The development of Brain-Computer Interfaces (BCIs) has been constrained by a predominant focus on signal classification. This paper rather emphasizes the integration of neurophysiological principles, BCI paradigm selection, and rigorous experimental design. By addressing common pitfalls in Machine Learning implementation, we provide researchers with a tutorial and robust framework for BCI development, promoting reproducibility and rigor. Furthermore, by tackling challenges at the intersection of BCI and Machine Learning, this work contributes to the advancement of practical, real-time BCI applications.