Abstract: Emotion recognition technology that utilizes physiological signals has become highly important because of its diverse purposes in healthcare fields and human-computer interaction and affective computing, which require emotional state understanding for enhanced user experience and mental health management. Support Vector Machines (SVM) and Random Forest (RF) serve as traditional machine learning approaches for emotion classification, but they struggle to accurately model spatial, temporal and long-range dependencies within multimodal physiological data, which leads to degraded overall performance. Created an Attention-Based CNN-BiLSTM-Transformer Model, which unites several neural network structures to extract features and classify information more effectively. This model implements Convolutional Neural Networks for detecting spatial patterns at the raw level of numerous physiological signals, which contain Electroencephalography, Electrocardiography, Galvanic Skin Response, and Electromyography. BiLSTM works as a temporal model which analyzes time-series physiological patterns through dual-directional contextual processing to create improved features from historical data patterns. The Transformer encoder serves to detect extended relationships between sequence items for better emotional change comprehension throughout time. The classification accuracy receives additional improvement because an attention-based fusion mechanism applies dynamic importance weights to different physiological signals, so the most significant features optimize the ultimate decision process. Testing of the proposed model using publicly accessible DEAP and AMIGOS resulted in 88.2% accuracy on DEAP while achieving 89.5% accuracy on AMIGOS, and both outcomes exceeded conventional machine learning methods as well as baseline deep learning approaches, which used CNN-LSTM and Transformer-only models. Testing showed that the attention mechanism successfully determined how to weigh multiple features, which resulted in better classification success. A deep learning framework based on TensorFlow and PyTorch operates throughout the implementation in Python to provide an efficient solution for emotion recognition in physiological signals.

Yue Pan, “Emotion Recognition Algorithm Based on Multi-Modal Physiological Signal Feature Fusion Using Artificial Intelligence and Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160613

@article{Pan2025,
title = {Emotion Recognition Algorithm Based on Multi-Modal Physiological Signal Feature Fusion Using Artificial Intelligence and Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160613},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160613},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Yue Pan}
}