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Investigation of Functional Brain Connectivity by Electroencephalogram Signals using Data Mining Technique

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초록

Human brain is one of the most complex and the most vital human body organs with different parts of it being interconnected even if these parts are anatomically separate. It is essential to consider the brain function as an integrated system in order to get insight into the complex structure and function of the cerebral network as a key concept in neuroscience. The patterns obtained from the function of different brain areas and their processing techniques yield a complete set of information about the available relationship between brain areas and make it possible to analyze the function of the brain system correctly using new modeling tools. In this regard, in the present study, brain function was analyzed in 6 subjects using the picture-naming test (148 images of animals D249 and tools D250) and EEG signal recording method through 256 channels. In this two-session test (with 74 stimuli), 12 signals related to the brain function of these subjects were recorded and analyzed in delta, theta, beta, alpha and non-filter state. Furthermore, the pattern of relationship between the channels and the brain communication network in different areas was calculated and elicited in two modes of D249 and D250 (the test stimuli included animals and tools pictures) using the available tools and calculation methods (correlation coefficient, t-test and association rule mining). The obtained results showed that the frontal and temporal areas had the highest activity in comparison with other areas. The brain behavioral patterns in these subjects were very similar in the three bands of theta, beta and alpha.

Abstract

Human brain is one of the most complex and the most vital human body organs with different parts of it being interconnected even if these parts are anatomically separate. It is essential to consider the brain function as an integrated system in order to get insight into the complex structure and function of the cerebral network as a key concept in neuroscience. The patterns obtained from the function of different brain areas and their processing techniques yield a complete set of information about the available relationship between brain areas and make it possible to analyze the function of the brain system correctly using new modeling tools. In this regard, in the present study, brain function was analyzed in 6 subjects using the picture-naming test (148 images of animals D249 and tools D250) and EEG signal recording method through 256 channels. In this two-session test (with 74 stimuli), 12 signals related to the brain function of these subjects were recorded and analyzed in delta, theta, beta, alpha and non-filter state. Furthermore, the pattern of relationship between the channels and the brain communication network in different areas was calculated and elicited in two modes of D249 and D250 (the test stimuli included animals and tools pictures) using the available tools and calculation methods (correlation coefficient, t-test and association rule mining). The obtained results showed that the frontal and temporal areas had the highest activity in comparison with other areas. The brain behavioral patterns in these subjects were very similar in the three bands of theta, beta and alpha.

참고문헌 (25)

  1. Allen, E. A., "Tracking whole-brain connectivity dynamics in the resting state", Cerebral cortex, 24(3), pp.663~676 (2014)
  2. Antonenko, P., "Using electroencephalography to measure cognitive load", Educational Psychology Review, 22(4), pp.425~438 (2010)
  3. Babiloni, F., "Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function", Neuroimage, 24(1), pp.118~131 (2005)
  4. Barahimi, S., "STUDIES ON SCHIZOPHRENIA AND DEPRESSIVE DISEASES BASED ON FUNCTIONAL NEAR-INFRARED SPECTROSCOPY", Biomedical Engineering: Applications, Basis and Communications, pp.1830002~ (2018)
  5. Bullmore, E., "Complex brain networks : graph theoretical analysis of structural and functional systems", Nature Reviews Neuroscience, 10(3), pp.186~ (2009)
  6. Dadgostar, M., "Functional connectivity of the PFC via partial correlation", Optik-International Journal for Light and Electron Optics, 127(11), pp.4748~4754 (2016)
  7. Dadgostar, M., "Classification of schizophrenia using SVM via fNIRS", Biomedical Engineering: Applications, Basis and Communications, 30(02), pp.1850008~ (2018)
  8. Einalou, Z., "Functional near infrared spectroscopy to investigation of functional connectivity in schizophrenia using partial correlation", Universal Journal of Biomedical Engineering, 2(1), pp.5~8 (2014)
  9. Einalou, Z., "Functional near infrared spectroscopy for functional connectivity during Stroop test via mutual information", Adv. Biores, 6(1), pp.62~67 (2015)
  10. Einalou, Z., "Effective channels in classification and functional connectivity pattern of prefrontal cortex by functional near infrared spectroscopy signals", Optik-International Journal for Light and Electron Optics, 127(6), pp.3271~3275 (2016)
  11. Einalou, Z., "Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS", Neurophotonics, 4(4), pp.041407~ (2017)
  12. Fallani, F. D. V., "Brain network analysis from high-resolution EEG recordings by the application of theoretical graph indexes", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(5), pp.442~452 (2008)
  13. Friston, K. J., "Functional and effective connectivity in neuroimaging: a synthesis", Human brain mapping, 2(1-2), pp.56~78 (1994)
  14. Hassan, M., "EEG source connectivity analysis: from dense array recordings to brain networks", PloS one, 9(8), pp.e105041~ (2014)
  15. Honey, C. J., "Network structure of cerebral cortex shapes functional connectivity on multiple time scales", Proceedings of the National Academy of Sciences, 104(24), pp.10240~10245 (2007)
  16. Horwitz, B., "The elusive concept of brain connectivity", Neuroimage, 19(2), pp.466~470 (2003)
  17. Mheich, A., "A new algorithm for spatiotemporal analysis of brain functional connectivity", Journal of neuroscience methods, 242, pp.77~81 (2015)
  18. Rizkallah, J., "Brain network modules of meaningful and meaningless objects", Paper presented at the Biomedical Engineering (MECBME), 2016 3rd Middle East Conference on (2016)
  19. Rubinov, M., "Complex network measures of brain onnectivity : uses and interpretations", Neuroimage, 52(3), pp.1059~1069 (2010)
  20. Sakkalis, V., "Review of advanced techniques for the estimation of brain connectivity measured with EEG", MEG, 2011, pp.41(12), 1110~1117
  21. Salmelin, R., "Dynamics of brain activation during picture naming", Nature, 368(6470), pp.463~ (1994)
  22. Schendan, H. E., "Neurophysiological evidence for visual perceptual categorization of words and faces within 150 ms", Psychophysiology, 35(3), pp.240~251 (1998)
  23. Sporns, O., "Networks of the Brain" (2010), MIT press
  24. Watts, D. J., "Collective dynamics of ‘small-world’ networks", nature, 393(6684), pp.440~ (1998)
  25. Zhou, D., "MATLAB toolbox for functional connectivity", Neuroimage, 47(4), pp.1590~1607 (2009)

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