The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
A Review on the Computational Methods for Emotional State Estimation from the Human EEG
Open accessAbstract
A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.
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Cites
Core affect and the psychological construction of emotion.
Russell, J. A. (2003) · Psychological Review · doi:10.1037/0033-295x.110.1.145 · Cited by 6,113 · cited 2×
“Various psychological models define emotional dimensions that subsequently constitute the basis for the emotional state space [25, 27–30].”
BackgroundWhat are emotions? And how can they be measured?
Scherer, K. R. (2005) · Social Science Information · doi:10.1177/0539018405058216 · Cited by 4,264
Neurobiology of emotion perception I: the neural basis of normal emotion perception
Phillips, M. L., Drevets, W. C., Rauch, S. L., & Lane, R. (2003) · Biological Psychiatry · doi:10.1016/s0006-3223(03)00168-9 · Cited by 2,173
Mauss, I. B., & Robinson, M. D. (2009) · Cognition and Emotion · doi:10.1080/02699930802204677 · Cited by 2,078
Belief and feeling: Evidence for an accessibility model of emotional self-report.
Robinson, M. D., & Clore, G. L. (2002) · Psychological Bulletin · doi:10.1037/0033-2909.128.6.934 · Cited by 1,806
Neuronal gamma-band synchronization as a fundamental process in cortical computation
Fries, P. (2009) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135603 · Cited by 1,713
The role of phase synchronization in memory processes
Fell, J., & Axmacher, N. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn2979 · Cited by 1,620
Synchronization of neural activity across cortical areas correlates with conscious perception
Melloni, L., Molina, C., Pena, M., Torres, D., Singer, W., & Rodriguez, E. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.4623-06.2007 · Cited by 716
Neuroimaging Support for Discrete Neural Correlates of Basic Emotions: A Voxel-based Meta-analysis
Vytal, K., & Hamann, S. (2010) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21366 · Cited by 680 · cited 1×
“Functional magnetic resonance imaging (fMRI) has been used to find cortical and subcortical structures implicated in emotional states [13].”
MethodDiscrete Emotions or Dimensions? The Role of Valence Focus and Arousal Focus
Barrett, L. F. (1998) · Cognition & Emotion · doi:10.1080/026999398379574 · Cited by 645 · cited 2×
“In the discrete model, an emotional state is defined as a set of a finite number of discrete states corresponding to one of core emotions, including anger, fear, disgust, surprise, happiness, and sadness, or a combination of them [2].”
Background
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
EEG-Based Emotion Recognition Using Hierarchical Network With Subnetwork Nodes
Yang, Y., Wu, Q. M. J., Zheng, W.-L., & Lu, B.-L. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2685338 · Cited by 141
Emotion assessment using Machine Learning and low-cost wearable devices
Laureanti, R., Bilucaglia, M., Zito, M., Circi, R., Fici, A., Rivetti, F., Valesi, R., Oldrini, C., Mainardi, L. T., & Russo, V. (2020) · 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175221 · Cited by 33
Metadata
- Authors
- Kim, M.-K., Kim, M., Oh, E., & Kim, S.-P.
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2013
- Volume
- 2013
- Pages
- 1-13
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- OpenAlex
- W2004204487
- Cited by
- 293
- References
- 12
- FWCI
- 18.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
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