This paper focuses on analyzing electroencephalogram (EEG) signals generated by the brain when visualizing images. The study enhances prior research by customizing classifier parameters for each individual user. Initially, a deep learning framework was developed to extract meaningful patterns from raw EEG data and predict images among forty distinct categories from the ImageNet dataset. The central purpose is to adapt this model for new users, enabling the creation of personalized models with minimal additional data.