The DOI matches its Crossref record; 272 of the 272 references it cites were found in a registry or an index.
Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review
Open accessAbstract
Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants’ emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.
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DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093
Zheng, W.-L., & Lu, B.-L. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2431497 · Cited by 2,595
A Multimodal Database for Affect Recognition and Implicit Tagging
Soleymani, M., Lichtenauer, J., Pun, T., & Pantic, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.25 · Cited by 1,664
Feature Extraction and Selection for Emotion Recognition from EEG
Jenke, R., Peer, A., & Buss, M. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2339834 · Cited by 1,081
Emotion and attention: event-related brain potential studies
Schupp, H. T., Flaisch, T., Stockburger, J., & Junghöfer, M. (2006) · Progress in Brain Research · doi:10.1016/s0079-6123(06)56002-9 · Cited by 1,029
EEG-Based Emotion Recognition in Music Listening
Lin, Y.-P., Wang, C.-H., Jung, T.-P., Wu, T.-L., Jeng, S.-K., Duann, J.-R., & Chen, J.-H. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2048568 · Cited by 1,021
Motivation, emotion, and their inhibitory control mirrored in brain oscillations
Knyazev, G. G. (2007) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2006.10.004 · Cited by 969
Emotional state classification from EEG data using machine learning approach
Wang, X.-W., Nie, D., & Lu, B.-L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.06.046 · Cited by 755
Multimodal Emotion Recognition in Response to Videos
Soleymani, M., Pantic, M., & Pun, T. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.37 · Cited by 722
Emotion Recognition From EEG Using Higher Order Crossings
Petrantonakis, P. C., & Hadjileontiadis, L. J. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2034649 · Cited by 687
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Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016
Wu, D., Xu, Y., & Lu, B.-L. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3007453 · Cited by 434
Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives
Spezialetti, M., Placidi, G., & Rossi, S. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.532279 · Cited by 293
Techniques and countermeasures for preventing insider threats
Alsowail, R. A., & Al-Shehari, T. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.938 · Cited by 45
Izhar, L. I., Babiker, A., Rizki, E. E., Lu, C.-K., & Abdul Rahman, M. (2022) · Sensors · doi:10.3390/s22072703 · Cited by 21
Metadata
- Authors
- Al-Nafjan, A., Hosny, M., Al-Ohali, Y., & Al-Wabil, A.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2017-12-01
- Volume
- 7
- Issue
- 12
- Pages
- 1239
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W2775675437
- Cited by
- 300
- References
- 272
- FWCI
- 3.27
- Type
- journal article
- Language
- en
- Open access
- gold
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