Brain-Computer Interfaces (BCIs) have emerged as a groundbreaking technology that connects the brain directly to external devices, bypassing traditional muscle and peripheral control. The advancements in neuroscience and machine learning allow users to control computers, artificial limbs, and other assistive devices using brain signals alone. By decoding brain activity, BCIs provide new communication and control methods for individuals with limited mobility and paralyzed, allowing them to speak, type, or manipulate objects in their environment. BCIs also play a vital role in neurorehabilitation, where they promote neural recovery by enhancing the brain’s plasticity. This dual application of BCIs, both in assistive technology and rehabilitation, makes them a powerful tool for enhancing patients’ quality of life and independence. The chapter provides a detailed overview of BCIs, discussing their history, development, and the technical foundations that enable them to translate neural signals into commands for external devices. It also covers the differences between invasive and non-invasive BCIs, highlighting their respective strengths. While assistive BCIs give individuals with disabilities newfound autonomy, rehabilitative BCIs work in synergy with physical therapy to restore motor skills and cognitive functions. The chapter also highlights the importance of the multimodal approaches that integrate additional technologies, such as virtual reality, robotics, and machine learning. These hybrid systems show promise in enhancing the precision, accuracy, and overall effectiveness of BCIs in both assistive and rehabilitative contexts. As these technologies evolve, BCIs are expected to become even more robust, offering better support for patients with disabilities and contributing to more effective neurorehabilitation strategies.