VR-EEG-Social-Anxiety

From Affective, Neuroscience, and Decision-making Lab

Highlights

• We combined virtual reality & simultaneous EEG-ECG recording to probe the neurophysiological responses of negative emotions, which can be considered as individualistic neural features which can predict individuals’ affective patterns during social interaction

• We found that individual differences in generating emotions during social interactions can be detected through neurophysiological responses. Individuals who shared similar neural patterns during VR-induced anxiety shared a similar effect of cue on their affective experience during social interaction.

• The results also suggested that controlling for other factors, social reward holds a unique contribution to the anxiety component of individuals' mixed affective experience.

• Virtual reality proved to be a valuable tool in creating realistic social and emotional experiences within controlled laboratory settings. This enhances the naturalism of neuroimaging and socio-affective research, which provides deeper insights into human behavior and affection.

Description

• This repo mainly contains scripts for processing multimodal neurophysiological data from this project.
• Data modality: EEG (Brain Porduct 64 ch, montage see BP_Montage), ECG (BIOPAC 3 leads)
• Paradigm: naturalistic viewing of negative emotinal videos under virtual reality (Unity + Steam VR), computer-based behavioral task, scales
• VR googles: VIVE Pro EYE (HTC)
• Main analysis method: ANOVA, Linear Regresssion, Intersubject similarity analysis (ISC) & Intersubject representational similarity analysis (IS-RSA)

Structure

├── Unity_call_python
│   ├── PortEEG.py         # set the trigger to EEG & ECG
│   ├── AddPortEEG.py      # load the PortEEG function 
│   ├── UnityCallPython.cs # script for importing python script in unity
│   └── inpoutx64.dll      # dll for parallel ports
├── data                   # preprocessed behavioral and EEG data
├── 1_behav_all_figs.qmd   # visualization of the behavioral results
├── 2_ISC_EEG_new.ipynb    # intersubejct simialrity analysis of the EEG data
├── 3_ISC_behav.ipynb      # intersubejct simialrity analysis of the preprocessed behav data
├── 4_ISRSA.ipynb          # intersubject representational similarity analysis
├── LICENSE
└── README.md

Requirements

Python

python 3.8
mne
neurokit2
pandas
numpy
scipy
statsmodels
matplotlib
seaborn

R

Quarto
ggplot2
tidyverse
dplyr
ggpubr
knitr
circlize
leaps