This chapter introduces brain-computer interfaces (BCIs) with a focus on their role in human-computer interaction and neuroadaptive technology. We first define BCIs and distinguish active, reactive, and passive systems, emphasizing how EEG-based BCIs transform neural activity into real-time system behavior. We then review the main neural signals and sensing modalities used in BCIs, with an emphasis on wearable EEG and emerging form factors for everyday use. The core BCI pipeline is outlined from experimental design and labeling through preprocessing, feature extraction, machine learning, and validation, providing readers with a practical overview of how decoding models are built and assessed. Using motor imagery, P300/SSVEP, and a range of passive paradigms as examples, we illustrate how BCIs can decode intention, workload, error perception, vigilance, and related mental states. Building on this, we describe how passive BCIs enable neuroadaptive systems, from mental state assessment and open-loop feedback to closed-loop and autonomous adaptations. Finally, we discuss key practical challenges, including artifacts, non-stationarity, and cross-user generalization, as well as ethical issues around privacy and neurorights. The chapter concludes by outlining future trajectories for BCIs as a core component of human-centered, adaptive AI systems.