This course introduces EEG-based Brain-computer Interfaces (BCIs) from end to end: quality control, preprocessing (filtering, artifact handling/ICA), epoching, feature extraction, decoding, evaluation (with strong emphasis on avoiding data leakage), and decision logic for feedback/control. Students implement and evaluate at least two BCI paradigms, including P300 and SSVEP (with motor imagery as an additional paradigm), and connect design choices to latency, accuracy, and robustness in both offline and streaming settings. The course builds the necessary machine learning and signal processing tools that are commonly used in BCIs.