Complexity–entropy causality plane based on power spectral entropy for complex time series

Dai, Yimei; Zhang, Hesheng; Mao, Xuegeng; Shang, Pengjian

doi:10.1016/j.physa.2018.06.081

Cited by 24 publications

(7 citation statements)

References 26 publications

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“…After singular value decomposition, the importance of decomposed quantities is determined according to the order of energy [ 41 ]. The spectral entropy can be expressed according to [ 42 ]. In the procedure, firstly, the signal is transformed by Fourier transform, then the power distribution of the signal is calculated and the unit power is normalized.…”

Section: Discussionmentioning

confidence: 99%

Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals

Hou

Chen²

2019

Computational Intelligence and Neuroscience

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Music can evoke a variety of emotions, which may be manifested by distinct signals on the electroencephalogram (EEG). Many previous studies have examined the associations between specific aspects of music, including the subjective emotions aroused, and EEG signal features. However, no study has comprehensively examined music-related EEG features and selected those with the strongest potential for discriminating emotions. So, this paper conducted a series of experiments to identify the most influential EEG features induced by music evoking different emotions (calm, joy, sad, and angry). We extracted 27-dimensional features from each of 12 electrode positions then used correlation-based feature selection method to identify the feature set most strongly related to the original features but with lowest redundancy. Several classifiers, including Support Vector Machine (SVM), C4.5, LDA, and BPNN, were then used to test the recognition accuracy of the original and selected feature sets. Finally, results are analyzed in detail and the relationships between selected feature set and human emotions are shown clearly. Through the classification results of 10 random examinations, it could be concluded that the selected feature sets of Pz are more effective than other features when using as the key feature set to classify human emotion statues.

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Section: Discussionmentioning

confidence: 99%

Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals

Hou

Chen²

2019

Computational Intelligence and Neuroscience

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“…Step 2: The power spectral entropy (PSE) [26,27] is constructed based on the original vibration data.…”

Section: Process Of Rul Prediction Methodsmentioning

confidence: 99%

Quantum Gene Chain Coding Bidirectional Neural Network for Residual Useful Life Prediction of Rotating Machinery

Cheng

Tang

et al. 2020

Preprint

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In classical recurrent neural networks, the pre- and post-relationships of time series tend to be neglected so that long-term overall memory is generally inaccessible; meanwhile, the weights are transferred and updated mainly by the gradient descent method, which leads to their low prediction accuracy and high computation cost in the application of residual useful life (RUL) prediction of rotating machinery (RM). In view of this, a quantum gene chain coding bidirectional neural network (QGCCBNN) is proposed to predict RUL of RM in this paper. In our proposed QGCCBNN, the quantum bidirectional transmission mechanism is designed to establish the pre- and post-relationships of time series for readjusting the weight parameters according to the feedback from the output layer, so that higher consistency between the input information and the overall memory of the network can be realized, thus endowing QGCCBNN with better nonlinear approximation ability. Moreover, in order to improve the global optimization ability and convergence speed, the quantum gene chain coding instead of gradient descent method is constructed to transmit and update data, in which the qubit probability amplitude real number coding is adopted and the cosine and sinusoidal qubit probability amplitudes corresponding to the minimum loss function are compared with those of the current time by the phase selection matrix for the directional parallel updating of the weight parameters. On this basis, a new RUL prediction method for RM is proposed, and higher prediction accuracy as well as desirable efficiency can be obtained due to the advantages of QGCCBNN in nonlinear approximation ability and convergence speed. The experimental example for RUL prediction of a double-row roller bearing demonstrates the effectiveness of our proposed method.

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“…As a parameter D , according to the authors of [ 16 ], one can choose any metric that determines the difference between the maximum entropy and the entropy of the studied signal. The simplest example of disequilibrium is the square of the Euclidean distance in

between the original distribution and the uniform distribution, but often, the Jensen–Shannon divergence [ 22 , 23 ] is also used.…”

Section: Information Criteriamentioning

confidence: 99%

“…In a series of articles [ 16 , 17 , 18 , 19 , 20 , 21 ], researchers introduced the concept of a statistical measure of signal complexity, which they called statistical complexity. In [ 22 , 23 ], statistical complexity and information entropy were used to classify various underwater objects of animate and inanimate nature from recorded sound. In the present article, we use this measure to indicate the appearance of an useful acoustic signal in a highly noisy mixture.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Information Criteria for Detection of Useful Signals in Noisy Environments

Berlin

Galyaev

Lysenko

2023

Sensors

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This paper considers the appearance of indications of useful acoustic signals in the signal/noise mixture. Various information characteristics (information entropy, Jensen–Shannon divergence, spectral information divergence and statistical complexity) are investigated in the context of solving this problem. Both time and frequency domains are studied for the calculation of information entropy. The effectiveness of statistical complexity is shown in comparison with other information metrics for different signal-to-noise ratios. Two different approaches for statistical complexity calculations are also compared. In addition, analytical formulas for complexity and disequilibrium are obtained using entropy variation in the case of signal spectral distribution. The connection between the statistical complexity criterion and the Neyman–Pearson approach for hypothesis testing is discussed. The effectiveness of the proposed approach is shown for different types of acoustic signals and noise models, including colored noises, and different signal-to-noise ratios, especially when the estimation of additional noise characteristics is impossible.

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Complexity–entropy causality plane based on power spectral entropy for complex time series

Cited by 24 publications

References 26 publications

Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals

Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals

Quantum Gene Chain Coding Bidirectional Neural Network for Residual Useful Life Prediction of Rotating Machinery

Comparison of Information Criteria for Detection of Useful Signals in Noisy Environments

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