The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers
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Cites
Petrantonakis, P. C., & Hadjileontiadis, L. J. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.7 · Cited by 260
Research works printed but not linked: 8
- AlMejrad, 2010, Human emotions detection using brain wave signals: a challenging, European Journal of Scientific Research, 44, 640
- Brunner, 2011, A comparison of univariate, vector, bilinear autoregressive, and band power features for brain–computer interfaces, Medical & Biological Engineering & Computing, 49, 1337, 10.1007/s11517-011-0828-x · DOI
- Calvo, 2010, Affect detection: an interdisciplinary review of models, methods, and their applications, IEEE Transactions on Affective Computing, 1, 18, 10.1109/T-AFFC.2010.1 · DOI · Paper page
- Giakoumis, 2011, Automatic recognition of boredom in video games using novel biosignal moment-based features, IEEE Transactions on Affective Computing, 2, 119, 10.1109/T-AFFC.2011.4 · DOI
- Koelstra, 2012, Deap: a database for emotion analysis; using physiological signals, IEEE Transactions on Affective Computing, 3, 18, 10.1109/T-AFFC.2011.15 · DOI · Paper page
- Wang, 2011, GA-SVM based feature selection and parameters optimization for BCI research, vol. 1, 580
- Wu, 2010, Optimal arousal identification and classification for affective computing using physiological signals: virtual reality stroop task, IEEE Transactions on Affective Computing, 1, 109, 10.1109/T-AFFC.2010.12 · DOI
- Zhang, 2008, Feature extraction of eeg signals using power spectral entropy, vol. 2, 435
Cited by
Emotions Recognition Using EEG Signals: A Survey
Alarcao, S. M., & Fonseca, M. J. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2714671 · Cited by 1,201
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.011 · Cited by 806
A systematic review on affective computing: emotion models, databases, and recent advances
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.009 · Cited by 615
EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508
Houssein, E. H., Hammad, A., & Ali, A. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07292-4 · Cited by 426
Metadata
- Authors
- Atkinson, J., & Campos, D.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 47
- Pages
- 35-41
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2176865601
- Cited by
- 506
- References
- 1
- FWCI
- 31.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- FONDECYT (1130035)
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- Atkinson, J., & Campos, D. (2016). Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2015.10.049
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
