Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques

Article


Islam, M. R., Moni, M. A., Islam, M. M., Rashed-Al-Mahfuz, M., Islam, M. S., Hasan, M. K., Hossain, M. S., Ahmad, M., Uddin, S., Azad, A., Alyami, S. A., Ahad, M. A. R. and Lió, P. 2021. Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques. IEEE Access. 9, pp. 94601-94624. https://doi.org/10.1109/ACCESS.2021.3091487
AuthorsIslam, M. R., Moni, M. A., Islam, M. M., Rashed-Al-Mahfuz, M., Islam, M. S., Hasan, M. K., Hossain, M. S., Ahmad, M., Uddin, S., Azad, A., Alyami, S. A., Ahad, M. A. R. and Lió, P.
Abstract

Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-of-the-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions.

KeywordsEmotion; EEG; Deep learning, AI
JournalIEEE Access
Journal citation9, pp. 94601-94624
ISSN2169-3536
Year2021
PublisherIEEE
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2021.3091487
Publication dates
Online22 Jun 2021
Publication process dates
Accepted14 Jun 2021
Deposited04 Dec 2023
Copyright holder© 2021, The Authors
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