Automated Recognition of Human Emotions from EEG Signals Using Signal Processing and Machine Learning Techniques

Abstract

One of the most difficult challenges in pattern recognition, machine learning, and artificial intelligence is emotion recognition. For automatic emotion recognition, voices, images, and electroencephalography (EEG) signals have been employed. Emotion recognition systems based on brain activity are extremely useful in a variety of sectors. In today’s computer age, providing reliable information on emotion recognition is a critical task. Because emotional activity is complicated, it is critical to apply cutting-edge technology and profit from signal processing and machine learning methods while learning about it. Although individuals have been interested in documenting emotional activities over the past decade, there are still fundamental issues that must be addressed in order to take advantage of technology in the understanding of emotion activity. In this chapter, we will go over the most recent signal processing and machine learning algorithms for detecting emotion activity information in the system. We will also cover the difficulties and significant considerations associated with emotion recognition. Several open concepts will be presented for future research to use in understanding the challenges with emotion recognition. Finally, we present some specific examples of emotion recognition using EEG signals employing various AI and signal processing techniques.