Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG

Gao, Zhong-Ke; Cai, Qing; Yang, Yuxuan; Dong, Na; Zhang, Shanshan

doi:10.1142/s0129065717500058

Cited by 156 publications

(71 citation statements)

References 92 publications

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“…The Logistic classifier reached an accuracy of 95% (38/40 participants correctly classified). Comparable results have been obtained using a leave-one-out cross-validation (LOOCV) [33]. …”

Section: Analysis and Resultsmentioning

confidence: 99%

The detection of faked identity using unexpected questions and mouse dynamics

2017

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The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent’s true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to “build” and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee.

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“…The Logistic classifier reached an accuracy of 95% (38/40 participants correctly classified). Comparable results have been obtained using a leave-one-out cross-validation (LOOCV) [33]. …”

Section: Analysis and Resultsmentioning

confidence: 99%

The detection of faked identity using unexpected questions and mouse dynamics

2017

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“…On the other hand, multivariate pseudo Wigner distribution allows uncovering local flow behavior revealing different oil—water flow patterns [41]. Gao et al proposed a multiscale limited penetrable horizontal visibility graph to analyze nonlinear time series [42] and then developed a novel AOK-TFR based visibility graph to classify epileptic EEG signals [43]. …”

Section: Introductionmentioning

confidence: 99%

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

2017

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Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

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“…Complex network theory [8][9][10][11][12][13][14][15][16][17][18] has undergone an explosive growth in recent years. In particular, complex network analysis of time series 19 has been well developed and it contributes greatly to solve challenging problems in different research fields.…”

Section: Introductionmentioning

confidence: 99%

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior

Gao

Dang

Yang

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The exploration of the spatial dynamical flow behaviors of oil-water flows has attracted increasing interests on account of its challenging complexity and great significance. We first technically design a double-layer distributed-sector conductance sensor and systematically carry out oil-water flow experiments to capture the spatial flow information. Based on the well-established recurrence network theory, we develop a novel multiplex multivariate recurrence network (MMRN) to fully and comprehensively fuse our double-layer multi-channel signals. Then we derive the projection networks from the inferred MMRNs and exploit the average clustering coefficient and the spectral radius to quantitatively characterize the nonlinear recurrent behaviors related to the distinct flow patterns. We find that these two network measures are very sensitive to the change of flow states and the distributions of network measures enable to uncover the spatial dynamical flow behaviors underlying different oil-water flow patterns. Our method paves the way for efficiently analyzing multi-channel signals from multi-layer sensor measurement system.

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Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG

Cited by 156 publications

References 92 publications

The detection of faked identity using unexpected questions and mouse dynamics

The detection of faked identity using unexpected questions and mouse dynamics

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior

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