JACIII Vol.15 No.9 pp. 1221-1227
doi: 10.20965/jaciii.2011.p1221


Evaluating Functional Connectivity in Alcoholics Based on Maximal Weight Matching

Guohun Zhu*,***, Yan Li*,***, and Peng (Paul) Wen**,***

*Department of Mathematics and Computing, University of Southern Queensland

**Faculty of Engineering and Surveying, University of Southern Queensland

***Centre for Systems Biology, University of Southern Queensland, USQ Toowoomba Campus, West Street, Toowoomba, QLD 4350, Australia

March 2, 2011
September 29, 2011
November 20, 2011
EEG, greedy maximal weight matching, synchronization, repeated and unrepeated stimuli
EEG-based applications have faced the challenge of multi-modal integrated analysis problems. In this paper, a greedy maximal weight matching approach is used to measure the functional connectivity in alcoholics datasets with EEG and EOG signals. The major discovery is that the processing of the repeated and unrepeated stimuli in the γ band in control drinkers is significantly more different than that in alcoholic subjects. However, the EOGs are always stable in the case of visual tasks, except for a weakly wave when subjects make an error response to the stimuli.
Cite this article as:
G. Zhu, Y. Li, and P. Wen, “Evaluating Functional Connectivity in Alcoholics Based on Maximal Weight Matching,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.9, pp. 1221-1227, 2011.
Data files:
  1. [1] E. A. de Bruin, S. Bijl, et al., “Abnormal EEG synchronization in heavily drinking students,” Clinical Neurophysiology, Vol.115, No.9, pp. 2048-2055, 2004.
  2. [2] E. P. Hayden, R. E. Wiegand, et al., “Patterns of Regional Brain Activity in Alcohol-Dependent Subjects,” Alcoholism: Clinical and Experimental Research, Vol.30, No.12, pp. 1986-1991, 2006.
  3. [3] A. Michael et al., “Interhemispheric electroencephalographic coherence as a biological marker in alcoholism,” Acta Psychiatrica Scandinavica, Vol.87, pp. 213-217, 1993.
  4. [4] G.Winterer et al., “EEG phenotype in alcoholism: increased coherence in the depressive subtype,” Acta Psychiatrica Scandinavica, Vol.108, pp. 51-60, 2003.
  5. [5] P. E. Keller, G. Knoblich, et al., “Pianists duet better when they play with themselves: On the possible role of action simulation in synchronization,” Consciousness and Cognition, Vol.16, No.1, pp. 102-111, 2007.
  6. [6] X. L. Zhang, H. Begleiter, et al., “Electrophysiological evidence of memory impairment in alcoholic patients,” Biological Psychiatry, Vol.42, No.12, pp. 1157-1171, 1997.
  7. [7] V. Sakkalis et al., “Optimal brain network synchrony visualization: Application in an alcoholism paradigm,” in Engineering in Medicine and Biology Society, 2007 (EMBS 2007), 29th Annual Int. Conf. of the IEEE, Lyon, pp. 4285-4288, October 22, 2007.
  8. [8] P. Salvador, et al., “Markovian Models forMedical Signals onWireless Sensor Networks,” in Communications Workshops, 2009, ICC Workshops 2009, IEEE Int. Conf. on, pp. 1-5, 2009.
  9. [9] B. Hu, et al., “EEG-Based Cognitive Interfaces for Ubiquitous Applications: Developments and Challenges,” Intelligent Systems, IEEE, Vol.26, pp. 46-53, 2011.
  10. [10] D. J. Watts, S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature, Vol.393, pp. 440-442, 1998.
  11. [11] M. D. Humphries, K. Gurney, et al., “The brainstem reticular formation is a small-world, not scale-free, network,” Proc. of the Royal Society B: Biological Sciences, Vol.273, No.1585, pp. 503-511, 2006.
  12. [12] S. Micheloyannis, E. Pachou, et al., “Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis,” Neuroscience Letters, Vol.402, No.3, pp. 273-277, 2006.
  13. [13] R. Palaniappan, P. Raveendran, et al., “VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics,” IEEE Trans. on Neural Networks, Vol.13, No.2, pp. 486-491, 2002.
  14. [14] M. Rangaswamy, B. Porjesz, et al., “Resting EEG in offspring of male alcoholics: beta frequencies,” Int. J. of Psychophysiology, Vol.51, No.3, pp. 239-251, 2004.
  15. [15] K. Ravi and R. Palaniappan, “Neural network classification of late gamma band electroencephalogram features,” Soft Computing – A Fusion of Foundations, Methodologies and Applications, Vol.10, No.2, pp. 163-169, 2006.
  16. [16] J. Theiler, “Spurious dimension from correlation algorithms applied to limited time-series data,” Physical Review A, Vol.34, No.3, pp. 2427-2432, 1986.
  17. [17] C. J. Stam and B. W. van Dijk, “Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets,” Physica D: Nonlinear Phenomena, Vol.163, No.3, pp. 236-251, 2002.
  18. [18] E. J. Sanz-Arigita et al., “Loss of ‘Small-World’ Networks in Alzheimer’s Disease: Graph Analysis of fMRI Resting-State Functional Connectivity,” PLoS ONE, Vol.5, p. e13788, 2010.
  19. [19] F. Takens, “Detecting strange attractors in turbulence Dynamical Systems and Turbulence,” Warwick 1980, D. Rand and L.-S. Young (Eds.), Springer Berlin/Heidelberg, Vol.898, pp. 366-381, 1981.
  20. [20] H. N. Gabow, “An Efficient Implementation of Edmonds Algorithm for Maximum Matching,” JACM, Vol.23, No.2, pp. 221-234, 1976.
  21. [21] P. Tichavsky and Z. Koldovsky, “Optimal pairing of signal components separated by blind techniques,” IEEE Signal Processing Letters, Vol.11, No.2, pp. 119-122, 2004.
  22. [22] A. Dapena, M. F. Bugallo, and L. Castedo, “Separation of convolutive mixtures of temporally-white signals: a novel frequencydomain approach,” In: Proc. Int. Conf. Independent Component Analysis and Blind Source Separation (ICA), San Diego, USA, pp. 315-320, 2001.

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