Emotion recognition in EEG signals using deep learning methods: A review
Article
Jafari, Mahboobeh, Shoeibi, Afshin, Khodatars, Marjane, Bagherzadeh, Sara, Shalbaf, Ahmad, García, David López, Gorriz, Juan M. and Acharya, U. Rajendra. 2023. "Emotion recognition in EEG signals using deep learning methods: A review." Computers in Biology and Medicine. 165. https://doi.org/10.1016/j.compbiomed.2023.107450
| Article Title | Emotion recognition in EEG signals using deep learning methods: A review |
|---|---|
| ERA Journal ID | 5040 |
| Article Category | Article |
| Authors | Jafari, Mahboobeh, Shoeibi, Afshin, Khodatars, Marjane, Bagherzadeh, Sara, Shalbaf, Ahmad, García, David López, Gorriz, Juan M. and Acharya, U. Rajendra |
| Journal Title | Computers in Biology and Medicine |
| Journal Citation | 165 |
| Article Number | 107450 |
| Number of Pages | 31 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0010-4825 |
| 1879-0534 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2023.107450 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482523009150 |
| Abstract | Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, planning, reasoning, and other mental states. As a result, they are considered a significant factor in human interactions. Human emotions can be identified through various sources, such as facial expressions, speech, behavior (gesture/position), or physiological signals. The use of physiological signals can enhance the objectivity and reliability of emotion detection. Compared with peripheral physiological signals, electroencephalogram (EEG) recordings are directly generated by the central nervous system and are closely related to human emotions. EEG signals have the great spatial resolution that facilitates the evaluation of brain functions, making them a popular modality in emotion recognition studies. Emotion recognition using EEG signals presents several challenges, including signal variability due to electrode positioning, individual differences in signal morphology, and lack of a universal standard for EEG signal processing. Moreover, identifying the appropriate features for emotion recognition from EEG data requires further research. Finally, there is a need to develop more robust artificial intelligence (AI) including conventional machine learning (ML) and deep learning (DL) methods to handle the complex and diverse EEG signals associated with emotional states. This paper examines the application of DL techniques in emotion recognition from EEG signals and provides a detailed discussion of relevant articles. The paper explores the significant challenges in emotion recognition using EEG signals, highlights the potential of DL techniques in addressing these challenges, and suggests the scope for future research in emotion recognition using DL techniques. The paper concludes with a summary of its findings. |
| Keywords | Artificial intelligence; Emotion recognition ; Biological signals ; EEG; Deep learning |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | University of Granada, Spain |
| Islamic Azad University, Iran | |
| Shahid Beheshti University of Medical Sciences, Iran | |
| University of Cambridge, United Kingdom | |
| School of Mathematics, Physics and Computing |
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