A Multiplex Connectivity Map of Valence-Arousal Emotional Model

Avraam D. Marimpis*, Stavros I. Dimitriadis, Rainer Goebel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

A high number of studies have already demonstrated an electroencephalography (EEG)-based emotion recognition system with moderate results. Emotions are classified into discrete and dimensional models. We focused on the latter that incorporates valence and arousal dimensions. The mainstream methodology is the extraction of univariate measures derived from EEG activity from various frequencies classifying trials into low/high valence and arousal levels. Here, we evaluated brain connectivity within and between brain frequencies under the multiplexity framework. We analyzed an EEG database called DEAP that contains EEG responses to video stimuli and users’ emotional self-assessments. We adopted a dynamic functional connectivity analysis under the notion of our dominant coupling model (DoCM). DoCM detects the dominant coupling mode per pair of EEG sensors, which can be either within frequencies coupling (intra) or between frequencies coupling (cross-frequency). DoCM revealed an integrated dynamic functional connectivity graph (IDFCG) that keeps both the strength and the preferred dominant coupling mode. We aimed to create a connectomic mapping of valence-arousal map via employing features derive from IDFCG. Our results outperformed previous findings succeeding to predict in a high accuracy participants’ ratings in valence and arousal dimensions based on a flexibility index of dominant coupling modes
Original languageEnglish
Pages (from-to)170928-170938
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Electroencephalography
  • Couplings
  • Sensors
  • Brain modeling
  • Psychology
  • Indexes
  • Affective computing
  • computational neuroscience
  • emotion in human-computer interaction
  • graph theory
  • modeling from video
  • modeling human emotion
  • music
  • neuroscience
  • video
  • BRAIN
  • RECOGNITION
  • MACHINE

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