Classification of human emotion using an EEG-based brain-machine interface: a machine learning approach

Abstract

The main purpose of this work is to investigate the possibility of using electroencephalography (EEG) data to improve machine learning models' ability to accurately identify emotions. The work focuses on emotion classification using EMG data, to improve data mining models. This work investigates the use of individual and ensemble classification methods in the processing of windowed data obtained from four scalp sites. This information is then utilized to calculate the emotions that participants felt at particular times. The results indicate that the use of a low resolution, readily available EEG device can be a useful tool for determining a human's emotional status. The submission of assembling technique increases the accuracy of the model; this highlights the possibility of creating categorization algorithms that may be used in practical decision support systems. Future studies in this field ought to concentrate on determining if the method, attribute creation, attribute selection, or both were responsible for this notable improvement.