results on six different types of genetic data show that our proposed algorithm has high accuracy and robustness compared to other advanced feature selection methods. In this method, the importance and correlation of features are considered at the same time, and the problem of gene selection in microarray data classification is solved.

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval’s theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination (R2) higher than the other models, and it has the smallest mean absolute percentage error (MAPE) and root mean square error (RMSE).

Integrating EEG and Machine Learning to Analyze Brain Changes during the Rehabilitation of Broca's Aphasia.

The fusion of electroencephalography (EEG) with machine learning is transforming rehabilitation. Our study introduces a neural network model proficient in distinguishing pre- and post-rehabilitation states in patients with Broca's aphasia, based on brain connectivity metrics derived from EEG recordings during verbal and spatial working memory tasks. The Granger causality (GC), phase-locking value (PLV), weighted phase-lag index (wPLI), mutual information (MI), and complex Pearson correlation coefficient (CPCC) across the delta, theta, and low- and high-gamma bands were used (excluding GC, which spanned the entire frequency spectrum). Across eight participants, employing leave-one-out validation for each, we evaluated the intersubject prediction accuracy across all connectivity methods and frequency bands. GC, MI theta, and PLV low-gamma emerged as the top performers, achieving 89.4%, 85.8%, and 82.7% accuracy in classifying verbal working memory task data. Intriguingly, measures designed to eliminate volume conduction exhibited the poorest performance in predicting rehabilitation-induced brain changes. This observation, coupled with variations in model performance across frequency bands, implies that different connectivity measures capture distinct brain processes involved in rehabilitation. The results of this paper contribute to current knowledge by presenting a clear strategy of utilizing limited data to achieve valid and meaningful results of machine learning on post-stroke rehabilitation EEG data, and they show that the differences in classification accuracy likely reflect distinct brain processes underlying rehabilitation after stroke.

A novel software defect prediction approach via weighted classification based on association rule mining

Software defect prediction technology is used to assist software practitioners in effectively allocating test resources and identifying hidden defects in a timely manner. However, the prediction of defect-prone software using association rule mining algorithms is limited because of the unbalanced distribution of defect data. Furthermore, although the existing weighted association rule mining approach considers item strength, the weight calculation still relies on expert experience and lacks fine granularity. We propose a novel software defect prediction approach based on mutual information and correlation coefficient weighted class association rule mining (MCWCAR). The MCWCAR model employs a cost-sensitive strategy and generates frequent itemsets according to three mining objectives while maintaining the original item distribution: defective class rules, non-defective class rules, and feature association relationships. During the weighted frequent itemset mining process, it combines feature selection and itemset screening to determine the appropriate feature combination through mutual information weighted support. Meanwhile, the correlation coefficient is applied to accurately depict the correlation between feature items and defect classes, serving as the weight to mine class association rules. Additionally, to ensure that interestingness measures have asymmetry and effectively represent negative associations under the condition of class imbalance, the addedvalue is adopted in the filtering association rules. We conducted experiments on 27 open-source datasets and evaluated the performance differences between MCWCAR and state-of-the-art baseline classifiers. Experimental results demonstrate that the proposed algorithm significantly outperforms other baselines in terms of Balance, Gmean, MCC, and F-measure.

AUTOMATIC ELECTROENCEPHALOGRAPHIC SOURCE SEPARATION STRATEGIES FOR SEIZURE PREDICTION APPLICATION

Electroencephalography (EEG) is a common clinical method of recording the electrical activity of the brain. EEG can record High-Frequency Oscillations (&gt;80 HZ), which carry appropriate information regarding Epilepsy. High-Frequency Oscillations (HFO) serve as a potential biomarker for Epileptogenesis. EEG signals are often prone to artifact corruptions, which mislead the clinicians by the incorrect signal interpretations. Therefore, automatic artifact removal approach is a key phase in all the Brain-Computer Interface (BCI) applications. In this work, the automatic artifact identification and removal strategy without consuming any supplementary reference channel using two different approaches is developed and discussed. A proficient novel Modified Online Bi-Conjugate Gradient-based Independent Component Analysis (MOBICA) is developed. An efficient threshold-based peak detection and removal strategy, Sparsity-based Artifact Removal Technique (SART) is constructed, where Principle Component Analysis (PCA) is replaced with Singular Value Decomposition (SVD) of the K-SVD algorithm. Both the proposed models are evaluated on two different datasets like CHB-MIT and SRM scalp data recordings. Both the MOBICA and SART algorithms removed the artifactual component parting the intact EEG source component. Finally, the performance of the proposed agenda is compared with the conventional approaches. Our MOBICA and SART algorithms remove the artifactual component parting the intact EEG source component. Empirical results of SART on CHB-MIT and SRM databases of 52 EEG recordings outperform MOBICA maintaining least Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and high Signal to Artifact Ratio (SAR), Mutual Information (MI), and Correlation Coefficient (CC). The proposed strategy vows to be a promising solution for artifact removal in the clinical use of EEG signals and in BCI applications.

Rotary Kiln Temperature Control Under Multiple Operating Conditions: an Error-Triggered Adaptive Model Predictive Control Solution

The temperature of rotary kiln, as one of the essential equipment in zinc smelting process, determines the product quality and resource utilization. However, since rotary kiln is a large-scale and highly coupled system, there are plenty of variables affecting the rotary kiln temperature, bringing too much redundant information to describe rotary kiln dynamics. In addition, due to the variations in raw materials, production load, and market demand, there exist various operation conditions, making it difficult to achieve stability control of the rotary kiln temperature with traditional control methods. To solve these problems, an error-triggered adaptive model predictive control (ET-AMPC) is proposed in this article. Specifically, since rotary kiln temperature is hard to regulate due to redundancy among variables and strong nonlinearity, an orthogonal maximum mutual information coefficient feature selection (OMICFS) method is first proposed to determine vital variables affecting temperature most. Then, aiming at the problem of changing operating conditions of rotary kilns, an ET-AMPC method is proposed, which can precisely adapt to different operating conditions and achieve stability control. Finally, experiments on a numerical simulation case and an industrial rotary kiln show the strength and reliability of the proposed method, which reduces 10%–20% trajectory tracking error in the period of operating condition changing and improves the control accuracy effectively.

Multi-scale analysis of the co-movement between China's new energy vehicle industry and Tesla: Evidence from capital market

China is currently the world's largest new energy vehicle market, and the development of its new energy vehicles is crucial to the sustainable development of mankind. As a leader in the new energy vehicle industry, Tesla's entry into the Chinese market has an important impact on its new energy vehicle industry, and the study of the relationship between the two is of great significance in promoting the development of China's new energy vehicle industry. Therefore, we analyzed the complex relationship between Tesla and China's new energy vehicle industry from 2013 to 2022 based on the stock market perspective using modal decomposition, Maximum mutual information coefficient, and transfer entropy. Mutual information coefficient results show that Tesla has stronger co-movements with China's New Energy Vehicle Manufacturing sector, and its strength is highest in the medium- and long-term time scales, up to 0.196 and 0.529, respectively. whereas the transfer entropy results show that Tesla has a stronger information transfer effect on the Vehicle Manufacturing sector and Charging Pile sector than on the New Energy Vehicles Battery sector and New Energy Vehicles Parts sector. However, in general, Tesla's information overflow to the whole Chinese new energy vehicle industry is on the rise. The Chinese government can appropriately give Tesla certain favorable policies and encourage Chinese enterprises to cooperate with it, giving full play to Tesla's catfish effect and technology demonstration effect, and then promoting the further development of China's new energy vehicle industry.

Control of Correlation Using Confinement in Case of Quantum System

AbstractThis article investigates the behavior of a Moshinsky atom in a 1D harmonic trap. Focus is given on the theoretical foundations of confinement and its impact on the correlation between particles in the Moshinsky atom. The investigation begins by illustrating the (de)localization of the probability density function using Shannon entropy. The basics of correlation and interpretation of correlation using tools such as mutual information and statistical correlation coefficients and how these can be quantified are discussed. Then the concept of confinement is explored. The impact of interaction strength and confinement on Shannon entropy, statistical correlation coefficients, and mutual information is investigated. How interaction strength and confinement can be used to induce correlations between previously uncorrelated particles, as well as how they can be used to suppress correlations between previously correlated particles is discussed. Their implications for quantum information processing and quantum simulation are discussed. In conclusion, confinement is a powerful tool for controlling correlations in quantum systems, and its impact on correlation can be understood through theoretical models. The importance of experimental studies in this field, which provide insights into the behavior of quantum systems under confinement and pave the way for future applications in quantum technology is also emphasized.

Semi-Supervised Deep Learning-Based Multi-component Spectral Calibration Modeling for UV-vis and Near-Infrared Spectroscopy without Information Loss.

Spectral analysis is an important method for characterizing and identifying chemical species. However, quantitative spectral analysis of multiple chemical properties in the real world has always been a challenging problem due to the strong correlation, massive noise, and serious information overlapping of the spectral features. Here, we present a new semi-supervised spectral calibration method based on information lossless decoupling of spectral features named NICEM. To realize the separation and extraction of key latent features, the method uses the flow-based model non-linear independent component estimation (NICE) to learn the sample distribution. The spectral data information is transformed into independent latent variables obeying Gaussian distribution by the reversible structure of deep network without information loss, so as to find the essential properties and realize the feature nonlinear decomposition. Moreover, the association between the input latent feature variables and attributes is evaluated by the maximum mutual information coefficient to eliminate the adverse effects of irrelevant information in the latent variable space and mine key information. Since the latent variables are independent in each dimension, the NICEM method is easier to establish an accurate semi-supervised multi-component calibration model even for high overlapping and complex spectral data. The applicability of the proposed spectral modeling method is demonstrated by using three ultraviolet-visible and near-infrared spectral data sets with 15 physical and chemical properties including diesel fuels, corn, and multi-metal ions solution. Results show that the proposed NICEM method has the highest determination coefficient (R2) and significantly improves extrapolation compared with the seven state-of-the-art methods. The proposed method is intuitive because it obviates complex feature engineering and prior knowledge and is a promising spectral calibration tool for quantitative analysis in other spectroscopy applications.

Estimation of prediction intervals for uncertainty assessment of artificial neural network based wastewater treatment plant effluent modeling

Uncertainty qualification of estimated biological variables of Tabriz wastewater treatment plant (WWTP) in Iran, was conducted by artificial intelligence-based modeling. The pollution variables at entrance of sedimentation section and its output were considered as inputs and outputs of the modeling. Non-linear mutual information (MI) and linear correlation coefficient (CC) were used to select inputs. Modeling was conducted via two feed-forward neural networks (FFNN) models for point prediction (PP) and lower and upper bound estimation (LUBE) to provide prediction intervals (PIs) of the computed variables as representative of the modeling uncertainty. In the proposed LUBE, an FFNN was trained with two outputs as upper and lower limits of the predictions. The results showed that selecting the inputs through MI led to narrower PI bound and less uncertainty. Computed determination coefficient (DC)train and DCTest for modeling effluent biological oxygen demand (BODeff) were respectively as 0.9 and 0.7, whereas the values for modeling effluent chemical oxygen demand (CODeff) were 0.8 and 0.7. Comparison of non-linear machine learning (ML) models of FFNN, support vector machine (SVR), adaptive neuro-fuzzy inference system (ANFIS), and linear autoregressive integrated moving average (ARIMA) model demonstrated the superiority of FFNN method for PP task over others. In the LUBE method, the best relationship between pollution parameters was obtained via the MI method. PICP and CWC values were obtained for BOD as 94.08 % and 0.152 and for COD as 86.41 % and 0.1769, respectively. The innovation of this research is to examine the uncertainties in neural network-based analysis of WWTP modeling.

Study on the influence of high and low temperature environment on the energy consumption of battery electric vehicles

In order to study the influence of high and low temperature environments on the energy consumption of battery electric vehicles, this paper takes Tianjin as an example to collect the actual road data of 8 battery electric passenger cars for eight months. It determines the correlation between each characteristic parameter and energy consumption based on the maximum mutual information coefficient of short stroke segments, of which the average driving speed correlation is the highest, which is 0.785. Given the two factors that have a greater impact on energy consumption in high and low temperature environments: driving resistance and air conditioning on motion and idle energy consumption analysis, the study shows that in terms of driving resistance, high temperature and low temperature energy consumption are reduced and increased by 5.4% and 13.3% respectively relative to normal temperature; The energy consumption of high temperature and low temperature air conditioner switching on increased by 17.12% and 47.48% respectively compared with the air conditioner off.

Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep

The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.