  Emotion Classification through EEG signals has achieved many advancements.
However, the problems like lack of data and learning the important features and
patterns have always been areas with scope for improvement both computationally
and in prediction accuracy. This works analyses the baseline machine learning
classifiers' performance on DEAP Dataset along with a tabular learning approach
that provided state-of-the-art comparable results leveraging the performance
boost due to its deep learning architecture without deploying heavy neural
networks.
