Discriminating multiple emotional states from EEG using a data-adaptive, multiscale information-theoretic approach
File(s)
Accepted version
Author(s)
Tonoyan, Y
Looney, D
Mandic, DP
Van Hulle, MM
Type
Journal Article
Abstract
A multivariate sample entropy metric of signal complexity is applied to EEG data recorded when subjects were viewing four prior-labeled emotion-inducing video clips from a publically available, validated database. Besides emotion category labels, the video clips also came with arousal scores. Our subjects were also asked to provide their own emotion labels. In total 30 subjects with age range 19–70 years participated in our study. Rather than relying on predefined frequency bands, we estimate multivariate sample entropy over multiple data-driven scales using the multivariate empirical mode decomposition (MEMD) technique and show that in this way we can discriminate between five self-reported emotions (p<0.05p<0.05). These results could not be obtained by analyzing the relation between arousal scores and video clips, signal complexity and arousal scores, and self-reported emotions and traditional power spectral densities and their hemispheric asymmetries in the theta, alpha, beta, and gamma frequency bands. This shows that multivariate, multiscale sample entropy is a promising technique to discriminate multiple emotional states from EEG recordings.
Date Issued
2016-01-28
Date Acceptance
2015-11-27
Citation
International Journal of Neural Systems, 2016, 26 (2)
ISSN
1793-6462
Publisher
World Scientific Publishing
Journal / Book Title
International Journal of Neural Systems
Volume
26
Issue
2
Copyright Statement
© 2016 World Scientific Publishing Company. Electronic version of an article published as Yelena Tonoyan et al, Int. J. Neur. Syst. 26, 1650005 (2016) [15 pages] DOI: http://dx.doi.org/10.1142/S0129065716500052 available at https://dx.doi.org/10.1142/S0129065716500052
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000371121500002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Emotion
complexity
multiscale sample entropy
EMD
FUZZY SYNCHRONIZATION LIKELIHOOD
EVENT-RELATED SYNCHRONIZATION
TIME-SERIES ANALYSIS
CEREBRAL ASYMMETRY
SPECTRUM DISORDER
FREQUENCY BANDS
HUMAN BRAIN
COMPLEXITY
DIAGNOSIS
THETA
Artificial Intelligence & Image Processing
0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Publication Status
Published
Article Number
ARTN 1650005