Deep feature extraction from EEG signals using xception model for emotion classification

Throughout the years, major advancements have been made in the field of EEG-based emotion classification. Implementing deep architectures for supervised and unsupervised learning from data has come a long way. This study aims to capitalize on these advancements to classify emotions from EEG signals accurately. It still is, however, a challenging task. The fact that the data we are reliant on changes from person to person calls for an elaborate machine-learning solution that can achieve high degrees of abstraction without sacrificing accuracy and legibility. In this study, the Xception model from Keras API was utilized, as well as wavelet transform for feature extraction, which was then used for classification using different classifiers. These features were classified into three distinct categories: NEGATIVE, POSITIVE and NEUTRAL. To examine the effectiveness of the Xception deep neural net, we compare the results of different classifiers like Support Vector Machine, Random Forest, AdaBoostM1, LogitBoost, Naive Bayes Updateable and Non-Nested Generalization Exemplars. The random forest ensemble achieved the best results from all the classifiers implemented in this study. It had higher accuracy scores than existing models without compromising on areas like precision, F1 score, and recall value.