As a revolutionary human-computer interaction technology, Brain-Computer Interface (BCI) provides a new paradigm for intelligent control of Unmanned Aerial Vehicles (UAVs). From a neuroergonomic perspective, this paper systematically reviews the research progress, key technologies, and future challenges of BCI-based UAV control systems. It first summarizes the research status of UAV control systems from the perspective of multimodal BCI, including fusion methods based on multiple experimental paradigms (e.g., SSVEP, MI, P300), multi-brain signals (e.g., EEG, fNIRS), and multi-physiological signals (e.g., EOG, EMG), and analyzes their advantages in command capacity, robustness, and real-time performance. Finally, it summarizes the current challenges faced by BCI-based UAV control systems and prospects their future development trends, aiming to provide theoretical references and new insights for related technical research on BCI-based UAV control.