Integration of electroencephalography (EEG) in Industry 4.0 use cases is a rapidly emerging concept that promises to overcome the data-centric limitations of conventional solutions and introduce the notion of hybrid solutions that can efficiently combine the consistency of automated deep learning approaches and the flexibility of human intuition. Color classification tasks are common in industrial use cases, especially in the field of manufacturing, and are important in regard to EEG responses as well. Responses to color stimuli can be markedly distinguished, and this poses the scope of developing an EEG-based color classifier that would be able to augment the performance of conventional Industry 4.0 solutions. This article presents a detailed study of the background behind color perception in the brain and use a neuroscience-guided methodology to develop an efficient processing pipeline that delivers 100% accuracy while using hardware with a limited sampling rate and channels while accounting for inter-subject and inter-trial variance.