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.
S. Donohue—This work was partially funded by DFG SFB 779 TP A14N.
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Doell, C., Donohue, S., Pätz, C., Borgelt, C. (2018). Training Neural Networks to Distinguish Craving Smokers, Non-craving Smokers, and Non-smokers. In: Duivesteijn, W., Siebes, A., Ukkonen, A. (eds) Advances in Intelligent Data Analysis XVII. IDA 2018. Lecture Notes in Computer Science(), vol 11191. Springer, Cham. https://doi.org/10.1007/978-3-030-01768-2_7
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