This systematic review examines the current state of consumer-grade EEG-controlled directional games, focusing on motor imagery (MI) techniques. We analyze peer-reviewed studies to identify prevalent signal processing methods, classification algorithms, performance metrics, and game design approaches. Our findings reveal that Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) remain dominant for feature extraction and classification, respectively, due to their computational efficiency in real-time applications. The Unity game engine emerges as the preferred development platform, and evaluation metrics show a strong bias toward quantitative measures. Key challenges include inherent system latency (1–3.5 s), limitation on game design, and a lack of standardized evaluation frameworks. The review highlights critical gaps in current research, particularly the need for enhanced entertainment value alongside technical optimization, and provides practical guidelines for developing EEG-controlled games. These insights aim to facilitate the transition from experimental systems to engaging, consumer-ready applications while establishing directions for future research in this evolving interdisciplinary field.