This chapter provides an overview of the preprocessing techniques employed to refine raw brain signals for effective analysis in neuroscience research and clinical diagnostics. It emphasizes the necessity of preprocessing to mitigate the impact of noise and artifacts inherent in data acquired from various brain signal recording technologies such as EEG, fMRI, fNIRS, MEG, intracortical recordings, and electrocorticography. The sources of these disturbances are categorized into physiological artifacts—such as cardiac pulses, muscle activities, and ocular movements—and environmental noise, which include electromagnetic interference from nearby electronic devices and infrastructural elements. Discussions on the methodologies used to address these challenges highlight the critical role of preprocessing in enhancing the signal-to-noise ratio, ensuring data integrity, and facilitating the extraction of meaningful neural information. Techniques such as spatial and temporal filtering, artifact subtraction, and data normalization are explored to demonstrate how they tailor preprocessing approaches to the unique characteristics of each recording method. This chapter also underscores the dynamic nature of preprocessing, reflecting ongoing advancements in technology and methodology that continue to improve the accuracy and reliability of neuroscientific data analysis.