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

• Published in: JMIR medical informatics Vol. 13; p. e72027
• Main Authors: Chen, Chi-Sheng, Chang, Shao-Hsuan, Liu, Che-Wei, Pan, Tung-Ming
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 25.06.2025
• Subjects: Accuracy; Adult; Brain - diagnostic imaging; Brain - physiology; Deep learning; Diffusion models; Electroencephalography; Electroencephalography - methods; Humans; Image Processing, Computer-Assisted - methods; Machine learning; Original Paper; Semantics; Signal Processing, Computer-Assisted; electroencephalography; diffusion models; brain-computer interface; multimodal generative framework; electroencephalography to image

Electroencephalography (EEG) has been widely used to measure brain activity, but its potential to generate accurate images from neural signals remains a challenge. Most EEG-decoding research has focused on tasks such as motor imagery, emotion recognition, and brain wave classification, which involve...