Emotion Recognition Method for English Classroom Learning Based on Facial Expression and EEG Signal Fusion

This study proposes an advanced method for emotion recognition in English classroom learning by fusing facial expression analysis and EEG signal data. The approach integrates multimodal data to enhance the robustness and accuracy of emotion detection, offering a comprehensive solution for understanding students’ emotional states. The proposed framework utilizes deep learning architectures for feature extraction and employs a hierarchical attention mechanism alongside a temporal consistency module. These elements capture both spatial features from facial expressions and temporal patterns from EEG signals, resulting in a unified model capable of real-time emotion recognition. The Emotion Fusion Network (EFN) is designed to address the challenges of multimodal data integration, ensuring effective interaction between facial expressions and EEG signals. This model utilizes separate processing modules for each modality, followed by a fusion mechanism that enhances the emotion recognition process. By dynamically adjusting the contribution of each modality, the system ensures that more reliable data sources are given higher priority, thereby improving the accuracy of emotion detection. The Emotion-Aware Multimodal Fusion Framework incorporates specific strategies to address the unique challenges of emotion recognition in educational environments. This framework focuses on feature extraction, alignment of temporal data, and robust fusion techniques to provide consistent and precise results. By leveraging these advanced methods, the system offers significant improvements in recognizing emotional states, ultimately contributing to personalized learning experiences, and adaptive teaching strategies in real-time classroom settings.