Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition

Year 2018, Volume: 18 Issue: 2, 263 - 274, 03.08.2018

Pınar Özel , Aydın Akan Bülent Yılmaz

Abstract

DOI: 10.26650/electrica.2018.00998

Emotion state detection or emotion
recognition cuts across different disciplines because of the many parameters
that embrace the brain's complex neural structure, signal processing methods,
and pattern recognition algorithms. Currently, in addition to classical
time-frequency methods, emotional state data have been processed via
data-driven methods such as empirical mode decomposition (EMD). Despite its
various benefits, EMD has several drawbacks: it is intended for univariate
data; it is prone to mode mixing; and the number of local extrema must be
enough before the EMD process can begin. To overcome these problems, this study
employs a multivariate EMD and its noise-assisted version in the emotional
state classification of electroencephalogram signals.

Keywords

Emotion recognition, electroencephalography, empirical mode decomposition, multivariate empirical mode decomposition, noise assisted multivariate empirical mode decomposition

Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition

Abstract

DOI: 10.26650/electrica.2018.00998

Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals.

Keywords

Emotion recognition, electroencephalography, empirical mode decomposition, multivariate empirical mode decomposition, noise assisted multivariate empirical mode decomposition

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