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Screening for chronic alcoholic subjects using multiple gamma band EEG: a pilot study
Keywords:
Alcoholic subject discrimination, Electroencephalogram, Gamma band energy, Neural networkAbstract
Electrophysiological impairments of alcoholism have been researched extensively. However, there is none or few reported research on screening methods for chronic alcoholic subjects. Since chronic alcoholics have serious brain dysfunction, a method to screen for them during specific job applications that require good memory, concentration and/or decision making would be useful. In this paper, a method is proposed to discriminate chronic alcoholic from non-alcoholic subjects while they are sober. Energies of electroencephalogram signals in multiple gamma bands recorded while the subjects performed a picture recognition task are used as features by a neural network to detect the chronic alcoholic subjects. Leave one out cross validation strategy reveals that alcoholics could be discriminated from non-alcoholics with accuracy of 94.55%. This pilot study has shown the potential of the method which could be further developed for use in automatic alcoholic screening procedures.
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References
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[2] L. Almasy, B. Porjesz, J. Blangeo, A. Goate, H.J. Edenberg, D.B. Chorlian, S. Kuperman, S.J. O’Connor, J. Rohrbaugh, L.O. Bauer, T. Foroud, J.P. Rice, and T. Reich, “Genetics of event-related brain potentials in response to a semantic priming paradigm in families with a history of alcoholism”,American Journal of Human Genetics, Vol. 68, 2001, pp. 128-135.
[3] E. Basar, Memory and Brain Dynamics: Oscillations Integrating Attention, Perception, Learning, and Memory. Boca Raton: CRC Press, 2004.
[4] H. Begleiter, B. Porjesz, and W. Wang, A Neurophysiologic Correlate of Visual Short-term Memory in Humans. Electroencephalography and Clinical Neurophysiology, Vol. 87, 1993, pp. 46-53.
[5] H. Begleiter, B. Porjesz, T. Reich, H.J. Edenberg, A. Goate, J. Blangero, L. Almasy, T. Foroud, P. V. Eerdewegh, J. Polich, J. Rohrbaugh, S. Kuperman, L.O. Bauer, S.J. O’Connor, D.B. Chorlian, T.K. Li, P.M. Conneally, V. Hesselbrock, J.P. Rice, M.A. Schuckit, R. Cloninger, J. Nurnberger Jr., R. Crowe, and F.E. Bloom, “ Quantitative Trait Loci Analysis of Human Event-related Brain Potentials”, Electroencephalography and Clinical Neurophysiology, Vol. 108, 1998, pp. 244-250.
[6] J. G. R. Jefferys, R.D. Traub, and M.A. Whittington, “ Neuronal networks for induced “ 40 Hz” rhythms”, Trends in Neurosciences, Vol. 19, 1996, pp.202-208.
[7] K. E. Misulis, Spehlmann’s Evoked Potential Primer: Visual, Auditory and Somatosensory Evoked Potentials in Clinical Diagnosis,Butterworth-Heinemann, 1994.
[8] R. Palaniappan, P. Raveendran, and S. Omatu, “ VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics”, IEEE Transactions on Neural Network, Vol. 13, No. 2, 2002, pp.486-491.
[9] J.G. Snodgrass and M. Vanderwart, “ A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity”, Journal of Experimental Psychology: Human Learning and Memory, Vol. 6, No. 2, 1980, pp.174-215.
[10] X. L. Zhang, H. Begleiter, B. Porjesz, and A. Litke, “ Electrophysiological Evidence of Memory Impairment in Alcoholic Patients”, Biological Psychiatry, Vol.42, 1997, pp.1157-1171.
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Published
2007-04-02
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[1]
“Screening for chronic alcoholic subjects using multiple gamma band EEG: a pilot study”, JCS&T, vol. 7, no. 02, pp. p. 182–185, Apr. 2007, Accessed: Mar. 06, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/790
