Identifying Stable Patterns over Time for Emotion Recognition from EEG

TL;DR

This study investigates the stability of EEG patterns over time for emotion recognition, validating machine learning methods on multiple datasets, and identifying consistent neural markers associated with different emotions.

Contribution

It systematically evaluates machine learning techniques for EEG-based emotion recognition and identifies stable neural patterns across sessions and datasets.

Findings

Stable EEG patterns are consistent across sessions.

Lateral temporal areas activate more for positive emotions in beta and gamma bands.

Neutral emotions show higher alpha responses at parietal and occipital sites.

Abstract

In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. To validate the efficiency of the machine learning algorithms used in this study, we systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset for this study. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.