Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

• Published in: IEEE access Vol. 12; pp. 50949 - 50965
• Main Authors: Bagherzadeh, Sara, Shalbaf, Ahmad, Shoeibi, Afshin, Jafari, Mahboobeh, Tan, Ru-San, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Arousal; Artificial neural networks; Brain modeling; Datasets; Deep learning; Effective connectivity; Electroencephalography; Emotion recognition; Emotional factors; Emotions; Feature extraction; Human-computer interface; Image classification; Information flow; Long short term memory; Machine learning; Medical imaging; Time-frequency analysis; Transfer functions; Transfer learning; Two dimensional models
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3384303

The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and robustness of emotion recognition by combining different effective connectivity (EC) methods and pre-trained convolutional neural networks (CNNs), as well as long short-term memory (LSTM). EC methods measure information flow in the brain during emotional states using EEG signals. We used three EC methods: transfer entropy (TE), partial directed coherence (PDC), and direct directed transfer function (dDTF). We estimated a fused image from these methods for each five-second window of 32-channel EEG signals. Then, we applied six pre-trained CNNs to classify the images into four emotion classes based on the two-dimensional valence-arousal model. We used the leave-one-subject-out cross-validation strategy to evaluate the classification results. We also used an ensemble model to select the best results from the best pre-trained CNNs using the majority voting approach. Moreover, we combined the CNNs with LSTM to improve recognition performance. We achieved the average accuracy and F-score of 98.76%, 98.86%, 98.66 and 98.88% for classifying emotions using DEAP and MAHNOB-HCI datasets, respectively. Our results show that fused images can increase the accuracy and that an ensemble and combination of pre-trained CNNs and LSTM can achieve high accuracy for automated emotion recognition. Our model outperformed other state-of-the-art systems using the same datasets for four-class emotion classification.