Effective brain connectivity for fNIRS data analysis based on multi-delays symbolic phase transfer entropy

We address the challenge of classifying financial time series via a newly proposed multiscale symbolic phase transfer entropy (MSPTE). Using MSPTE method, we succeed to quantify the strength and direction of information flow between financial systems and classify financial time series, which are the stock indices from Europe, America and China during the period from 2006 to 2016 and the stocks of banking, aviation industry and pharmacy during the period from 2007 to 2016, simultaneously. The MSPTE analysis shows that the value of symbolic phase transfer entropy (SPTE) among stocks decreases with the increasing scale factor. It is demonstrated that MSPTE method can well divide stocks into groups by areas and industries. In addition, it can be concluded that the MSPTE analysis quantify the similarity among the stock markets. The symbolic phase transfer entropy (SPTE) between the two stocks from the same area is far less than the SPTE between stocks from different areas. The results also indicate that four stocks from America and Europe have relatively high degree of similarity and the stocks of banking and pharmaceutical industry have higher similarity for CA. It is worth mentioning that the pharmaceutical industry has weaker particular market mechanism than banking and aviation industry.

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The cortical-muscular functional coupling performance evaluation based on harmonic wavelet and symbolic phase transfer entropy

2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) ◽

10.1109/cbs.2018.8612194 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xiaobi Chen ◽

Guanghua Xu ◽

Sicong Zhang ◽

Li min

Keyword(s):

Performance Evaluation ◽

Phase Transfer ◽

Transfer Entropy ◽

Functional Coupling ◽

Harmonic Wavelet ◽

Coupling Performance

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Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy

Cognitive Neurodynamics ◽

10.1007/s11571-021-09680-3 ◽

2021 ◽

Author(s):

Ali Ekhlasi ◽

Ali Motie Nasrabadi ◽

Mohammad Reza Mohammadi

Keyword(s):

Information Flow ◽

Phase Transfer ◽

Brain Regions ◽

Transfer Entropy ◽

Healthy Children

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Promises and pitfalls of Topological Data Analysis for brain connectivity analysis

10.1101/2021.02.10.430469 ◽

2021 ◽

Author(s):

Luigi Caputi ◽

Anna Pidnebesna ◽

Jaroslav Hlinka

Keyword(s):

Data Analysis ◽

Brain Connectivity ◽

Effective Connectivity ◽

Persistent Homology ◽

Brain Network ◽

Topological Data Analysis ◽

Support Vector ◽

Technical Problems ◽

Brain States ◽

Topological Data

AbstractDeveloping sensitive and reliable methods to distinguish normal and abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture its prominent tool of persistent homology may benefit from going beyond analysing structural and functional connectivity to effective connectivity graphs capturing the direct causal interactions or information flows. Therefore, we assess the potential of persistent homology to directed brain network analysis by testing its discriminatory power in two enigmatic examples of disease-related brain connectivity alterations: epilepsy and schizophrenia. We estimate connectivity from functional magnetic resonance imaging and electrophysiology data, employ Persistent Homology and quantify its ability to distinguish healthy from diseased brain states by applying a support vector machine to features quantifying persistent homology structure.We show how this novel approach compares to classification using standard undirected approaches and original connectivity matrices. In the schizophrenia classification, topological data analysis generally performs close to random, while classifications from raw connectivity perform substantially better; likely due to topographical, rather than topological, specificity of the differences. In seizure discrimination from scalp electroencephalography data, classification based on directed persistent homology features provided comparable results to other methods, reaching 89 percent accuracy. Specific niche for topological data analysis opens when direct comparison of connectivity matrices is unsuitable - such as for intracranial electrophysiology with individual number and location of measurements. While standard homology performed overall better than directed homology, this could be due to notorious technical problems of accurate effective connectivity estimation.

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Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v8i2.6515 ◽

2021 ◽

Author(s):

Ali Ekhlasi ◽

Ali Motie Nasrabadi ◽

Mohammadreza Mohammadi

Keyword(s):

Frequency Band ◽

Phase Transfer ◽

Effective Connectivity ◽

Brain Regions ◽

Transfer Entropy ◽

Healthy Children ◽

Eeg Signals ◽

Children With Adhd ◽

Frequency Bands ◽

Artificial Neural Network Ann

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods. Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

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