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Personal Identification Based on Brain Networks of EEG Signals

, Graz University of Technology, Graz, http://www.bbci.de/competition/iv/desc_2a.pdf. Bullmore, E. and Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems, Nature Reviews Neuroscience 10(3): 186-198. Chavez, M., Valencia, M., Latora, V. and Martinerie, J. (2010). Complex networks: New trends for the analysis of brain connectivity, International Journal of Bifurcation & Chaos 20(6): 1677-1686. Das, K., Zhang, S., Giesbrecht, B. and Eckstein, M.P. (2009). Using rapid

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Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography. Int J Comput Assist Radiol Surg. 2016;11(11):2007-2019. [5] Varsha YS, Shyry SP. A Novel Approach for Identifying the Stages of Brain Tumor. International Journal of Computer Trends and Technology (IJCTT) 2014. 10. [6] Jazayeri SB, et al. Epidemiology of Primary CNS Tumors in Iran: A Systematic. Asian Pacific Journal of Cancer Prevention. 2013;14(6):3979-3985. [7] Ferlay J, et al. World cancer statistics for the most common cancers 2012. 2012

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Neurobiology of Consciousness: Current Research and Perspectives

which midline regions contribute to selfevaluation”, Frontiers in Human Neuroscience 7, 2013, pp. 45. 47. Foster, P.P., “Role of physical and mental training in brain network configuration”, Frontiers in Aging Neuroscience 7, 2015, pp. 117. 48. Gallagher, S., “Philosophical conceptions of the self: implications for cognitive science”, Trends in Cognitive Science 4(1), 2000, pp. 14-21. 49. Gallagher, S., “A pattern theory of self”, Frontiers in Human Neuroscience 7, 2013, pp. 443. 50. Gallese, V., Lakoff

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The Default Mode Network and the Problem of Determining Intrinsic Mental Contents

References Addis, D. R., Wong, A. T., & Schacter, D. L. (2007). Remembering the past and imagining the future: Common and distinct neural substrates during event construction and elaboration. Neuropsychologia, 45(7), 1363-1377. Anticevic, A., Cole, M. W., Murray, J. D., Corlett, P. R., Wang, X-J., & Krystal, J. H. (2012). The role of default network deactivation in cognition and disease. Trends in Cognitive Sciences, 16(12), 584-592. Bechtel, W. (2013). The endogenously active brain: The need for an alternative

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Will We Hit a Wall? Forecasting Bottlenecks to Whole Brain Emulation Development

. Fiala, J. 2002. Three-dimensional structure of synapses in the brain and on theWeb. Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290) 1-4. Hanson, R. 1994. If Uploads Come First: The Crack of a Future Dawn. Extropy 6(2). Hanson, R. 2009. The Economics of Brain Emulations. In Healey, P., and Rayner, S., eds., Unnatural Selection: The Challenges of Engineering Tomorrow’s People. Sterling, VA: Earthscan. Hausmann, R.; Hidalgo, C. A.; Bustos, S.; Coscia, M.; Chung, S

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Generalization of Patterns by Identification with Polynomial Neural Network

.—VAVROCH, O.—FRANTA, V.: Principles of Modelling (Základy modelování, SNTL, Praha, 1989. (in Czech) BEŇUŠKOVÁ, Ľ.: Neuron and Brain. Cognitive Sciences. (Neurón a mozog. Kognitívne vedy), Calligram, Bratislava, 2002. (in Slovak) ZJAVKA, L.: Polynomial Neural Network, Transcom 2007, 7-th European conference proceedings. Žilina June 25-27, 2007. ZJAVKA, L.: Dependence of Variables Identification with Polynomial Neural Network, Proceedings of CSE 2008 International Scientific Conference on Computer

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Prediction of e-Learning Efficiency by Neural Networks

References 1. John, M. Economic Forecasting Challenges and Neural Network Solutions. Computer Science Dept., Oregon Graduate Institut, USA, 1995. 2. Hou, Zai-En, Fu-Jian Duan. The Neural Network Method of Economy Forecasting. World Congress on Software Engineering, IEEE, 2009. 3. Leonardo, M. S., R. Ballini. Design a Neural Network for Time Series Financial Forecasting: Accuracy and Robustness Analisys. Instituto de Economia, Universidade Estadual de Campinas, Sao Paulo-Brasil, 2008. 4

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Processing EEG signals acquired from a consumer grade BCI device

domain properties” in Electroencephalography and Clinical Neurophysiology, Volume 29, Issue 3, 306 – 310 [32] S-H. Oh, Y-R. Lee, and H-N. Kim, “A Novel EEG Feature Extraction Method Using Hjorth Parameter” in International Journal of Electronics and Electrical Engineering, Vol. 2, No. 2, pp. 106-110, June 2014. [33] Y. Liu, Y. Lin, S. Wu, C. Chuang and C. Lin, “Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 2, pp. 347-360, Feb

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The neurobiology of language: Relevance to linguistics

: An fMRI study of modality effects and individual differences in language comprehension” Psychol Neurosci 2 111 123 Bullmore, E. and O. Sporns. 2009. “Complex brain networks: graph theoretical analysis of structural and functional systems”. Nat Rev Neurosci 10. 186–198. Bullmore E. Sporns O. 2009 “Complex brain networks: graph theoretical analysis of structural and functional systems” Nat Rev Neurosci 10 186 198 Copeland, J. 1993. Artificial intelligence: A philosophical introduction . Hoboken: Wiley

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Evolutionary Schema of Modeling Based on Genetic Algorithms

References Churchland, P. S. (1986). Neuropsychology: Toward a unified science of mind/brain. Cambridge, MA: MIT Press. Frigg, R., & Hartmann, S., (2012). Models in science. In E. N Zalta (Ed.), The Stanford encyclopedia of philosophy (Fall 2012 ed.). Retrieved from http://plato.stanford.edu/archives/fall2012/entries/models-science/. Harel, D. (1987). Algorithmics: The spirit of computing. Reading, MA: Addison-Wesley. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine

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