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Title: Discriminating multiple emotional states from EEG using a data-adaptive, multiscale information-theoretic approach
Authors: Tonoyan, Yelena
Looney, D
Mandic, DP #
Van Hulle, Marc # ×
Issue Date: Mar-2016
Publisher: World Scientific
Series Title: International Journal of Neural Systems vol:26 issue:2
Article number: doi: 10.1142/S0129065716500052
Abstract: A multivariate sample entropy metric of signal complexity is applied to EEG data recorded when subjects were viewing 4 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 technique (MEMD) and show that in this way we can discriminate between 5 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.
ISSN: 0129-0657
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Laboratory for Neuro- and Psychofysiology
× corresponding author
# (joint) last author

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