Discriminating Multiple Emotional States from EEG Using a Data-Adaptive, Multiscale Information-Theoretic Approach

International Journal of Neural Systems
Yelena TonoyanMarc M Van Hulle

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.

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Citations

Jan 5, 2018·International Journal of Neural Systems·Alicia Fernández-SotoAntonio Fernández-Caballero
Jun 22, 2017·International Journal of Neural Systems·Regina W Y WangShang-Wen Chuang
May 13, 2017·Frontiers in Neuroinformatics·Arturo Martínez-RodrigoAntonio Fernández-Caballero
Jun 20, 2019·Frontiers in Neuroinformatics·Arturo Martínez-RodrigoAntonio Fernández-Caballero
Nov 13, 2018·International Journal of Neural Systems·Jennifer SorinasEduardo Fernandez
Oct 31, 2018·International Journal of Neural Systems·Arturo Martínez-RodrigoAntonio Fernández-Caballero

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