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Emotion Recognition from Physiological Channels Using Graph Neural Network.

Tomasz Wierciński¹, Mateusz Rock², Robert Zwierzycki²

¹Faculty of Electronics, Telecommunications and Informatics and Digital Technologies Center, Gdańsk University of Technology, 80-233 Gdańsk, Poland.

Sensors (Basel, Switzerland)

|April 23, 2022

Summary

This study adapted GraphSleepNet, a graph neural network (GNN), for emotion recognition. The research validates GNNs for recognizing emotions using differential entropy features, showing improved accuracy for the Ekman model.

Keywords:

affective computing bioelectrical signals biosensors biosignals emotion recognition graph neural network multi-modality

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Area of Science:

Affective computing
Machine learning
Artificial intelligence

Background:

Neural networks are increasingly used in affective computing.
Graph neural networks (GNNs) represent a new trend for emotion recognition.
Existing methods often focus on discrete emotion models.

Purpose of the Study:

To adapt and validate GraphSleepNet, a GNN for sleep staging, for emotion recognition.
To analyze GNN performance for emotion recognition using differential entropy features within the Ekman and Circumplex models.
To investigate continuous emotion recognition during activities and the influence of multimodal data.

Main Methods:

Utilized GraphSleepNet, a GNN architecture, for emotion recognition tasks.
Validated the model on the AMIGOS dataset.
Analyzed emotion recognition accuracy for Ekman and Circumplex models using differential entropy features.
Evaluated performance across different modalities and GNN configurations.

Main Results:

The adapted GNN demonstrated effectiveness for emotion recognition, particularly for the Ekman model.
Multimodal data significantly influenced the accuracy of recognizing basic emotions and neutral states.
The GNN achieved accuracy comparable to baseline methods for the Circumplex model.
Specific configurations of the GNN yielded notable results for Ekman's emotion model.

Conclusions:

Graph neural networks, specifically the GraphSleepNet architecture, show significant potential for emotion recognition.
Differential entropy features are valuable for continuous emotion recognition, especially within the Ekman model.
The study highlights the impact of data modalities on affective computing model performance.