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Training Neural Networks to Distinguish Craving Smokers, Non-craving Smokers, and Non-smokers

  • Christoph Doell
  • Sarah Donohue
  • Cedrik Pätz
  • Christan Borgelt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11191)

Abstract

In the present study, we investigate the differences in brain signals of craving smokers, non-craving smokers, and non-smokers. To this end, we use data from resting-state EEG measurements to train predictive models to distinguish these three groups. We compare the results obtained from three simple models – majority class prediction, random guessing, and naive Bayes – as well as two neural network approaches. The first of these approaches uses a channel-wise model with dense layers, the second one uses cross-channel convolution. We therefore generate a benchmark on the given data set and show that there is a significant difference in the EEG signals of smokers and non-smokers.

Keywords

Smoker Craving EEG Neural network Classification 

References

  1. 1.
    Arlot, Sylvain, Celisse, Alain: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Brown, Barbara B.: Some characteristic EEG differences between heavy smoker and non-smoker subjects. Neuropsychologia 6(4), 381–388 (1968)CrossRefGoogle Scholar
  3. 3.
    Donohue, S.E., Woldorff, M.G., Hopf, J.-M., Harris, J.A., Heinze, H.-J., Schoenfeld, M.A.: An electrophysiological dissociation of craving and stimulus-dependent attentional capture in smokers. Cogn. Affect. Behav. Neurosci. 16(6), 1114–1126 (2016)CrossRefGoogle Scholar
  4. 4.
    Gramfort, A., et al.: MEG and EEG data analysis with MNE-Python. Front. Neurosci. 7 (2013)Google Scholar
  5. 5.
    Hochreiter, Sepp, Schmidhuber, Jürgen: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  6. 6.
    Knott, Verner, Cosgrove, Meaghan, Villeneuve, Crystal, Fisher, Derek, Millar, Anne, McIntosh, Judy: EEG correlates of imagery-induced cigarette craving in male and female smokers. Addict. Behav. 33(4), 616–621 (2008)CrossRefGoogle Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 1097–1105 (2012)Google Scholar
  8. 8.
    Le Boudec, Jean-Yves: Performance Evaluation of Computer and Communication Systems. EPFL Press, Lausanne (2010)zbMATHGoogle Scholar
  9. 9.
    Lerman, C., Gu, H., Loughead, J., Ruparel, K., Yang, Y., Stein, E.A.: Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function. JAMA psychiatry 71(5), 523–530 (2014)CrossRefGoogle Scholar
  10. 10.
    Logemann, H.N.A., Böcker, K.B.E., Deschamps, P.K.H., Kemner, C., Kenemans, J.L.: The effect of the augmentation of cholinergic neurotransmission by nicotine on EEG indices of visuospatial attention. Behav. Brain Res. 260, 67–73 (2014)CrossRefGoogle Scholar
  11. 11.
    Luck, S.J.: An introduction to the event-related potential technique (cognitive neuroscience). A Bradford Book (2005)Google Scholar
  12. 12.
    Pariyadath, V., Stein, E.A., Ross, T.J.: Machine learning classification of resting state functional connectivity predicts smoking status. Front. Hum. Neurosci. 8, 425 (2014)CrossRefGoogle Scholar
  13. 13.
    Rass, O., Ahn, W.Y., O’Donnell, B.F.: Resting-state EEG, impulsiveness, and personality in daily and nondaily smokers. Clin. Neurophys. 127(1), 409–418 (2016)CrossRefGoogle Scholar
  14. 14.
    Schetinin, V., Jakaite, L., Nyah, N., Novakovic, D., Krzanowski, W.: Feature extraction with gmdh-type neural networks for EEG-based person identification. Int. J. Neural Syst. 1750064 (2017)Google Scholar
  15. 15.
    Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG. arXiv preprint arXiv:1703.05051 (2017)
  16. 16.
    Sebastiani, Fabrizio: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Stoeckel, L.E., Chai, X.J., Zhang, J., Whitfield-Gabrieli, S., Evins, A.E.: Lower gray matter density and functional connectivity in the anterior insula in smokers compared with never smokers. Addict. Biol. 21(4), 972–981 (2016)CrossRefGoogle Scholar
  18. 18.
    Sutton, Samuel, Braren, Margery, Zubin, Joseph, John, E.R.: Evoked-potential correlates of stimulus uncertainty. Science 150(3700), 1187–1188 (1965)CrossRefGoogle Scholar
  19. 19.
    Weiland, B.J., Sabbineni, A., Calhoun, V.D., Welsh, R.C., Hutchison, K.E.: Reduced executive and default network functional connectivity in cigarette smokers. Hum. Brain Mapp. 36(3), 872–882 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christoph Doell
    • 1
  • Sarah Donohue
    • 2
  • Cedrik Pätz
    • 3
  • Christan Borgelt
    • 1
    • 3
  1. 1.Dept of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
  2. 2.Department of Behavioral NeurologyLeibnitz-Institute for NeurobiologyMagdeburgGermany
  3. 3.Institute for Intelligent Cooperating SystemsUniversity of MagdeburgMagdeburgGermany

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