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:: Volume 10, Issue 2 (Spring 2022) ::
Shefaye Khatam 2022, 10(2): 57-67 Back to browse issues page
Using Visibility Graph to Analyze Brain Connectivity
Hoda Majdi , Mahdi Azarnoosh * , Majid Ghoshuni , VahidReza Sabzevari
Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran , M_Azarnoosh@mshdiau.ac.ir
Abstract:   (1502 Views)
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.
Keywords: Electroencephalography, Brain-Computer Interfaces, Brain Mapping
Full-Text [PDF 2697 kb]   (1505 Downloads)    
Type of Study: Research --- Open Access, CC-BY-NC | Subject: Cognitive Neuroscience
1. Sadiq MT, Yu X, Yuan Z, Aziz MZ, ur Rehman N, Ding W, et al. Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition. IEEE Transactions on Emerging Topics in Computational Intelligence. 2022. [DOI:10.1109/TETCI.2022.3147030]
2. Ghafourian P, Ghoshuni M, Vosoogh I. Evaluation of Exam Anxiety in Healthy Subjects using Brain Signals Analysis. The Neuroscience Journal of Shefaye Khatam. 2020; 8(3): 61-9. [DOI:10.29252/shefa.8.3.61]
3. Fallani FDV, Bassett DS. Network neuroscience for optimizing brain-computer interfaces. Physics of life reviews. 2019; 31: 304-9. [DOI:10.1016/j.plrev.2018.10.001]
4. Zhu G. Analysis of EEG signals using complex brain networks: University of Southern Queensland; 2014.
5. Lacasa L, Luque B, Ballesteros F, Luque J, Nuno JC. From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences. 2008; 105(13): 4972-5. [DOI:10.1073/pnas.0709247105]
6. Lacasa L, Luque B, Luque J, Nuno JC. The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion. EPL (Europhysics Letters). 2009; 86(3): 30001. [DOI:10.1209/0295-5075/86/30001]
7. Bashiri F, Mokhtarpour A. Depression classification and recognition by graph-based features of EEG signals. International Journal of Medical Engineering and Informatics. 2022; 14(3): 252-63. [DOI:10.1504/IJMEI.2022.122284]
8. Altundogan TG, Karaköse M, editors. EEG Signal Classification with Deep Neural Networks using Visibility Graphs. 2022 26th International Conference on Information Technology (IT); 2022. [DOI:10.1109/IT54280.2022.9743535]
9. Zhang X, Landsness EC, Chen W, Miao H, Tang M, Brier LM, et al. Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning. Journal of neuroscience methods. 2022; 366: 109421. [DOI:10.1016/j.jneumeth.2021.109421]
10. Zhu G, Li Y, Wen P. Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal. IEEE journal of biomedical and health informatics. 2014; 18(6): 1813-21. [DOI:10.1109/JBHI.2014.2303991]
11. Olamat A, Ozel P, Akan A. Synchronization Analysis In Epileptic EEG Signals Via State Transfer Networks Based On Visibility Graph Technique. International journal of neural systems. 2021: 215. [DOI:10.1142/S0129065721500416]
12. Bajaj S, Butler AJ, Drake D, Dhamala M. Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation. NeuroImage: Clinical. 2015; 8: 572-82. [DOI:10.1016/j.nicl.2015.06.006]
13. Kong T, Shao J, Hu J, Yang X, Yang S, Malekian R. EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph. Sensors. 2021; 21(5): 1870. [DOI:10.3390/s21051870]
14. Xuan Q, Zhou J, Qiu K, Xu D, Zheng S, Yang X. CLPVG: Circular limited penetrable visibility graph as a new network model for time series. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2022; 32(1): 013130. [DOI:10.1063/5.0048243]
15. Jirsa VK, McIntosh AR. Handbook of brain connectivity: Springer; 2007. [DOI:10.1007/978-3-540-71512-2]
16. Wiener N. The theory of prediction. Modern mathematics for engineers. New York. 1956; 165.
17. Granger CW. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society. 1969: 424-38. [DOI:10.2307/1912791]
18. Kaminski M, Liang H. Causal influence: advances in neurosignal analysis. Critical Reviews™ in Biomedical Engineering. 2005; (33): 4. [DOI:10.1615/CritRevBiomedEng.v33.i4.20]
19. Marinazzo D, Liao W, Chen H, Stramaglia S. Nonlinear connectivity by Granger causality. Neuroimage. 2011; 58(2): 330-8. [DOI:10.1016/j.neuroimage.2010.01.099]
20. Repper-Day C. Mapping dynamic brain connectivity using EEG, TMS, and Transfer Entropy: The University of Manchester (United Kingdom); 2017.
21. Schreiber T. Measuring information transfer. Physical review letters. 2000; 85(2): 461. [DOI:10.1103/PhysRevLett.85.461]
22. Moslemi B, Azmodeh M, Tabatabaei M, Alivandi Vafa M. The Effect of Transcranial Direct Current Stimulation on Dorsolateral Prefrontal Cortex: a Review of its Role on Cognitive Functions. The Neuroscience Journal of Shefaye Khatam. 2019; 8(1): 129-44. [DOI:10.29252/shefa.8.1.129]
23. Li S, Shang P. Analysis of nonlinear time series using discrete generalized past entropy based on amplitude difference distribution of horizontal visibility graph. Chaos, Solitons & Fractals. 2021; 144: 110687. [DOI:10.1016/j.chaos.2021.110687]
24. Brunner C, Leeb R, Müller-Putz G, Schlögl A, Pfurtscheller G. BCI Competition 2008-Graz data set A. Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology. 2008; 16: 1-6.
25. Ghumman MK, Singh S, Singh N, Jindal B. Optimization of parameters for improving the performance of EEG-based BCI system. Journal of Reliable Intelligent Environments. 2021; 7(2): 145-56. [DOI:10.1007/s40860-020-00117-y]
26. Ahmadlou M, Adeli H, Adeli A. New diagnostic EEG markers of the Alzheimer's disease using visibility graph. Journal of neural transmission. 2010; 117(9): 1099-109. [DOI:10.1007/s00702-010-0450-3]
27. Ballesteros F, Luque F, Lacasa L, Luque B, Nuno J. From time series to complex networks: the visibility graphs. Proc Natl Acad Sci USA. 2008; 105: 4972. [DOI:10.1073/pnas.0709247105]
28. Song Z, Zhan G, Lin Y, Fang T, Niu L, Zhang X, et al. Electroacupuncture Alters BCI-Based Brain Network in Stroke Patients. Computational intelligence and neuroscience. 2022. [DOI:10.1155/2022/8112375]
29. Hasani H, Jafari M. Dimension Reduction in fMRI Images based on Metaheuristic Algorithm to Diagnose Autism. The Neuroscience Journal of Shefaye Khatam. 2021; 9(3): 1-11. [DOI:10.52547/shefa.9.3.1]
30. Mohammadpoor M, Alizadeh A. Using Support Vector Machines as an Intelligent Algorithm for Detecting Seizures from EEG Signals. The Neuroscience Journal of Shefaye Khatam. 2021; 9(2): 1-9. [DOI:10.52547/shefa.9.2.1]
31. Heyrani A aN. The Effects of Bilateral Motor Training on the Power of Grip in Affected Hand of Children with Spastic Hemiplegic Cerebral Palsy. The Neuroscience Journal of Shefaye Khatam. 2021; 9(3): 27-35. [DOI:10.52547/shefa.9.3.27]
32. Ang KK, Chin ZY, Zhang H, Guan C, editors. Filter bank common spatial pattern (FBCSP) in brain-computer interface. 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence); 2008.

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Majdi H, Azarnoosh M, Ghoshuni M, Sabzevari V. Using Visibility Graph to Analyze Brain Connectivity. Shefaye Khatam 2022; 10 (2) :57-67
URL: http://shefayekhatam.ir/article-1-2303-en.html

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 10, Issue 2 (Spring 2022) Back to browse issues page
مجله علوم اعصاب شفای خاتم The Neuroscience Journal of Shefaye Khatam
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