Brain-Computer Interface for Emotion Recognition Based on Electroencephalography

Abstract

Human emotions are erratic, and so are their brain signals throughout the day. Brain-computer interface devices must handle high signal throughputs in the form of electroencephalography from different areas of the human brain for us to learn more about these emotions. Researchers can recognize emotions from these signals after data acquisition, preprocessing, feature extraction, and feature selection to the classifier. Efficient machine learning algorithms for the process are imperative to quickly provide emotional feedback to a device. Models of such devices are already applicable in medical fields, gaming, and more. In the meantime, ethical considerations arise. Most people are concerned about their privacy infringements. This paper aims to assist researchers in swiftly comprehending the basic theory of emotion recognition while also offering insights into the future development of this highly interdisciplinary technology. Furthermore, it underscores the need for a delicate equilibrium between technological progress and human values.