用注意力机制提升脑机接口的信号解析能力，融合多模态数据更精准捕捉大脑活动。
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces
- 结合卷积、循环网络与自注意力，提取脑电信号时空特征。
- 在多模态融合中显著提升模型对复杂信号的表征能力。
- 适合脑机接口、神经工程领域研究者参考应用。
随着深度学习快速发展，注意力机制已成为脑电图（EEG）信号分析中的关键组件，显著提升了脑机接口（BCI）的应用性能。本文系统综述了传统与基于Transformer的注意力机制、嵌入策略及其在基于EEG的BCI中的应用，尤其关注多模态数据融合。通过捕捉时间、频率和空间通道上的脑电变化，注意力机制增强了特征提取、表征学习与模型鲁棒性。方法可分为两类：传统注意力机制常与卷积或循环网络结合；而基于Transformer的多头自注意力则在建模长程依赖方面表现优异。此外，注意力机制还推动了多模态EEG应用，有效实现脑电与其他生理或感官数据的融合。最后，本文讨论了当前挑战与新兴趋势，为推进脑机接口技术指明未来方向。本综述旨在为希望利用注意力机制提升脑电信号解读与应用的研究者提供重要参考。
原文摘要 · Abstract (English)
With the rapid advancement of deep learning, attention mechanisms have become indispensable in electroencephalography (EEG) signal analysis, significantly enhancing Brain-Computer Interface (BCI) applications. This paper presents a comprehensive review of traditional and Transformer-based attention mechanisms, their embedding strategies, and their applications in EEG-based BCI, with a particular emphasis on multimodal data fusion. By capturing EEG variations across time, frequency, and spatial channels, attention mechanisms improve feature extraction, representation learning, and model robustness. These methods can be broadly categorized into traditional attention mechanisms, which typically integrate with convolutional and recurrent networks, and Transformer-based multi-head self-attention, which excels in capturing long-range dependencies. Beyond single-modality analysis, attention mechanisms also enhance multimodal EEG applications, facilitating effective fusion between EEG and other physiological or sensory data. Finally, we discuss existing challenges and emerging trends in attention-based EEG modeling, highlighting future directions for advancing BCI technology. This review aims to provide valuable insights for researchers seeking to leverage attention mechanisms for improved EEG interpretation and application.
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