This paper comprehensively reviews the attention mechanism of electroencephalography (EEG) signal analysis, which has become an essential element in brain-computer interface (BCI) applications with the development of deep learning. We cover EEG-based BCI applications, focusing on conventional attention mechanisms, Transformer-based attention mechanisms, embedding strategies, and multimodal data fusion in particular. By capturing EEG changes in time, frequency, and spatial channels, we enhance feature extraction, representation learning, and model robustness. Conventional attention mechanisms are usually integrated with convolutional and recurrent neural networks, and Transformer-based multihead self-attention excels in capturing long-range dependencies. Beyond single-modal analysis, attention mechanisms enhance multimodal EEG applications, enabling effective fusion between EEG and other physiological or sensory data. Finally, we discuss existing challenges and emerging trends in attention-based EEG modeling, and suggest future directions for the development of BCI technology.
