This study evaluates datasets of neural signals and how they can improve the analysis of the generation of visual content in human bodies, and how these experiences can be recreated using cross-domain learning. The created framework mapped brain patterns from the THINGS-EEG visual dataset and the DEAP emotional response dataset to enable three visual abilities: accurate retrieval of content and its classification, lange descriptions using large language models, and reconstruction of images using diffusion models. The results showed that the BrainVision technology was more accurate than single-domain methods, obtaining a 15.3% increase in accuracy of retrieval and 12.7% increase in the similarity of the structures of reconstructed images compared to other state-of-the-art techniques. The framework also had a very strong generalization ability, managing to sustain 82% performance when it was used on new stimuli it had not previously been exposed to. The outputs of the framework helped to create a stronger comprehension of how the vision and visual intent is interpreted in the brain.