mlsanityData to 14 Oct 2026

Transformer-based EEG Decoding: A Survey

arXiv 2507.02320 ↗First posted 3 Jul 2025cs.LG cs.HCcited by 3

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Abstract (arXiv)

Electroencephalography (EEG) is one of the most common signals used to capture the electrical activity of the brain, and the decoding of EEG, to acquire the user intents, has been at the forefront of brain-computer/machine interfaces (BCIs/BMIs) research. Compared to traditional EEG analysis methods with machine learning, the advent of deep learning approaches have gradually revolutionized the field by providing an end-to-end long-cascaded architecture, which can learn more discriminative features automatically. Among these, Transformer is renowned for its strong handling capability of sequential data by the attention mechanism, and the application of Transformers in various EEG processing tasks is increasingly prevalent. This article delves into a relevant survey, summarizing the latest application of Transformer models in EEG decoding since it appeared. The evolution of the model architecture is followed to sort and organize the related advances, in which we first elucidate the fundamentals of the Transformer that benefits EEG decoding and its direct application. Then, the common hybrid architectures by integrating basic Transformer with other deep learning techniques (convolutional/recurrent/graph/spiking neural netwo-rks, generative adversarial networks, diffusion models, etc.) is overviewed in detail. The research advances of applying the modified intrinsic structures of customized Transformer have also been introduced. Finally, the current challenges and future development prospects in this rapidly evolving field are discussed. This paper aims to help readers gain a clear understanding of the current state of Transformer applications in EEG decoding and to provide valuable insights for future research endeavors.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 55 of its 184 references that are in the map. First posted on arXiv 3 Jul 2025.

Builds on

55 of 184 references.

References · 55

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38 more
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Foundation Models for Cross-Domain EEG Analysis Application: A Surveycited by 4
  2. 2026HCFT: Hierarchical Convolutional Fusion Transformer for EEG Decodingcited by 0
  3. 2026EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priorscited by 0

Similar, not cited

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  5. 2023Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models
  6. 2025Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals
  7. 2024Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces
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