Brain Network Constancy and Participant Recognition: an Integrated Approach to Big Data and Complex Network Analysis

Qiu, Lu; Nan, Wenya

doi:10.3389/fpsyg.2020.01003

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…Lun et al [ 72 ] used a novel deep learning framework based on graph convolutional neural networks (GCNs) that learned the generalized features, and this achieved a classification accuracy of 88.57%. Qiu et al [ 73 ] calculated the symbolic transfer entropy (STE) between electrode channels and constructed the brain networks of various cognitive behaviors of each participant by using the directed minimum spanning tree (DMST) algorithm. Finally, the spectral distribution set scoring (SDSS) method was used to recognize 69.35% of the labels.…”

Section: Resultsmentioning

confidence: 99%

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

Awais

Yusoff

Khan

et al. 2021

Sensors

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Motor imagery (MI)-based brain–computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain’s neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms—an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain–computer interfaces.

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Section: Resultsmentioning

confidence: 99%

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

Awais

Yusoff

Khan

et al. 2021

Sensors

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“…Therefore, an appropriate method for simplifying the graph is required. Because the two correlation matrices constructed herein are both directed matrices, we simplify network graphs using a DMST (Kwon et al, 2008; Qiu et al, 2020) [ 20 , 21 ] and a DPMFG (Ye et al, 2019) [ 22 ]. The DMST procedure is as follows.…”

Section: Methodologiesmentioning

confidence: 99%

Research on China’s Risk of Housing Price Contagion Based on Multilayer Networks

Qiu

Wang

2022

Entropy

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The major issue in the evolution of housing prices is risk of housing price contagion. To model this issue, we constructed housing multilayer networks using transfer entropy, generalized variance decomposition, directed minimum spanning trees, and directed planar maximally filtered graph methods, as well as China’s comprehensive indices of housing price and urban real housing prices from 2012 to 2021. The results of our housing multilayer networks show that the topological indices (degree, PageRank, eigenvector, etc.) of new first-tier cities (Tianjin, Qingdao, and Shenyang) rank higher than those of conventional first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzheng).

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“…We intend to use DMST and DPMFG to reduce the noise and create multilayer financial networks namely, third-layer (VD-DMST) and fourthlayer (VD-DPMFG) networks. The steps of DMST [39] are as follows:…”

Section: Construction Of a Risk Spillover Networkmentioning

confidence: 99%

Financial crisis prediction based on multilayer supervised network analysis

Lü

Wang

2022

Front. Phys.

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Financial crisis prediction is essential in preventing financial problems as its monitoring indicators help regulators judge the probability of future crises. In this context, the activities of the scientific community have been focused on the dynamics of single/multiple sequences and utilized unsupervised/supervised methods for financial crisis prediction. It is noteworthy that the cross-correlation between the risks of multiple economic entities makes financial network analysis paramount in crisis prediction. Focusing on this point, we propose a multilayer supervised network analysis (MSNA) method to train the multilayer network, and select the most suitable layer for financial crisis prediction. Specifically, we use 37 crucial stock market indices from 4 continents to create successive multilayer financial networks with 120-day windows and 1-day step by Pearson cross-correlation (PCC), variance decompositions (VD), transfer entropy (TE), minimum spanning tree (MST), directed MST (DMST), planar maximally filtered graph (PMFG) and directed PMFG (DPMFG) methods. Based on the multilayer network, we embed the graph neural network classification (GNNC) model and train the dynamic multilayer networks at each window scale (240,120, and 60 days). Finally, we conclude that the accuracy of the short window (60 days) is significantly higher than that of the long window. The network constructed by PCC with MST is the most suitable for short sequence (60 days) crisis prediction (AUC = 0.959), and the network constructed by TE with DMST is the most suitable for long sequence (240 days) crisis prediction (AUC = 0.772).

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Brain Network Constancy and Participant Recognition: an Integrated Approach to Big Data and Complex Network Analysis

Cited by 5 publications

References 41 publications

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

Research on China’s Risk of Housing Price Contagion Based on Multilayer Networks

Financial crisis prediction based on multilayer supervised network analysis

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