Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition

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ELECTRICA, vol.18, no.2, pp.263-274, 2018 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 2
  • Publication Date: 2018
  • Doi Number: 10.26650/electrica.2018.00998
  • Journal Name: ELECTRICA
  • Journal Indexes: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
  • Page Numbers: pp.263-274
  • Keywords: Emotion recognition, electroencephalography, empirical mode decomposition, multivariate empirical mode decomposition, noise assisted multivariate empirical mode decomposition, AROUSAL, MEMD
  • Abdullah Gül University Affiliated: Yes


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.