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Deep Learning for EEG-Based Visual Classification and Reconstruction: Panorama, Trends, Challenges and Opportunities

🔥INFO

Blog: 2025/07/17 by IgniSavium

  • Title: Deep Learning for EEG-Based Visual Classification and Reconstruction: Panorama, Trends, Challenges and Opportunities
  • Authors: Wei Li, Penglu Zhao, Cheng Xu, Yingting Hou, and Aiguo Song ( Southeast University)
  • Published: May 2025
  • Comment: IEEE Transactions on Biomedical Engineering
  • URL: https://ieeexplore.ieee.org/document/10993346

🥜TLDR: This paper provides the first review on EEG-based visual classification and reconstruction, comprehensively summarizing the representative deep learning methods from both feature encoding and decoding perspectives.

Comparison with previous surveys

This review highlights EEG-based visual classification and reconstruction using deep learning, distinguishing itself from prior works in several ways:

  • Robinson et al. focus on theoretical neural decoding, especially cognitive processes, but do not cover practical applications like image reconstruction.
  • Wang et al. center on GANs for EEG signal processing tasks like data augmentation and artifact removal (method-oriented perspective).
  • Rakhimberdina et al. explore deep learning for fMRI-based image reconstruction. However, EEG's high temporal resolution and low spatial fidelity pose different challenges.
  • Huang et al. focus on fMRI-based visual decoding, whereas EEG faces more severe domain gap issues, which are a central concern of this review due to its temporal resolution and data variability.
  • Nestor et al. discuss face image reconstruction from fMRI and behavioral data.

Overall, this review fills gaps by addressing EEG-specific challenges and offering a broader, task-focused perspective compared to existing surveys.

Methods Summary

Generally utilize an Encoder-Decoder architecture.

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