Brain-Computer Interface (BCI) technology is a scientific field focused on establishing a direct communication pathway between the human brain and the digital world. This review covers the evolution of this work, from the initial challenges of non-invasive and invasive signal acquisition to the complex signal processing and machine learning pipelines needed to interpret user intent. We trace the development of core interaction methods—including Motor Imagery (MI), P300 Event-Related Potentials, and Steady-State Visual Evoked Potentials (SSVEP)—which provide the distinct languages for brain-computer dialogue. The central thesis of this paper is that the most critical development in the field has been the shift from purely technical optimization toward integrated, multimodal, and human-centric systems. This change, which prioritizes user experience (UX), cognitive load reduction, and reliable performance in real-world settings, is essential for transitioning BCI from a laboratory concept to a practical, intuitive extension of human capability. By examining the range of clinical, consumer, and industrial applications along with their important ethical considerations, this review argues that the future of effective and accessible BCIs depends on this human-centric, multimodal approach.