When designing EEG-based Brain-Computer Interfaces (BCI), a crucial signal processing component is the feature extraction step. It consists in representing EEG signals by a number of values that describe the relevant information they contain. This lecture will first expose the main features that are used to represent EEG signals such as Motor Imagery or P300. However, due to volume conduction, EEG signals inherently have a low spatial resolution, and the information they contain is generally spread over several channels. This makes features extracted individually from each EEG channel not as efficient as it could be. To alleviate this issue and improve the signal-to-noise ratio, it is important to use spatial filtering algorithms, in order to gather the relevant information from multiple channels. Therefore, this lecture will also present the spatial filter algorithms that can be used for each feature representation. This will include inverse solutions, Common Spatial Patterns (CSP) and variants, or the xDAWN algorithm, among other.