Temporal Spatial Graph- Integrated Framework for EEG- Based Emotion Recognition Using LSTM-GCN Architecture

This paper presents an advanced framework for EEG-based emotion recognition that synergistically combines temporal and spatial neural modeling. A dual-stage architecture is employed, where Long Short-Term Memory (LSTM) networks extract dynamic temporal features from multi-channel EEG signals, while Graph Convolutional Networks (GCN) capture spatial correlations among electrodes through a structured brain topology. The approach leverages both temporal evolution and spatial connectivity to learn discriminative representations of affective states. Evaluations on a benchmark dataset confirm that the proposed LSTM-GCN fusion model effectively enhances the robustness and generalization of emotion classification compared to conventional deep learning baselines. The findings highlight the potential of graph-aware temporal modeling for next-generation affective computing applications. In this study, we successfully achieved promising results, including an accuracy of ۹۷.۹۸% and a loss of ۰.۰۵۵۸.

کلیدواژه ها:

SEED ، EEG ، Emotion Recognition ، Deep Learning ، LSTM ، GCN