EEG-based emotion recognition using a wrapper-based feature selection method

Abstract

Emotions are important part of the daily communication process between people. The need for embed emotion recognition in the human-computer interaction systems became an important issue recently. Researchers addressed the use of internal physiological signals for emotion observation. Electroencephalography (EEG) has a great attention recently and it is now the most used method for observing brain activities. This paper presents a method for EEG-based emotion recognition. Addressing the high dimensionality of the EEG features, recursive feature elimination (RFE) as a wrapper-based feature selection method is used to select the most important features. Then, many classifiers are evaluated to classify emotions using the selected features. The presented method has been tested on a public dataset, and the results demonstrate the robustness of this method and its superiority compared to other studies on the same dataset.