Paper Info
Title
Discriminating Multiple Emotional States From Eeg Using A Data-Adaptive, Multiscale Information-Theoretic Approach
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 < 0.05). 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.
Year
DOI
Venue
2016
10.1142/S0129065716500052
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
Field
DocType
Emotion, complexity, multiscale sample entropy, EMD
Time series,Sample entropy,Computer science,Artificial intelligence,Multivariate analysis,Electroencephalography,CLIPS,Brain asymmetry,Arousal,Pattern recognition,Multivariate statistics,Speech recognition,Machine learning
Journal
Volume
Issue
ISSN
26
2
0129-0657
Citations 
PageRank 
References 
9
0.56
10
Authors
4
Name
Order
Citations
PageRank
Yelena Tonoyan1100.94
David Looney2618.04
Danilo Mandic31641173.32
Marc M. Van Hulle462269.75