EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph

Kong, Tianjiao; Shao, Jie; Hu, Jiuyuan; Yang, Xin; Yang, Shiyiling; Malekian, Reza

doi:10.3390/s21051870

Cited by 22 publications

(6 citation statements)

References 55 publications

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“…Due to the effectiveness of the VG algorithm, the family of VG algorithms has been applied in different fields to address practical problems, such as financial time series [ 115 – 117 ], electroencephalogram (EEG) signal [ 70 , 118 ], traffic data [ 119 , 120 ], earthquake time series [ 121 , 122 ], and market dataset [ 123 , 124 ].…”

Section: Visibility Graphmentioning

confidence: 99%

“…Numerous properties in the time series can be revealed by the family of visibility graphs, for example, estimating the Hurst exponent of fractal stochastic processes [ 62 , 63 ], proving the relationship between the power-law degree distribution and fractality in series [ 57 ], analyzing multifractal properties of time series [ 64 ], and measuring the irreversibility of real-valued time series [ 65 – 67 ]. In addition, it has been applied in different fields to address practical problems, including studying the dynamics of a passive scalar plume [ 68 ], planning long-voyage routes [ 69 ], analyzing electroencephalogram signals [ 70 ], extracting hidden information in coupled timessss series [ 71 , 72 ], and aggregating data in complex systems [ 73 ].…”

Section: Introductionmentioning

confidence: 99%

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Visibility graph for time series prediction and image classification: a review

2022

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The analysis of time series and images is significant across different fields due to their widespread applications. In the past few decades, many approaches have been developed, including data-driven artificial intelligence methods, mechanism-driven physical methods, and hybrid mechanism and data-driven models. Complex networks have been used to model numerous complex systems due to its characteristics, including time series prediction and image classification. In order to map time series and images into complex networks, many visibility graph algorithms have been developed, such as horizontal visibility graph, limited penetrable visibility graph, multiplex visibility graph, and image visibility graph. The family of visibility graph algorithms will construct different types of complex networks, including (un-) weighted, (un-) directed, and (single-) multi-layered networks, thereby focusing on different kinds of properties. Different types of visibility graph algorithms will be reviewed in this paper. Through exploring the topological structure and information in the network based on statistical physics, the property of time series and images can be discovered. In order to forecast (multivariate) time series, several variations of local random walk algorithms and different information fusion approaches are applied to measure the similarity between nodes in the network. Different forecasting frameworks are also proposed to consider the information in the time series based on the similarity. In order to classify the image, several machine learning models (such as support vector machine and linear discriminant) are used to classify images based on global features, local features, and multiplex features. Through various simulations on a variety of datasets, researchers have found that the visibility graph algorithm outperformed existing algorithms, both in time series prediction and image classification. Clearly, complex networks are closely connected with time series and images by visibility graph algorithms, rendering complex networks to be an important tool for understanding the characteristics of time series and images. Finally, we conclude in the last section with future outlooks for the visibility graph.

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Section: Visibility Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visibility graph for time series prediction and image classification: a review

2022

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“…For the problems caused by these factors, complex network can realize the topology analysis of device variable attributes and complex interaction system. Complex networks, as a powerful tool to describe the relationship between multiple process variables in complex systems, are widely used in psychology, electricity, social networking, disease transmission, meteorology and other aspects [7][8][9][10][11][12]. In practice, most of the research on complex networks is to simply abstract many individuals into a single node, so as to discuss their various nonlinear functions and connections [13]; This method cannot reflect the correlation structure between them in time and space, and multi-layer network can meet this requirement.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Fault Propagation Traceability of Chemical Process Based on Complex Network Method

2023

IEIM

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In order to ensure the safe production of chemical handicrafts, it is necessary to monitor the process variables of the chemical system in order to deal with the failures. In order to improve the accuracy and accuracy of the monitoring process, the whole process is modeled to find out the key variables that cause the fault, and at the same time, the impact of the key variables in the system is analyzed to find out the propagation path of the key variables. Based on the complex network theory, the fault node set is obtained by combining the measured data with the horizontal visibility map; At the same time, the fault propagation complex network is constructed, and the fault propagation source node is identified according to the topology characteristics of the complex network. A traceback algorithm is proposed to determine the propagation path of the source node; The importance ranking (IR) is used to quantify the impact of the propagation path on the system and focus on monitoring the key nodes in the path. By taking the Tennessee Eastman process as the verification object, the results show that the method can identify the occurrence of the fault and control the propagation path of the fault, which proves the effectiveness of the method.

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“…There are many worthy research topics in network science including but not limiting to community detection [16], fractal dimension [17,18], link prediction [19], evolutionary game theory [20][21][22], self similarity analysis [23] and so forth. Algorithms and tools in network science can also be used for time series analysis [24,25], pattern recognition [26,27], multi-criteria decision making [28,29], uncertainty modeling [30], recommender system [31,32], just to name a few. We will see the emerging progress in both the theory and the applications of network science in the near future.…”

Section: Introductionmentioning

confidence: 99%

A modified gravity model based on network efficiency for vital nodes identification in complex networks

Li¹,

Shang²,

Deng³

2021

Preprint

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Vital nodes identification is an essential problem in network science. Various methods have been proposedto solve this problem. In particular, based on the gravity model, a series of improved gravity models are proposed to find vital nodes better in complex networks. However, they still have the room to be improved.In this paper, a novel and improved gravity model, which is named network efficiency gravity centrality model (NEG), integrates gravity model and network efficiency is proposed. Compared to other methods based on different gravity models, the proposed method considers the effect of the nodes on structure robustness of the network better. To solidate the superiority of the proposed method, experiments on varieties of real-world networks are carried out.

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EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph

Cited by 22 publications

References 55 publications

Visibility graph for time series prediction and image classification: a review

Visibility graph for time series prediction and image classification: a review

Fault Diagnosis and Fault Propagation Traceability of Chemical Process Based on Complex Network Method

A modified gravity model based on network efficiency for vital nodes identification in complex networks

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