mlsanityData to 14 Oct 2026

Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey

arXiv 2112.06654 ↗First posted 8 Dec 2021eess.SP cs.HC cs.LGcited by 11

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Abstract (arXiv)

Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity. Traditional EEG decoding methods have achieved moderate success when applied to data acquired in static, well-controlled lab environments. However, an open-world environment is a more realistic setting, where situations affecting EEG recordings can emerge unexpectedly, significantly weakening the robustness of existing methods. In recent years, deep learning (DL) has emerged as a potential solution for such problems due to its superior capacity in feature extraction. It overcomes the limitations of defining `handcrafted' features or features extracted using shallow architectures, but typically requires large amounts of costly, expertly-labelled data - something not always obtainable. Combining DL with domain-specific knowledge may allow for development of robust approaches to decode brain activity even with small-sample data. Although various DL methods have been proposed to tackle some of the challenges in EEG decoding, a systematic tutorial overview, particularly for open-world applications, is currently lacking. This article therefore provides a comprehensive survey of DL methods for open-world EEG decoding, and identifies promising research directions to inspire future studies for EEG decoding in real-world applications.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 32 of its 87 references that are in the map. First posted on arXiv 8 Dec 2021.

Builds on

32 of 87 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 3; 2026: 1.

11 papers.

  1. 2024EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classificationcited by 21
  2. 2024Self-supervised Learning for Electroencephalogram: A Systematic Surveycited by 19
  3. 2023Data Augmentation for Seizure Prediction with Generative Diffusion Modelcited by 14
  4. 2023EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networkscited by 6
  5. 2024Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIscited by 6
  6. 2024PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decodingcited by 3
  7. 2025A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyondcited by 3
  8. 2023DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reusecited by 2
  9. 2025Tyee: A Unified, Modular, and Fully-Integrated Configurable Toolkit for Intelligent Physiological Health Carecited by 1
  10. 2025D4PM: A Dual-branch Driven Denoising Diffusion Probabilistic Model with Joint Posterior Diffusion Sampling for EEG Artifacts Removalcited by 0
  11. 2026Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decodingcited by 0