mlsanityData to 14 Oct 2026

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

arXiv 2502.19281 ↗First posted 26 Feb 2025eess.SP cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 38 of its 107 references that are in the map. First posted on arXiv 26 Feb 2025.

Builds on

38 of 107 references.

References · 38

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26 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decodingcited by 2
  2. 2026iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integrationcited by 0