Emotions Recognition Using EEG Signals: A Survey

By Soraia M. Alarcão, Manuel José Fonseca. IEEE Transactions on Affective Computing, 2019

1,020 citations recorded by the source corpus.

Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/baedd244794e35e11712ba2241f64b9eec27e7e3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Dongrui Wu
2. Wei-Long Zheng
3. Wei Liu
4. Chunyan Miao
5. Di Wang
6. Peixiang Zhong
7. Zhen Cui
8. Wenming Zheng

In Experimental and Cognitive Psychology

1. Dongrui Wu
2. Wei-Long Zheng
3. Wei Liu
4. Chunyan Miao
5. Di Wang
6. Peixiang Zhong
7. Zhen Cui
8. Wenming Zheng

Citations in the corpus

• EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, background): “rapid development of noninvasive, easy-to-use and inexpensive EEG recording devices, EEG-based emotion recognition has received an increasing amount of attention in both research [1] and applications [2].”
• EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention (2023, background): “Generally, most EEG-based emotion recognition studies have focused on first extracting relevant features, and then the extracted features are used to classify the subject’s emotional state [8], [14], [25].”
• EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[24] Soraia M Alarcao and Manuel J Fonseca.”
• EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities (2020, methodology): “Since the last comprehensive review for emotion recognition was published by Alarcao and Fonseca [22], this review paper will serve as an update on the previously reviewed paper.”
• A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition (2019, methodology): “These observations are consistent with the cognition observation in biological psychology [48], [49], namely, the frontal and temporal lobes are the main areas of the brain correlated with emotional activity [8].”
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions (2019, background): “Recently, Alarcao and Fonseca [26] presented a detailed survey about emotion recognition using EEG signals, including stimuli, feature extraction, and classifiers.”
• Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition (2020, methodology): “For the negative emotion of the target, the main Beta and Gamma activations emerge in the parietal region [6], however, the main activations of the source are in the left frontal.”
• EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, methodology): “To deal with EEG emotion classification problem, there are many methods appeared in the literatures [33], among which the method of using deep neural networks (DNN) [19] had been demonstrated to be one of the most successful one.”
• Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, positive): “Recently, there has been a fast-growing research interest in aBCIs [34]–[37].”
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “Alarcão and Fonseca [29] also reviewed works about EEG-based emotion recognition from 2009 to 2016, but focused on subjects, feature representation, classification, and their performance.”