Annals of Engineering, Imperial Open Publishing (2020)
ABSTRACT Emotion recognition based on electroencephalogram has been a lucrative task nowadays. Different procedures are executed to improve the computational power of emotion recognition frameworks utilizing electroencephalogram (EEG). There are several computational strategies that have performed incredibly well in emotion recognition task. An automated recognition system is typically limited to few emotions. So more effective and accurate emotion classification is the primary objective for Artificial Intelligence researchers. This paper aims to classify three emotion recognition conditions, such as positive, negative, and neutral. To reduce the over-fitting dispute, principal component analysis (PCA) is applied to extricate the most significant parts of input features. Besides, covariate move adjustment of the essential parts is executed to limit the non-stationary impact of EEG signals. Several machine learning models have been deployed to classify this problem. In this case, traditional algorithms such as logistic regression, linear support vector machine, random forest, artificial neural network, long short term memory (LSTM) and extreme gradient boosted (XGBoost) classifier have been enforced on the EEG brainwave signal dataset. The XGBoost has performed outstandingly in terms of accuracy and less time complexity.
