  In this paper, we explore prior research and introduce a new methodology for
classifying mental state levels based on EEG signals utilizing machine learning
(ML). Our method proposes an optimized training method by introducing a
validation set and a refined standardization process to rectify data leakage
shortcomings observed in preceding studies. Furthermore, we establish novel
benchmark figures for various models, including random forest and deep neural
networks.
