Higher cognitive process efforts may result in mental exhaustion, poor performance, and long-term health issues. An EEG-based methods for detecting a pilot's mental state have recently been created utilizing machine learning algorithms. EEG signals include a significant noise component, and these approaches either ignore this or use a random mix of preprocessing techniques to reduce noise. In the absence of uniform preprocessing procedures for cleaning, it would be impossible to compare the efficacy of machine learning models across research, even if they employ data obtained from the same experiment. In this study, we intend to evaluate how preprocessing approaches affect the performance of machine learning models. To do this, we concentrated on fundamental preprocessing techniques, such as a band-pass filter and independent component analysis. Using a publicly accessible actual physiological dataset gathered from a pilot who was exposed to a variety of mental events, we explore the influence of these preprocessing strategies on two machine learning models, SVMs and ANNs. Our findings indicate that the performance of the models is unaffected by preprocessing techniques. Moreover, our findings indicate that the models were able to anticipate the mental states from merged data collected in two environments. These findings demonstrate the necessity for a standardized methodological framework for the application of machine learning models to EEG inputs.