This chapter explores the transformative impact of transformer models on EEG-based motor imagery brain-computer interfaces (BCIs)—systems that are pushing the boundaries of human-machine interaction. Transformers, renowned for their self-attention mechanisms, excel at handling sequential data, making them uniquely suited for decoding intricate EEG patterns. We offer a comprehensive review of transformer applications in BCIs, showcasing how they significantly improve signal interpretation accuracy, efficiency, and robustness. The chapter examines the technical foundations, including the inherent complexities of EEG signals—noise, non-stationarity, and intersubject variability—and how transformers tackle them through superior feature extraction and denoising capabilities. We trace the evolution of these models from traditional machine-learning approaches to sophisticated architectures that capture both temporal and spatial dependencies in EEG data. The chapter then delves into practical applications of these models in real-world BCI systems, discussing how they translate into tangible benefits for users. We explore prospects and ongoing research aimed at overcoming limitations like computational demands and the need for personalized models. By analyzing emerging trends and envisioning future directions, this chapter provides a roadmap for the BCI research community, ultimately leading to more intuitive, versatile, and effective human-computer interactions.
