Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition


ÖZEL P., AKAN A., YILMAZ B.

ELECTRICA, cilt.18, sa.2, ss.263-274, 2018 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 2
  • Basım Tarihi: 2018
  • Doi Numarası: 10.26650/electrica.2018.00998
  • Dergi Adı: ELECTRICA
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.263-274
  • İstanbul Üniversitesi Adresli: Hayır

Özet

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.