SEC-EnD: Stacked Ensemble Correlation-based Feature Selection Method for Emotion Detection

Because the brain signal is so complex, choosing the right features from the EEG signal has become a major area of research and is still important if you want to receive reliable findings. Due to the extremely high dimensionality of EEG data, redundant features and the dimensionality curse are frequently present, and the classification method frequently fails to produce the intended results. We have chosen an optimum feature set for emotion recognition using certain well-known feature selection techniques to address these issues. But most of them are unstable in nature, and for different training subsets, different types of feature selection methods work best. In this research, we provide SEC-END, a stacked ensemble correlation-based feature selection method for emotion detection that can combine and stack different feature selection methods to find the best features. This method has a 3-level feature selection technique, which helps to finetune the selection of features. After integrating the entire feature set, we took the features that each approach had in common and used the union quorum method to eliminate the features that were unnecessary. Then, using four classifiers-Random Forest, Decision Tree, SVM, and KNN-we tested the feature set. Regardless of the training set and classifier, the feature subset is outperforming other state-of-the-art techniques for all the dimensions. The experiment is also carried out with different sizes of windows for EEG signals so that the optimum window size can be recognized and used for further experiments.