Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context

TL;DR

This study introduces a novel EEG dataset capturing brief, self-reported emotional events during naturalistic stimuli, demonstrating improved emotion classification accuracy over existing datasets by focusing on precise emotional feelings.

Contribution

The paper presents a new dataset with self-reported emotional events and a CNN-LSTM classification approach that outperforms benchmarks like DEAP and SEED.

Findings

Higher classification accuracy with emotional event data

Precise emotional feelings improve emotion recognition

Compared favorably against DEAP and SEED datasets

Abstract

Emotion recognition using EEG signals is an emerging area of research due to its broad applicability in BCI. Emotional feelings are hard to stimulate in the lab. Emotions do not last long, yet they need enough context to be perceived and felt. However, most EEG-related emotion databases either suffer from emotionally irrelevant details (due to prolonged duration stimulus) or have minimal context doubting the feeling of any emotion using the stimulus. We tried to reduce the impact of this trade-off by designing an experiment in which participants are free to report their emotional feelings simultaneously watching the emotional stimulus. We called these reported emotional feelings "Emotional Events" in our Dataset on Emotion with Naturalistic Stimuli (DENS). We used EEG signals to classify emotional events on different combinations of Valence(V) and Arousal(A) dimensions and compared the results with benchmark datasets of DEAP and SEED. STFT is used for feature extraction and used in the classification model consisting of CNN-LSTM hybrid layers. We achieved significantly higher accuracy with our data compared to DEEP and SEED data. We conclude that having precise information about emotional feelings improves the classification accuracy compared to long-duration EEG signals which might be contaminated by mind-wandering.