EEG Based Emotion Recognition: A Tutorial and Review
Open accessAbstract
Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions.
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Emotions cannot be reliably captured with unimodal signals because genuine emotion can be hidden by different facial expressions or tones.
“emotions are considered a complex representation that cannot be reliably captured with unimodal signals, since genuine emotion can be hidden by different facial expressions or tones [11].”
From Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG (2024) · cites this paper as [11]
Section 5.3 says people may conceal feelings behind social masks and that a single modality may be insufficient for accurate recognition; the introduction (1:2) says deliberate concealment can affect facial- or speech-based methods. The claim overstates this as a categorical inability to capture emotion reliably with any unimodal signal, whereas the review discusses limitations that may arise. (LLM, conf=0.86)
Experimental — accuracy not yet measuredEEG signals extracted from the central nervous system can more accurately and objectively reflect changes in people's emotions than other signals.
“EEG signals extracted from the central nervous system can more accurately and objectively reflect changes in people's emotions than other signals [2], [11].”
From Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG (2024) · cites this paper as [11]
Section 2.2 says EEG records central nervous system activity and is an effective measure for studying emotion. The introduction (p. 1:2) notes that facial and speech cues can be affected when people conceal their feelings, but it does not establish that EEG reflects emotional changes more accurately and objectively than other signals generally. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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Jafari, M., Shoeibi, A., Khodatars, M., Bagherzadeh, S., Shalbaf, A., García, D. L., Gorriz, J. M., & Acharya, U. R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107450 · Cited by 240 · see citing works
Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG
Pan, J., Fang, W., Zhang, Z., Chen, B., Zhang, Z., & Wang, S. (2024) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2023.3240280 · Cited by 121 · see citing works
EEG-based emotion recognition with autoencoder feature fusion and MSC-TimesNet model
Yin, J., Qiao, Z., Han, L., & Zhang, X. (2026) · Computer Methods in Biomechanics and Biomedical Engineering · doi:10.1080/10255842.2025.2477801 · Cited by 41 · see citing works
- Authors
- Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P.
- Affiliations
- Qilu University of Technology (CN)
- Zhengzhou University of Light Industry (CN)
- Aalto University (FI)
- Beijing Institute of Technology (CN)
- Thapar Institute of Engineering & Technology (IN)
- ORCID
- Venue
- ACM Computing Surveys
- Published
- 2022-11-21
- Volume
- 55
- Issue
- 4
- Pages
- 1-57
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4220866631
- Cited by
- 508
- References
- 225
- Citations by year
- 2026: 93 · 2025: 191 · 2024: 138 · 2023: 71 · 2022: 15
- FWCI
- 95.20
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Computing Surveys (publishedVersion)
- Aaltodoc (Aalto University) (submittedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Major Science and Technology Innovation Projects of Key R&D Programs of Shandong Province (2019JZZY010108, 2019JZZY010113)
- Natural Science Foundation of China (U1636203, and 62006212)
- Natural Science Foundation of Shandong Province
- Research on Cross-domain Emotion Recognition Based on Large-scale Pre-trained EEG Model
- State Key Lab. for Novel Software Technology in Nanjing University (KFKT2021B41)
- Industrial Science and Technology Research Project of Henan Province (222102210031)
- Academy of Finland (336033, and 315896)
- Business Finland (884/31/2018)
- EU H2020 (101016775)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- EEG-based emotion recognition, EEG, emotion recognition, human-computer interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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