This chapter talks about brain signal analysis, focusing on the crucial steps of feature extraction and selection, followed by the application of machine learning techniques. It begins by detailing the inherent complexity of brain signals and the necessity for robust feature engineering to transform these signals into a more interpretable and manageable form. Various types of features—time-domain, frequency-domain, time-frequency domain, and spectral domain—are explored, each providing unique insights into brain function. The feature extraction process is vital for reducing data dimensionality while preserving essential information, which is critical for subsequent analyses. The chapter also discusses different machine learning methodologies applied to the refined data, including supervised, semi-supervised, unsupervised, and reinforcement learning, each offering distinct advantages for specific applications in brain–computer interfaces (BCIs) and other neurotechnological applications. Practical examples of how these methods are applied to real-world data, such as EEG, fMRI, and MEG signals, illustrate the theoretical concepts discussed. The groundwork laid here sets the stage for further detailed discussions in subsequent volumes, focusing on the expansive applications of these technologies in neurology, rehabilitation, and beyond.