Synchrosqueezing transform based feature extraction from EEG signals for emotional state prediction

Ozel P., Akan A., YILMAZ B.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL, vol.52, pp.152-161, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 52
  • Publication Date: 2019
  • Doi Number: 10.1016/j.bspc.2019.04.023
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.152-161
  • Keywords: Emotion recognition, Electroencephalography, Synchrosqueezing transform, Multivariate synchrosqueezing transform, VAD model, EMPIRICAL MODE DECOMPOSITION, HILBERT SPECTRUM, CLASSIFICATION, RECOGNITION, FRAMEWORK, SELECTION
  • Abdullah Gül University Affiliated: Yes


This paper presents a novel method for emotion recognition based on time-frequency analysis using multivariate synchrosqueezing transform (MSST) of multichannel electroencephalography (EEG) signals. With the advancements of the multichannel sensor applications, the need for multivariate algorithms has become obvious for extracting features that stem from multichannel dependency in addition to mono-channel features. In order to model the joint oscillatory structure of these multichannel signals, MSST has recently been proposed. It uses the concepts of joint instantaneous frequency and bandwidth. Electrophysiological data processing mostly requires joint time-frequency analysis in addition to both time and frequency analysis separately. The short-time Fourier transform (STFT) and wavelet transform (WT) are the main approaches utilized in time-frequency analysis. In this paper, the feasibility and performance of multivariate wavelet-based synchrosqueezing algorithm was demonstrated on EEG signals obtained from publically available DEAP database by comparing with its univariate version. Eight emotional states were considered by combining arousal-valence and dominance dimensions. Using linear support vector machines (SVM) as a classifier, MSST and its univariate version resulted in the highest prediction accuracy rates of (9) over tilde3% among all emotional states. (C) 2019 Elsevier Ltd. All rights reserved.