Neuro-VR Experiment
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CSP_Model
HBP Analysis
LSTM Model
Preprocessing/ECG
SPOC
.gitignore
README.md

README.md

NeVRo

This is a rough outline. A more detailed description will come soon. Please see the publications for the specific models linked in the corresponding sections.

Introduction

We used virtual reality (VR) to investigate emotional arousal under ecologically valid conditions. 45 subjects experienced virtual roller coaster rides while their neural (EEG) and peripheral physiological (ECG, GSR) responses were recorded. Afterwards, they rated their subject levels of arousal retrospectively on a continuous scale while viewing a recording of their experience.

Methods

We tackled the data with three model approaches. The corresponding code can be found in the respective folders.

SPoC Model

upload coming soon
Source Power Comodulation (SPoC) decomposes the EEG signal such that it maximizes the covariance between the power-band of the frequency of interest (here alpha, 8-12Hz) and the target variable (ratings).

CSP Model

Common Spatial Pattern (CSP) algorithm derives a set of spatial filters to project the EEG data onto compontents whose band-power maximally relates to the prevalence of specified classes (here low and high arousal).

This part of the study was published at IEEE VR 2018 in Reutlingen, Germany:
Klotzsche, Mariola, Hofmann, Nikulin, Villringer, & Gaebler. IEEE VR, 2018.

LSTM Model

Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) were trained on alpha-frequency components of the recorded EEG signal to predict subjective reports of arousal (ratings) in a binary (low and high arousal) and a continuous prediction task. The fed EEG components were generated via Spatio Spectral Decomposition (SSD) or SPoC. The SSD emphasizes the frequency of interest (here alpha) while attenuating the adjacent frequency bins. Performances of SPoC-trained models served as benchmark-proxies for models that were trained only on neural alpha information.
Furthermore, we tested whether peripheral physiological responses, here the cardiac information (ECG), increases the performance of the model, and therefore encodes additional information about the subjective experience of arousal.

This part of the study was published at IEEE AIVR 2018 in Taichung, Taiwan:
Hofmann, Klotzsche, Mariola, Nikulin, Villringer, & Gaebler. IEEE AIVR, 2018

Miscellaneous

Before the main phase of the experiment, we tested the Interceptive Accuracy (IA) of each subject with the Heart Beat Perception (HBP) task.

Collaborators

Simon M. Hofmann
Felix Klotzsche
Alberto Mariola