Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces

TL;DR

This paper reviews how attention mechanisms, especially Transformer-based models, enhance EEG analysis in brain-computer interfaces by improving feature extraction, robustness, and multimodal data fusion.

Contribution

It provides a comprehensive overview of traditional and Transformer-based attention methods in EEG-based BCI, highlighting their applications and future challenges.

Findings

Attention mechanisms improve EEG feature extraction and robustness.

Transformer models capture long-range dependencies in EEG data.

Attention enhances multimodal EEG data fusion.

Abstract

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.