EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

By Nazmi Sofian Suhaimi, James Mountstephens, Jason Teo. Computational Intelligence and Neuroscience, 2020

321 citations recorded by the source corpus.

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful “emotional” interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/8106d1bd47ca72c714b18405374cdb2943b0decd

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. Bin Hu
2. Prayag Tiwari
3. Yazhou Zhang
4. Meihong Yang
5. Xiang Li
6. Zhigang Zhao
7. Pekka Marttinen
8. Neeraj Kumar

In Experimental and Cognitive Psychology

1. Bin Hu
2. Prayag Tiwari
3. Yazhou Zhang
4. Meihong Yang
5. Xiang Li
6. Zhigang Zhao
7. Pekka Marttinen
8. Neeraj Kumar

Citations in the corpus

• Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI (2024, background): “…2020), acute stress detection (Sharma et al., 2022), sleep stage classification (Aboalayon et al., 2016), motor imagery recognition (Amin et al., 2019), abnormal identification (Roy et al., 2019), emotion analysis (Suhaimi et al., 2020), and auditory attention detection (Biesmans et al., 2016).”
• EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[26] used the dynamic graph convolution neural network (DGCNN) to model the multichannel EEG features and they also presented a novel attention-long”