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This paper is in the following e-collection/theme issue:

AI Language Models in Health Care (802) Imaging Informatics (493) Artificial Intelligence (4936) Medical imaging (200) Advancements in Neuroimaging (28) AI in Neurotechnology (26)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72027, first published .
Man with EEG cap experiences dreamscape visualization in neuroscience research.

Exploring the Potential of Electroencephalography Signal–Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis

Exploring the Potential of Electroencephalography Signal–Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis

Authors of this article:

Chi-Sheng Chen1 Author Orcid Image ;   Shao-Hsuan Chang1, 2 Author Orcid Image ;   Che-Wei Liu3, 4 Author Orcid Image ;   Tung-Ming Pan5, 6 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. An Y, Tong Y, Wang W, Su S. Enhancing Brain Signal Generation Through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models. IEEE Transactions on Medical Imaging 2026;45(8):4254 View

Conference Proceedings

  1. Zhao X, Hao Y, Wen X, Zhou M, Bian J, Cao R. Proceedings of the 2026 International Conference on Multimedia Retrieval. NIGCL: Neuro-Image Geometric Contrastive Learning for Robust EEG-Based Visual Retrieval View
  2. R L, Sasidharan D, Veetil I. 2026 International Conference on Emerging Technologies in Computing and Communication (ETCC). Spatiotemporal Feature Extraction via Signal-to-Image Transformation of EEG–fNIRS Signals for Multimodal Cognitive State Classification View