Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals
Published in IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), 2022
Recommended citation: L. I. Mampitiya, R. Nalmi and N. Rathnayake, "Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals," 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), Atlanta, GA, USA, 2021, pp. 71-77, doi: 10.1109/CogMI52975.2021.00018. https://ieeexplore.ieee.org/abstract/document/9750281
Abstract
This study is based on EEG brain wave classification of a well-known dataset called the EEG Brainwave Dataset. The dataset combines three classes such as positive, negative, and neutral. The classification is performed using an ensemble classifier that combines RF, KNN, DT, SVM, NB, and LR. The meta classifier is LR, while the other five algorithms work as the base classifiers. Furthermore, PCA is used as the dimension reduction method to increase the accuracy of the final output. The results are evaluated under 11 different parameters. Moreover, the accuracy of this study is compared with the seven other EEG emotion classification methods. The proposing method attained 99.25% of accuracy, outperforming the other state-of-the-art algorithms.
Recommended citation: L. I. Mampitiya, R. Nalmi and N. Rathnayake, “Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals,” 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), Atlanta, GA, USA, 2021, pp. 71-77, doi: 10.1109/CogMI52975.2021.00018.