The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Feature Extraction and Selection for Emotion Recognition from EEG
Open accessAbstract
Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.
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EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
Zheng, W.-L., Liu, W., Lu, Y., Lu, B.-L., & Cichocki, A. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2797176 · Cited by 1,276
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
Identifying Stable Patterns over Time for Emotion Recognition from EEG
Zheng, W.-L., Zhu, J.-Y., & Lu, B.-L. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2712143 · Cited by 990
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
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
Metadata
- Authors
- Jenke, R., Peer, A., & Buss, M.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2014-07-01
- Volume
- 5
- Issue
- 3
- Pages
- 327-339
- ISSN
- 1949-3045 (ISSN-L)
- OpenAlex
- W2081420711
- Cited by
- 1,081
- References
- 59
- FWCI
- 51.01
- Type
- journal article
- Language
- en
- Open access
- green
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