Emotion Processing in Virtual Reality: EEG Functional Connectivity Using Weighted Phase Lag Index.

Area of Science:

• Neuroscience
• Cognitive Science
• Psychology

Background:

• Understanding the neural basis of emotion is crucial for treating emotional disorders like depression.
• Existing research highlights the need for detailed investigation into brain activity during various emotional states.

Purpose of the Study:

• To explore the neural mechanisms underlying different emotional states using electroencephalogram (EEG).
• To identify specific brain networks involved in emotion processing and their functional connectivity.

Main Methods:

• Recruited 42 healthy participants and induced emotions via virtual reality (VR).
• Recorded electroencephalogram (EEG) data during induced emotional states.
• Applied the weighted phase lag index (wPLI) to analyze brain network connectivity across seven functional subnetworks.

Main Results:

• The Default Mode Network (DMN) demonstrated the strongest association with emotional states.
• Positive emotions correlated with increased phase synchronization within the DMN (theta and alpha bands).
• Enhanced phase synchronization in the Visual Network during positive emotions was observed in the theta band.

Conclusions:

• The Default Mode Network (DMN) and Visual Network play significant roles in processing emotions.
• Connectivity patterns within and between these networks are indicative of emotional states.
• Findings provide insights into the neural correlates of positive emotions and potential targets for emotional disorder interventions.