Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

Abstract

Brain-computer interfaces (BCIs) have undergone significant advancements in recent years. The integration of deep learning techniques, specifically transformers, has shown promising development in research and application domains. Transformers, which were originally designed for natural language processing, have now made notable inroads into BCIs, offering a unique self-attention mechanism that adeptly handles the temporal dynamics of brain signals. This comprehensive survey delves into the application of transformers in BCIs, providing readers with a lucid understanding of their foundational principles, inherent advantages, potential challenges, and diverse applications. In addition to discussing the benefits of transformers, we also address their limitations, such as computational overhead, interpretability concerns, and the data-intensive nature of these models, providing a well-rounded analysis. Furthermore, the paper sheds light on the myriad of BCI applications that have benefited from the incorporation of transformers. These applications span from motor imagery decoding, emotion recognition, and sleep stage analysis to novel ventures such as speech reconstruction. This review serves as a holistic guide for researchers and practitioners, offering a panoramic view of the transformative potential of transformers in the BCI landscape. With the inclusion of examples and references, readers will gain a deeper understanding of the topic and its significance in the field.

Keywords

• Deep learning
• brain–computer interfaces
• review
• transformer architecture
• EEG
• emotion recognition