Using Visibility Graph to Analyze Brain Connectivity

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Majdi Hoda; AZARNOOSH MAHDI; GHOSHUNI MAJID; Sabzevari Vahid Reza

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Journal Paper

Paper Information

Journal: Neuroscience Journal of Shefaye Khatam Year:2022 | Volume:10 | Issue:2 Page(s): 57-67

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Title

Using Visibility Graph to Analyze Brain Connectivity

Author(s)

Majdi Hoda | AZARNOOSH MAHDI | GHOSHUNI MAJID | Sabzevari Vahid Reza | Issue Writer Certificate

Keywords

Electroencephalography

Brain-Computer Interfaces

Brain Mapping

Abstract

Introduction: Recognition of mental activities in brain-computer interface systems based on motor imagery has attracted the attention of many researchers. A visibility graph is a powerful method for analyzing the function and connectivity of different areas of the brain. The aim of this study is to improve and develop the visibility graph method for analyzing brain behavior and detecting motor imagery. Materials and Methods: First, brain signals including four motor imagery classes of left-handed, right-handed, foot, and tongue were transformed into three types of visibility graphs, and important features of these graphs were extracted. Then, to reduce features, the method of analysis of variance was used. To classify the motor imagery classes, the support vector machine was used. In most investigations, graph degree distribution has been used to extract information and graph weighting. In the present study, amplitude difference distribution has been used so shorter time series are required. To analyze the function and connectivity of different areas of the brain and to obtain the direction of information flow, a new method called weighted horizontal visibility graph-transfer entropy has been proposed. Results: Increasing the kappa value compared to other studies showed that a weighted horizontal visibility graph is a suitable method for processing brain signals based on motor imagery. A comparison of brain graphs and the direction of information flow in the four classes of motor imagery showed a significant difference between them. Conclusion: Temporal networks provide a better understanding of brain dynamics in brain-computer interface systems based on motor imagery.

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APA: Copy

Majdi, Hoda, AZARNOOSH, MAHDI, GHOSHUNI, MAJID, & Sabzevari, Vahid Reza. (2022). Using Visibility Graph to Analyze Brain Connectivity. NEUROSCIENCE JOURNAL OF SHEFAYE KHATAM, 10(2 ), 57-67. SID. https://sid.ir/paper/984884/en

Vancouver: Copy

Majdi Hoda, AZARNOOSH MAHDI, GHOSHUNI MAJID, Sabzevari Vahid Reza. Using Visibility Graph to Analyze Brain Connectivity. NEUROSCIENCE JOURNAL OF SHEFAYE KHATAM[Internet]. 2022;10(2 ):57-67. Available from: https://sid.ir/paper/984884/en

IEEE: Copy

Hoda Majdi, MAHDI AZARNOOSH, MAJID GHOSHUNI, and Vahid Reza Sabzevari, “Using Visibility Graph to Analyze Brain Connectivity,” NEUROSCIENCE JOURNAL OF SHEFAYE KHATAM, vol. 10, no. 2 , pp. 57–67, 2022, [Online]. Available: https://sid.ir/paper/984884/en

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