  While capable of segregating visual data, humans take time to examine a
single piece, let alone thousands or millions of samples. The deep learning
models efficiently process sizeable information with the help of modern-day
computing. However, their questionable decision-making process has raised
considerable concerns. Recent studies have identified a new approach to extract
image features from EEG signals and combine them with standard image features.
These approaches make deep learning models more interpretable and also enables
faster converging of models with fewer samples. Inspired by recent studies, we
developed an efficient way of encoding EEG signals as images to facilitate a
more subtle understanding of brain signals with deep learning models. Using two
variations in such encoding methods, we classified the encoded EEG signals
corresponding to 39 image classes with a benchmark accuracy of 70% on the
layered dataset of six subjects, which is significantly higher than the
existing work. Our image classification approach with combined EEG features
achieved an accuracy of 82% compared to the slightly better accuracy of a pure
deep learning approach; nevertheless, it demonstrates the viability of the
theory.
