Lempel-Ziv complexity changes and physiological mental fatigue level during different mental fatigue state with spontaneous EEG

The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.

Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis

We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.

Radar Emitter Signal Recognition Based on Complexity Features

Intra-pulse characteristics of different radar emitter signals reflect on signal waveform by way of changing frequency, phase and amplitude. A novel approach was proposed to extract complexity features of radar emitter signals in a wide range of signal-to-noise ratio (SNR), and radial basis probability neural network (RBPNN) was used to recognize different radar emitter signals. Complexity features, including Lempel-Ziv complexity (LZC) and correlation dimension (CD), can measure the complexity and irregularity of signals, which mirrors the intra-pulse modulation laws of radar emitter signals. In an experiment, LZC and CD features of 10 typical radar emitter signals were extracted and RBPNN was applied to identify the 10 radar emitter signals. Simulation results show that the proposed approach is effective and has good application values because average accurate recognition rate is high when SNR varies in a wide range.

Spatio-Temporal Kinematic Decomposition of Movements

We propose the new experimental method for investigating and approximating the organization and structure of movements with given accuracy. The composition of approximating trajectories illuminating the movement traits discloses the level of movement expertise in dancers and golf players. The method allows estimating the level of movement expertise, drawing the detailed structure of movements, and classifying movements into a given repertoire automatically.

Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.

Pulsating Solitons in the Two-Dimensional Complex Swift-Hohenberg Equation

In this paper, we performed an investigation of the dissipative solitons of the two-dimensional (2D) Complex Swift-Hohenberg equation (CSHE). Stationary to pulsating soliton bifurcation analysis of the 2D CSHE is displayed. The approach is based on the semi-analytical method of collective coordinate approach. This method is constructed on a reduction from an infinite-dimensional dynamical dissipative system to a finite-dimensional model. The reduced model helps to obtain approximately the boundaries between the stationary and pulsating solutions. We analyzed the dynamics and characteristics of the pulsating solitons. Then we obtained the bifurcation diagram for a definite range of the saturation of the Kerr nonlinearity values. This diagram reveals the effect of the saturation of the Kerr nonlinearity on the period pulsations. The results show that when the parameter of the saturation of the Kerr nonlinearity increases, one period pulsating soliton solution bifurcates to double period pulsations.

Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion

A new fault diagnosis technique for rolling element bearing using multi-scale Lempel-Ziv complexity(LZC) and Mahalanobis distance(MD) criterion is proposed in this study. A multi-scale coarse-graining process is used to extract fault features for various bearing fault conditions to overcome the limitation of the single stage coarse-graining process in the LZC algorithm. This is followed by the application of MD criterion to calculate the accuracy rate of LZC at different scales, and the best scale corresponding to the maximum accuracy rate is identified for fault pattern recognition. A comparison analysis with Euclidean distance(ED) criterion is also presented to verify the superiority of the proposed method. The result confirms that the fault diagnosis technique using a multi-scale LZC and MD criterion is more effective in distinguishing various fault conditions of rolling element bearings.

Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment.In this work,we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum（MPS） through a multi-scale morphology analysis procedure.The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves.Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.

Efficient Approach for 3D Stationary Optical Solitons in Dissipative Systems

We feature the stationary solutions of the 3D complex cubic-quintic Ginzburg-Landau equation (CGLE). Our approach is based on collective variables approach which helps to obtain a system of variational equations, giving the evolution of the light pulses parameters as a function of the propagation distance. The collective variables approach permits us to obtain, efficiently, a global mapping of the 3D stationary dissipative solitons. In addition it allows describing the influence of the parameters of the equation on the various physical parameters of the pulse and their dynamics. Thus it helps to show the impact of dispersion and nonlinear gain on the stationary dynamic.

Development and stability analysis of a high-speed train bearing system under variable speed conditions

During a high-speed train operation,the train speed changes frequently,resulting in motion change as a function of time.A dynamic model of a double‐row tapered roller bearing system of a high-speed train under variable speed conditions is developed.The model takes into consideration the structural characteristics of one outer ring and two inner rings of the train bearing.The angle iteration method is used to determine the rotation angle of the roller within any time period,solving the difficult problem of determining the location of the roller.The outer ring and inner ring faults are captured by the model,and the model response is obtained under variable speed conditions.Experiments are carried out under two fault conditions to validate the model results.The simulation results are found to be in good agreement with the results of the formula,and the errors between the simulation results and the experimental results when the bearing has outer and inner ring faults are found to be,respectively,5.97% and 2.59%,which demonstrates the effectiveness of the model.The influence of outer ring and inner ring faults on system stability is analyzed quantitatively using the Lempel–Ziv complexity.The results show that for low train acceleration,the inner ring fault has a more significant effect on the system stability,while for high acceleration,the outer ring fault has a more significant effect.However,when the train acceleration changes,the outer ring has a greater influence.In practice,train acceleration is usually small and does not frequently change in one operation cycle.Therefore,the inner ring fault of the bearing deserves more attention.