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Event-related EEG oscillatory responses elicited by dynamic facial expression

Authors
  • Aktürk, Tuba1, 1, 2
  • de Graaf, Tom A.2
  • Abra, Yasemin3, 4, 5
  • Şahoğlu-Göktaş, Sevilay1, 1
  • Özkan, Dilek6
  • Kula, Aysun7
  • Güntekin, Bahar1, 1
  • 1 Istanbul Medipol University, Istanbul, Turkey , Istanbul (Turkey)
  • 2 Maastricht University, Maastricht, Netherlands , Maastricht (Netherlands)
  • 3 Middle East Technical University, Ankara, Turkey , Ankara (Turkey)
  • 4 Universität Der Bundeswehr München, Munich, Germany , Munich (Germany)
  • 5 Ludwig-Maximilians-Universität München, Munich, Germany , Munich (Germany)
  • 6 Konya Necmettin Erbakan University, Konya, Turkey , Konya (Turkey)
  • 7 Sivas Cumhuriyet University, Sivas, Turkey , Sivas (Turkey)
Type
Published Article
Journal
BioMedical Engineering OnLine
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Apr 27, 2021
Volume
20
Issue
1
Identifiers
DOI: 10.1186/s12938-021-00882-8
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundRecognition of facial expressions (FEs) plays a crucial role in social interactions. Most studies on FE recognition use static (image) stimuli, even though real-life FEs are dynamic. FE processing is complex and multifaceted, and its neural correlates remain unclear. Transitioning from static to dynamic FE stimuli might help disentangle the neural oscillatory mechanisms underlying face processing and recognition of emotion expression. To our knowledge, we here present the first time–frequency exploration of oscillatory brain mechanisms underlying the processing of dynamic FEs.ResultsVideos of joyful, fearful, and neutral dynamic facial expressions were presented to 18 included healthy young adults. We analyzed event-related activity in electroencephalography (EEG) data, focusing on the delta, theta, and alpha-band oscillations. Since the videos involved a transition from neutral to emotional expressions (onset around 500 ms), we identified time windows that might correspond to face perception initially (time window 1; first TW), and emotion expression recognition subsequently (around 1000 ms; second TW). First TW showed increased power and phase-locking values for all frequency bands. In the first TW, power and phase-locking values were higher in the delta and theta bands for emotional FEs as compared to neutral FEs, thus potentially serving as a marker for emotion recognition in dynamic face processing.ConclusionsOur time–frequency exploration revealed consistent oscillatory responses to complex, dynamic, ecologically meaningful FE stimuli. We conclude that while dynamic FE processing involves complex network dynamics, dynamic FEs were successfully used to reveal temporally separate oscillation responses related to face processing and subsequently emotion expression recognition.

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