From thought to visual composition: a brain-driven visual blends technique for visual blending tasks

Visual blends is a design technique that combines elements from multiple images into harmonious compositions and has been increasingly explored as a means to support early-stage ideation in engineering design. However, existing blending workflows rely heavily on manual image selection and composition, making the process difficult, time-consuming, and skill-intensive for designers. In this work, we present a proof-of-concept brain-guided visual blends technique that integrates an EEG-to-image model to simplify the image acquisition process and a local image editing model to enable automated and controllable image composition. Our EEG-to-image model employs a two-stage training strategy, combining pretraining on large-scale unlabelled EEG data with fine-tuning in an EEG-conditioned diffusion model, achieving state-of-the-art performance in reconstructing visual stimuli. To support visual blending tasks, we incorporate a local editing model (Paint-by-Example) that generates coherent blends using user-provided masks, reference images, and backgrounds. A user study with 15 participants demonstrated that the model effectively supported the creation of visual blends that aligned with users' design vision, even without artistic skills. The results suggest that brain-guided blending can serve as a early-stage ideation interface in engineering design, helping designers iterate on mental concepts before formal modelling and evaluation.