Transforming raw EEG recordings into interpretable data is a complex process that demands careful preprocessing. While published studies often outline their methods, these can be difficult for researchers new to EEG analysis to fully grasp. This chapter aims to bridge that knowledge gap by providing a clear overview of essential preprocessing steps. We begin by discussing the core components of EEG preprocessing, including filtering, re-referencing, and artifact handling. These steps are foundational for improving data quality and ensuring the validity of subsequent analyses. Preprocessing is typically considered complete when the data is ready for segmentation, which is the process of dividing continuous EEG signals into meaningful time windows or epochs for analysis. Rather than advocating for a single, standardized pipeline, this chapter equips you with the essential knowledge needed to design and adapt preprocessing workflows suited to specific research goals. While not exhaustive, this overview is supported by references to key resources and recent literature to guide deeper exploration. To facilitate practical implementation, the final section presents a compilation of established EEG processing toolboxes and software to support the initiation and refinement of preprocessing pipelines.