Deep Fuzzy Framework for Emotion Recognition using EEG Signals and Emotion Representation in Type-2 Fuzzy VAD Space

TL;DR

This paper introduces a deep fuzzy framework utilizing Type-2 fuzzy VAD space for emotion recognition from EEG signals, achieving high accuracy across 24 emotions and demonstrating cross-subject generalization for real-world applications.

Contribution

The study develops a novel fuzzy VAD space model integrated with EEG data, enhancing emotion recognition accuracy and generality over existing methods.

Findings

Emotion recognition accuracy of over 95% for 24 emotions.

Deep fuzzy framework outperforms crisp VAD and other fuzzy models.

Cross-subject emotion prediction accuracy of 78.37%."

Abstract

Recently, the representation of emotions in the Valence, Arousal and Dominance (VAD) space has drawn enough attention. However, the complex nature of emotions and the subjective biases in self-reported values of VAD make the emotion model too specific to a particular experiment. This study aims to develop a generic model representing emotions using a fuzzy VAD space and improve emotion recognition by utilizing this representation. We partitioned the crisp VAD space into a fuzzy VAD space using low, medium and high type-2 fuzzy dimensions to represent emotions. A framework that integrates fuzzy VAD space with EEG data has been developed to recognize emotions. The EEG features were extracted using spatial and temporal feature vectors from time-frequency spectrograms, while the subject-reported values of VAD were also considered. The study was conducted on the DENS dataset, which includes a wide range of twenty-four emotions, along with EEG data and subjective ratings. The study was validated using various deep fuzzy framework models based on type-2 fuzzy representation, cuboid probabilistic lattice representation and unsupervised fuzzy emotion clusters. These models resulted in emotion recognition accuracy of 96.09\%, 95.75\% and 95.31\%, respectively, for the classes of 24 emotions. The study also included an ablation study, one with crisp VAD space and the other without VAD space. The result with crisp VAD space performed better, while the deep fuzzy framework outperformed both models. The model was extended to predict cross-subject cases of emotions, and the results with 78.37\% accuracy are promising, proving the generality of our model. The generic nature of the developed model, along with its successful cross-subject predictions, gives direction for real-world applications in the areas such as affective computing, human-computer interaction, and mental health monitoring.