EEG-based Inter-Subject Correlation Schemes in a Stimuli-Shared Framework: Interplay with Valence and Arousal

TL;DR

This study investigates how inter-subject correlation in EEG signals relates to emotional states, specifically valence and arousal, across different computational schemes and datasets, revealing that ISC decreases with valence and increases with arousal.

Contribution

It introduces a comprehensive analysis of EEG-based ISC schemes in relation to emotion, highlighting the effects of valence and arousal on inter-subject EEG correlations.

Findings

ISC decreases with valence

ISC increases with arousal

Results consistent across datasets and schemes

Abstract

Affective computing is confronted to high inter-subject variability, in both emotional and physiological responses to a given stimulus. In a stimuli-shared framework, that is to say for different subjects who watch the same stimuli, Inter-Subject Correlation (ISC) measured from Electroencephalographic (EEG) recordings characterize the correlations between the respective signals at the different EEG channels. In order to investigate the interplay between ISC and emotion, we propose to study the effect of valence and arousal on the ISC score. To this end, we exploited various computational schemes corresponding to different subsets of the dataset: all the data, stimulus-wise, subject pairwise, and both stimulus-wise and subject pairwise. We thus applied these schemes to the HCI MAHNOB and DEAP databases. Our results suggest that the ISC score decreases with valence and increases with arousal, as already shown by previous results on functional MRI.