Machine Learning For Classification Of Antithetical Emotional States
This work addresses emotion classification for EEG analysis, but it is incremental as it builds on existing methods with a focus on computational efficiency.
The paper tackled the problem of emotion classification from EEG signals by evaluating baseline machine learning classifiers and a tabular learning approach on the DEAP dataset, achieving state-of-the-art comparable results without heavy neural networks.
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.