Searching for new biometric traits is currently a necessity because traditional ones such as fingerprint, voice, or face are highly prone to forgery. For this reason, the study of bioelectric signals has the potential to develop new biometric systems. A motivation for using electroencephalogram signals is that they are unique to each person and are much more difficult to replicate than conventional biometrics. The objective of this study is to analyze the factors that influence the performance of a biometric system based on electroencephalogram signals. This work uses six different classifiers to compare several decomposition levels of the discrete wavelet transform as a preprocessing technique and also explores the importance of the recording time. These classifiers are Gaussian Naïve Bayes Classifier, K-Nearest Neighbors, Random Forest, AdaBoost, Support Vector Machine, and Multilayer Perceptron. This work proves that the decomposition level does not have a high impact on the overall result of the system. On the other hand, the recording time of electroencephalograms has a significant impact on the performance of the classifiers. It is worth mentioning that this study used two different datasets to validate the results. Finally, our experiments show that Support Vector Machine and AdaBoost are the best classifiers for this particular problem since they achieved a sensitivity, specificity, and accuracy of 85.94±1.8, 99.55±0.06, 99.12±0.11 and 95.54±0.53, 99.91±0.01, and 99.83±0.02 respectively.