Research on emotion recognition methods based on multi-modal physiological signal feature fusion.
10.7507/1001-5515.202401020
- Author:
Zhiwen ZHANG
1
;
Naigong YU
1
;
Yan BIAN
2
;
Jinhan YAN
1
Author Information
1. School of Information Science and Technology, Beijing University of Technology, Beijing 100124, P. R. China.
2. School of Automation and Electrical Engineering, Tianjin Polytechnic Normal University, Tianjin 300222, P. R. China.
- Publication Type:Journal Article
- Keywords:
Electrodermal activity;
Electroencephalogram;
Electromyogram;
Emotion;
Feature fusion;
Multi-modality
- MeSH:
Humans;
Emotions/physiology*;
Electroencephalography;
Support Vector Machine;
Electromyography;
Signal Processing, Computer-Assisted;
Galvanic Skin Response/physiology*;
Machine Learning;
Male
- From:
Journal of Biomedical Engineering
2025;42(1):17-23
- CountryChina
- Language:Chinese
-
Abstract:
Emotion classification and recognition is a crucial area in emotional computing. Physiological signals, such as electroencephalogram (EEG), provide an accurate reflection of emotions and are difficult to disguise. However, emotion recognition still faces challenges in single-modal signal feature extraction and multi-modal signal integration. This study collected EEG, electromyogram (EMG), and electrodermal activity (EDA) signals from participants under three emotional states: happiness, sadness, and fear. A feature-weighted fusion method was applied for integrating the signals, and both support vector machine (SVM) and extreme learning machine (ELM) were used for classification. The results showed that the classification accuracy was highest when the fusion weights were set to EEG 0.7, EMG 0.15, and EDA 0.15, achieving accuracy rates of 80.19% and 82.48% for SVM and ELM, respectively. These rates represented an improvement of 5.81% and 2.95% compared to using EEG alone. This study offers methodological support for emotion classification and recognition using multi-modal physiological signals.
