The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Toward Emotion Aware Computing: An Integrated Approach Using Multichannel Neurophysiological Recordings and Affective Visual Stimuli
Abstract
This paper proposes a methodology for the robust classification of neurophysiological data into four emotional states collected during passive viewing of emotional evocative pictures selected from the International Affective Picture System. The proposed classification model is formed according to the current neuroscience trends, since it adopts the independency of two emotional dimensions, namely arousal and valence, as dictated by the bidirectional emotion theory, whereas it is gender-specific. A two-step classification procedure is proposed for the discrimination of emotional states between EEG signals evoked by pleasant and unpleasant stimuli, which also vary in their arousal/intensity levels. The first classification level involves the arousal discrimination. The valence discrimination is then performed. The Mahalanobis (MD) distance-based classifier and support vector machines (SVMs) were used for the discrimination of emotions. The achieved overall classification rates were 79.5% and 81.3% for the MD and SVM, respectively, significantly higher than in previous studies. The robust classification of objective emotional measures is the first step toward numerous applications within the sphere of human-computer interaction.
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Cites
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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
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 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
EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM
Yin, Y., Zheng, X., Hu, B., Zhang, Y., & Cui, X. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106954 · Cited by 466
Metadata
- Authors
- Frantzidis, C. A., Bratsas, C., Papadelis, C. L., Konstantinidis, E., Pappas, C., & Bamidis, P. D.
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2010-05
- Volume
- 14
- Issue
- 3
- Pages
- 589-597
- ISSN
- 1089-7771 (ISSN-L)
- OpenAlex
- W2160410052
- Cited by
- 247
- References
- 9
- FWCI
- 9.19
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Frantzidis, C. A., Bratsas, C., Papadelis, C. L., Konstantinidis, E., Pappas, C., & Bamidis, P. D. (2010). Toward Emotion Aware Computing: An Integrated Approach Using Multichannel Neurophysiological Recordings and Affective Visual Stimuli. IEEE Transactions on Information Technology in Biomedicine. https://doi.org/10.1109/titb.2010.2041553
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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