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Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review
Open accessAbstract
Affective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI), emotion recognition has piqued the interest of academics from a variety of domains. Facial expressions, speech, behavior (gesture/posture), and physiological signals can all be used to identify human emotions. However, the first three may be ineffectual because people may hide their true emotions consciously or unconsciously (so-called social masking). Physiological signals can provide more accurate and objective emotion recognition. Electroencephalogram (EEG) signals respond in real time and are more sensitive to changes in affective states than peripheral neurophysiological signals. Thus, EEG signals can reveal important features of emotional states. Recently, several EEG-based BCI emotion recognition techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of what has been accomplished in this area. It also provides an overview of the datasets and methods used to elicit emotional states. According to the usual emotional recognition pathway, we review various EEG feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods (e.g., convolutional and recurrent neural networks with long short term memory). In addition, EEG rhythms that are strongly linked to emotions as well as the relationship between distinct brain areas and emotions are discussed. We also discuss several human emotion recognition studies, published between 2015 and 2021, that use EEG data and compare different machine and deep learning algorithms. Finally, this review suggests several challenges and future research directions in the recognition and classification of human emotional states using EEG.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334 · cited 2×
“Here are a few examples: (a) Russell’s circumplex 2D model, which can include up to 150 affective labels using arousal and valence dimensions [19]; (b) Whissell’s continuous 2D space, with evaluation and activation as dimensions [34]; and (c) Schloberg’s three-dimensional emotion model, which adds…”
MethodPeng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,544
Measuring emotion: The self-assessment manikin and the semantic differential
Bradley, M. M., & Lang, P. J. (1994) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(94)90063-9 · Cited by 9,538
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,947
Independent component analysis: algorithms and applications
Hyvärinen, A., & Oja, E. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00026-5 · Cited by 8,705
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Physiological time-series analysis using approximate entropy and sample entropy
Richman, J. S., & Moorman, J. R. (2000) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2000.278.6.h2039 · Cited by 7,880
Research works printed but not linked: 29
- Abdulkader SN, Atia A, Mostafa M-SM (2015) Brain computer interfacing: applications and challenges. Egypt Inf J 16(2):213–230
- Valery P (1999) Emotion in speech: Recognition and application to call centers. In: Proceedings of artificial neural networks in engineering, volume 710, p 22
- Xiaowei L, Bin H, Tingshao Z, Jingzhi Y, Fang Z (2009) Towards affective learning with an eeg feedback approach. In: Proceedings of the first ACM international workshop on Multimedia technologies for distance learning, pp 33–38
- Silvan T (1962) Affect imagery consciousness: Volume I: The positive affects. Springer publishing company
- Watson JB, Kimble GA (2017) Behaviorism. Routledge, London · doi:10.4324/9781351314329
- Gurney E (1884) What is an emotion? Mind 9(35):421–426 · doi:10.1093/mind/os-ix.35.421
- Frijda NH et al (1986) The emotions. Cambridge University Press, Cambridge
- Izard Carroll E (2013) Human emotions. Springer, New York
- Lang PJ, Bradley MM, Cuthbert BN et al (1997) International affective picture system (iaps): technical manual and affective ratings. NIMH Center Study Emot Attent 1(39–58):3
- Teplan M et al (2002) Fundamentals of eeg measurement. Measure Sci Rev 2(2):1–11
- Asadi Ghanbari A, Nazari Kousarrizi MR, Teshnehlab M, Aliyari M (2009) An evolutionary artifact rejection method for brain computer interface using ica. Int J Electr Comput Sci 9(9):48–53
- Houssein EH, Hamad A, Hassanien AE, Fahmy AA (2019) Epileptic detection based on whale optimization enhanced support vector machine. J Inf Optim Sci 40(3):699–723
- Langley P et al (1994) Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall symposium on relevance 184:245–271
- Fister Jr I, Yang X-S, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186 · arXiv
- Miranda Correa JA, Khomami Abadi M, Sebe N, Patras I (2018) Amigos: a dataset for affect, personality and mood research on individuals and groups. IEEE Trans Affect Comput
- Kumar S, Singh S, Kumar J (2019) Multiple face detection using hybrid features with svm classifier. In: Data and communication networks. Springer, pp 253–265 · doi:10.1007/978-981-13-2254-9_23
- Shi L, Wang X, Shen Y (2020) Research on 3d face recognition method based on lbp and svm. Optik 220:165157 · doi:10.1016/j.ijleo.2020.165157
- Asuntha A, Brindha A, Indirani S, Srinivasan A (2016) Lung cancer detection using svm algorithm and optimization techniques. J Chem Pharm Sci 9(4):3198–3203
- Fausett LV (2006) Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India
- Sharma S, Sharma S (2017) Activation functions in neural networks. Towards Data Science 6(12):310–316
- Quinlan JR (2014) C4. 5: programs for machine learning. Elsevier
- Loh W-Y (2011) Classification and regression trees. Wiley Interdiscip Rev: Data Min Knowl Discov 1(1):14–23
- Rafik Khairul Amin, Yuliant Sibaroni, et al (2015) Implementation of decision tree using c4. 5 algorithm in decision making of loan application by debtor (case study: Bank pasar of yogyakarta special region). In 2015 3rd International Conference on Information and Communication Technology (ICoICT), pp 75–80. IEE · doi:10.1109/icoict.2015.7231400
- Sasirekha K, Thangavel K (2019) Optimization of k-nearest neighbor using particle swarm optimization for face recognition. Neural Comput Appl 31(11):7935–7944 · doi:10.1007/s00521-018-3624-9
- Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, Léon Bottou (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11(12)
- Chamara L, Zhou H, Huang GB, Vong CM (2013) Representational learning with extreme learning machine for big data. IEEE Intell Syst 28(6):31–34
- Chen M, Weinberger KQ, Zhixiang X, Sha F (2015) Marginalizing stacked linear denoising autoencoders. The Journal of Machine Learning Research 16(1):3849–3875
- Guo J (2013) Backpropagation through time. Unpubl. ms. Harbin Institute of Technology 40:1–6
- Alhagry S, Fahmy AA, El-Khoribi RA (2017) Emotion recognition based on eeg using lstm recurrent neural network. Emotion 8(10):355–358
Cited by
Emotion recognition in EEG signals using deep learning methods: A review
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
Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126866 · Cited by 142
Erdem Güler, S., & Patlar Akbulut, F. (2025) · IEEE Access · doi:10.1109/access.2025.3538642 · Cited by 21
State-of-the-art Multimodal Emotion Recognition: A comprehensive survey and taxonomy
Yazici, A., Kucukyilmaz, T., Dokeroglu, T., Sharipbay, A., Lee, M.-H., & Tyler, B. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200642 · Cited by 11
Moussaoui, K., & Farah, M. (2024) · 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP) · doi:10.1109/atsip62566.2024.10638977 · Cited by 2
Metadata
- Authors
- Houssein, E. H., Hammad, A., & Ali, A. A.
- Venue
- Neural Computing and Applications
- Published
- 2022-05-07
- Volume
- 34
- Issue
- 15
- Pages
- 12527-12557
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W4229024336
- Cited by
- 426
- References
- 166
- FWCI
- 73.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Funders
- Minia University
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