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

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


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.


Smoker Craving EEG Neural network Classification 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christoph Doell
    • 1
    Email author
  • 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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