Emotion Recognition Classification in EEG Signals Using Multivariate Synchrosqueezing Transform

ÖZEL P., AKAN A., YILMAZ B.

Medical Technologies National Congress (TIPTEKNO), Trabzon, Turkey, 12 - 14 October 2017 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Trabzon
  • Country: Turkey
  • Abdullah Gül University Affiliated: Yes

Abstract

Electrophysiological data processing can take place both in time and in frequency domains as well as in the joint time-frequency domain. Short Time Fourier Transform and Wavelet Transform are commonly used time-frequency analysis methods. The limitations of these methods initiated the use of methods such as synchrosqueezing and multivariate synchrosqueezing methods. In our proposed method 88.9%, 77.8%, 80.6% accuracy rates were obtained respectively for the valence, activation and dominance parameters using and multivariate synchrosqueezing methods and support vector machines(SVM) which yields better results than most of the other methods mentioned in the literature.