© 2021 IEEE.In the late 1990s, it was discovered that Electroencephalogram (EEG) data carry genetically specific information. EEG-based biometric systems can be used by anyone, since every living person, even in a coma, produces an EEG signal. However, EEG signals are heavily affected by environmental influences. In this work, effects of alcohol, coffee and tea consumption to EEG based biometric systems are analyzed. For creating an EEG based user identification system, a Convolutional Neural Network (CNN) algorithm is utilized and it runs with % 89.06 accuracy on DEAP database. According to the classification results, alcohol, coffee and tea consumption of the misclassified samples were examined. Our experiment shows that, while alcohol consumption increases the misclassification rate, coffee consumption decreases it. On the other hand, it has not been observed that tea consumption has a significant effect on misclassification.