earticle

영어

Recent advances in generative modeling have sparked growing interest in image generation from electroencephalography (EEG) signals. A critical yet technically challenging component of this task lies in effectively encoding EEG signals to capture semantic information corresponding to visual stimuli. In this preliminary study, we investigate the feasibility of employing CLIP (Contrastive Language–Image Pre-training), a state-of-the-art pretrained multimodal contrastive learning model, to semantically align EEG representations with image and caption feature vectors. Our analysis explores the potential of CLIP-based EEG encoding as a foundation for brain-to-image generation systems.