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This study aims to develop a BCI system using Electroencephalogram (EEG) for lie detection. EEG signal is obtained in a non-invasive manner with eight electrodes mounted at designated positions on the scalp employing a hand-made design EEG headset. The selected input for classification in this research comprises 11 features extracted across all three domains: time, frequency, and time-frequency. In which nine time-domain features include: Mean, Variance, Standard Deviation, Skewness, Kurtosis, Permutation Entropy, SVD Entropy, Approximate Entropy, and Sample Entropy. Over the frequency domain and time-frequency domain, we have the Spectral Entropy and an 8-dimensional image computed by Continuous Wavelet Transform. Modern researches indicate that Neural Networks have an extraordinary capacity to classify patterns with diverse inputs such as data sequences and pictures. Four variations are applied in this study, including the Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN). In which MLP, LSTM, GRU are worked on the first ten time and frequency features, while the 8-dimensional image composed after the Continuous Wavelet Transform is adopted as the input of the CNN network. The evaluation results demonstrated the feasibility of our EEG-Based BCI system in Deception recognition.

This paper proposed and developed fault detection and diagnosis solution based on the ensemble empirical mode decomposition and support vector machine (SVM). The collected field signals are used as the input signals that are decomposed into a series of intrinsic mode functions (IMF) by using the empirical mode decomposition. The dominant eigenmode functions are then selected in the proposed solution and their approximate entropy and energy ratio are calculated as signal features. Finally, the feature vector of the signals is used as the input into the support vector machine for model training to classify different mechanical faults of the wind turbine gearbox. The developed algorithmic solution is extensively verified through a set of experiments and the numerical results confirmed the effectiveness of the proposed solution.

The main aim of this paper is to analyse the effects of health and age to human stability. For this purpose, the dataset with a recording of force platforms from subjects undergoing stabilography tests is utilized. These tests are consist of steady standing for 60 s. These tests were done upon several conditions and each test was repeated several times. From these recording, the approximate entropy was computed and the results were analysed using tools of mathematical statistics.

The main aim of this paper is the study of dynamical properties of the Laplacian-type coupled map lattice induced by the Tent family on a periodic lattice depending on two parameters: the state parameter of the Tent map and the coupling constant. For this purpose, tools like approximate entropy, maximal Lyapunov exponent, and the 0–1 test for chaos are introduced and applied to numerical simulations performed using a supercomputer.

In this paper, the dynamical behavior of the Gotthans-Petržela third-order autonomous model is researched. For this purpose, the 0-1 test for chaos and fast approximate entropy is newly applied. Using these tools, the dynamic is quantified and qualified. Depending on the system’s parameters, it is shown that irregular (chaotic) and regular (periodic) movement character appears.

The field of structural health monitoring (SHM) applies damage detection techniques to provide timely in-situ system condition assessment. Previously, researchers have suggested a fundamental axiom for SHM that states, “damage will increase the complexity of a system.” One way this increased complexity can manifest itself is in the increased complexity of sensor data recorded from the structure when damage occurs. The question then becomes how to best quantify the increase in complexity of those data. Information complexity is one such approach and within this framework various information entropy quantities have been proposed as measures of complexity. The literature has shown that there are multiple information entropy measures, including; Shannon Entropy, Renyi Entropy, Permutation Entropy, Sample Entropy, Approximate Entropy, and Spectral Entropy. With multiple measures proposed to quantify information entropy; a study to compare the relative effectiveness of these entropy measures in the context of SHM is needed. Therefore, the objective of this paper is to compare the effectiveness of entropy-based methods in distinguishing between “Healthy” and “Unhealthy” labeled datasets. The labeled datasets considered in this study were obtained from a 4DOF impact oscillator, a rotating machine with a damaged bearing, and an impact oscillator excited by a rotating machine. Furthermore, two methods were used in this study to classify the results from the different entropy measures; Naive-Bayes classification, and K-means clustering. Effectiveness of a given entropy measure is determined by the number of misclassifications produced when compared to the true labels. The analysis showed that entropy measures obtained from data corresponding to sensors closer to the damage source had fewer misclassifications for the datasets tested. For the datasets considered in this study, the researchers concluded that each dataset had a different most effective entropy measure. The study would need to be expanded to include other classification methods and other datasets to define more precisely which entropy measure is the most effective in identifying the increase in complexity associated with damage and, hence, distinguishing between healthy and damaged data.

In order to improve the emotion recognition rate, this paper proposes an electroencephalograph (EEG) emotion recognition model using a library for support vector machine (LIBSVM) as the classifier. In this paper, we collected EEG signals from 10 volunteers along with the participants’ self-assessment of their affective state after each stimulus, in terms of Valence and arousal. After these signals are filtered, we calculate the features of Lempel-Ziv complexity, wavelet detail coefficient, and the co-integration relationship degree first. At the same time, EMD is carried out to calculate the average approximate entropy of the first four Intrinsic Mode Functions (IMFs). At last, all the features extracted will input into the LIBSVM for training and testing, and complete emotion recognition. In this paper, two classifications are carried out on the two dimensions of Valence and Arousal, respectively. The experimental results show that the average emotional recognition rate is 83.64% and 75.11%, respectively, which proves that the proposed scheme has a certain feasibility.

A fractional Fourier transform-based strategy is proposed for the analysis of morphological irregularities of 933 simulated electrograms recorded from a 3D simulated human atria under atrial fibrillation. Optimum fractional domains are considered to quantify the non-stationary features in the signals after preprocessing and segmentation. Optimum order maps obtained with the proposed strategy are compared with approximate entropy maps generated in previous studies. The results indicate that distinct types of electrical activity can be associated with different fractional domains and that the FrFT can be sensitive to different underlying propagation patterns where the approximate entropy exhibits uniform behavior.

Human brain functions are underlined by spatially and temporally coherent activity. Characterizing the spatio-temporal coherence (STC) of brain activity is then important to understand brain function, which however is still elusive in the literature. In this study, we proposed a new method to measure the spatio-temporal incoherence (STIC) by segmenting the fMRI time series of neighboring voxels into a series of continuous 4-dimensional elements and recording the similarity of every element to all others. STIC was then calculated as the log differential of the similarity sum of all elements and that when the time window is increased by 1 – a process similar to and directly extended from the time-embedding based approximate entropy calculation. Experiment results showed that STIC revealed the correct irregularity difference between random noise and spatio-temporally coherent signal. STIC was less sensitive to noise than a multi-variate entropy measure. When applied to 917 young health subjects’ resting-state fMRI, we identified highly replicable STIC maps with very fine cortical structures. Females and more matured brain (older here) had higher STIC. Higher STIC in putamen showed a trend of correlations with better mental examination outcome. These data showed STIC as a potential functional brain marker.

Nonlinear techniques are extremely useful to analyze the human movement within sport context. One of the most popular metric of process complexity is entropy. This work concerns application of quaternion approximation entropy for analysis of human motion during downhill skiing. Skier motion is represented as time series of orientations of two skies and chest. Orientations are coded as unit quaternions. Such approach was used to quantify chaotic behaviour in context of regularity in time series. The preliminary study discussed the results obtained from IoT system consists of three inertial measurement sensors (IMU) and smartphone.

Human concentration state detection using the eye movement information is now a popular research topic in computer vision, especially the detection of driver fatigue and advertising analysis. In this paper we analyze eye movement styles on a person’s concentration state through watching different video clips. We propose a novel method including the fusion features of eye event data and raw eye movement to detect attention. Firstly, we use the logistic regression algorithm to conduct the new feature by eye movement event data, and use wavelet and approximate entropy algorithm to conduct the new feature by raw eye movement data. Secondly, we train attention detection model using these new merged features. In order to avoid the problem caused by insufficient samples, crossing method is used to train the model to ensure its accuracy. Our model achieves a satisfying 95.25% accuracy.

Since biomedical signals are high dimensional data sets with a lot of noise signal, the results processed by the classical signal processing method are subjected to the impact of the noise and interference. Entropy as a measure of disorder or uncertainty in the data has been applied in signal processing research areas. This review is to introduce the application of entropy in the analysis of biomedical signals and discuss the advantages and shortcomings of various entropies. Especially, the utilization and application of entropy concept in cancer research are highlighted.

Predicting future states of harvested marine populations requires an understanding of how intrinsic time delay processes and volatile phenomena (e.g., impulsive mortality) act in concert to alter the dynamic population trajectory. Separately, the effects (on population dynamics) of time-delays, and impulses, have been studied theoretically by several authors. This paper shows an example approach, where the effects of impulses and time-delays are integrated in the same modeling framework, to understand how they may alter the predictability of future population states. The paper uses a stage-structured, Impulsive-Delay-Differential-Equation (IDDE) model to describe a single species marine population system. It presents results based on numerical experiments.

Presence of partial discharge pattern implies the fault behavior on insulation system of medium voltage overhead lines, especially with covered conductors (CC). This paper covers the examination of Approximation and Sample entropy as a signal complexity measures on partial discharge patterns of several kinds of faults. These features are calculated on multiple different adjustments such as the different applied denoising schemes and varying embedding dimensions. The final results reveal the splitting ability of these complexity measures on applied data.

In prior work, we have shown how to compute global network entropy using a heat bath analogy and Maxwell-Boltzmann statistics. In this work, we show how to project out edge-entropy components so that the detailed distribution of entropy across the edges of a network can be computed. This is particularly useful if the analysis of non-homogeneous networks with a strong community as hub structure is being attempted. To commence, we view the normalized Laplacian matrix as the network Hamiltonian operator which specifies a set of energy states with the Laplacian eigenvalues. The network is assumed to be in thermodynamic equilibrium with a heat bath. According to this heat bath analogy, particles can populate the energy levels according to the classical Maxwell-Boltzmann distribution, and this distribution together with the energy states determines thermodynamic variables of the network such as entropy and average energy. We show how the entropy can be decomposed into components arising from individual edges using the eigenvectors of the normalized Laplacian. Compared to previous work based on the von Neumann entropy, this thermodynamic analysis is more effective in characterizing changes of network structure since it better represents the edge entropy variance associated with edges connecting nodes of large degree. Numerical experiments on real-world datasets are presented to evaluate the qualitative and quantitative differences in performance.

This paper presents the study we have done to detect “meditation” brain state by analyzing electroencephalographic (EEG) data. We firstly discuss what is “meditation” state and some prior studies on meditation. We then discuss how meditation state can be reflected in the subject’s brain waves; and what features of the brain waves data can be used in machine learning algorithms to classify meditation state from other states. We studied the suitability of 3 types of entropy: Shannon entropy, approximate entropy, and sample entropy in different circumstances. We found that overall Sample entropy is a good tool to extract information from EEG data. Discretization of EEG data enhances the classification rates by using both the approximate entropy and Shannon entropy.

HRV signals can be viewed as discrete time series and treated as well through accepted mathematical procedures in order to find specific properties. These mathematical procedures can be linear or non-linear. Lately, non-linear analysis methods brought new and valuable results in HRV analysis and prediction. This paper deals with approximate entropy and sample entropy calculations in order to find unrevealed properties of these signals. The used signals are obtained from the MIT-BIH Long-term ECG database. The aim of this paper is to measure information theory based parameters as different entropies for different signals to emphasize non-linear dynamics in HRV in order to help cardiology specialists.

Electroencephalography (EEG) based preliminary examination has been widely used in diagnosis of brain diseases. Based on previous studies, clinical brain death determination also can be actualized by analyzing EEG signal of patients. Dynamic Multivariate empirical mode decomposition (D-MEMD) and approximate entropy (ApEn) are two kinds of methods to analyze brain activity status of the patients in different perspectives for brain death determination. In our previous studies, D-MEMD and ApEn methods were always used severally and it cannot analyzing the patients’ brain activity entirety. In this paper, we present a combine analysis method based on D-MEMD and ApEn methods to determine patients’ brain activity level. Moreover, We will analysis three different status EEG data of subjects in normal awake, comatose patients and brain death. The analyzed results illustrate the effectiveness and reliability of the proposed methods.

Passive brain-computer interface (BCI) can monitor cognitive function through physiological signals in human-machine system. This paper established a passive BCI based on functional near-infrared spectroscopy (fNIRS) to detect the sustained attentional load. Three levels of attentional load were adjusted by modifying the number of stimulate in feature-absence Continuous Performance Test (CPT) tasks. 15 healthy subjects were recruited in total, and 10 channels were measured in prefrontal cortex (PFC). Performance and NASA-TLX scales were also recorded as reference. The mean value of oxyhemoglobin and deoxyhemoglobin, signal slope, power spectrum and approximate entropy in 0–10 s were extracted from raw fNIRS signal for support vector machine (SVM) classification. The best performance features were selected by SVM-RFE algorithm. In conclusion over 80% average accuracy was achived between easy and hard attentional load, which demonstrated fNIRS can be a proposed method to detect sustained attention load for a passive BCI.