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Numerous methods have been developed to gain reliable real-time remote control over pilotless flying aircraft and to perform teleoperation. Recently, state-of-the-art brain-computer interface (BCI) research has provided an avant-garde approach to reach this goal. Due to its broad range of application, BCI has been the center of attention as a promising candidate for deciphering brain signals into corresponding control commands for various systems. This paper surveys the application of BCI in designing control modules for unmanned aerial vehicles (UAVs). We first describe the basic configuration of UAVs, as well as identify the principle components of their control systems. We proceeded to describe different classes of BCI with emphasis on their applicability in controlling UAVs, and highlight the potential benefits and challenges in implementing each BCI paradigm. Details will be given on how essential strategies and key techniques regarding feature extraction, and the classification of data, as well as hybrid-modality, could be applied in this field to develop robust systems demonstrating optimal fidelity and performance. Moreover, by reviewing the primary trends in previous studies, we attempt to address the missing steps in current research and shed light on the road map for future innovation.