摘要
An intelligent emotion recognition system based on electroencephalography(EEG)signals shows considerable potential in various domains such as healthcare,entertainment,and education,thanks to its portability,high temporal resolution,and real-time capabilities.However,the existing research in this field faces limitations stemming from the nonstationary nature and individual variability of EEG signals.In this study,we present a novel EEG emotion recognition model,named GraphEmotionNet,designed to enhance the accuracy of EEG-based emotion recognition through the incorporation of a spatiotemporal attention mechanism and transfer learning.The proposed GraphEmotionNet model can effectively learn the intrinsic connections between EEG channels and construct an adaptive graph.This graph’s adaptive nature is crucial in optimizing spatial-temporal graph convolutions,which in turn enhances spatial-temporal feature characterization and contributes to the process of emotion classification.Moreover,an integration of domain adaptation aligns the extracted features across different domains,further alleviating the impact of individual EEG variability.We evaluate the model performance on two benchmark databases,employing two types of cross-validation protocols:within-subject cross-validation and cross-subject cross-validation.The experimental results affirm the model’s efficacy in extracting EEG features linked to emotional semantics and demonstrate its promising performance in emotion recognition.
基金
supported in part by the National Natural Science Foundation of China under grants 62276169,62071310,and 82272114
in part by the Medical-Engineering Interdisciplinary Research Foundation of Shenzhen University
in part by the Shenzhen University-Lingnan University Joint Research Programme
in part by the Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions under grant 2022SHIBS0003
in part by Shenzhen Science and Technology Research and Development Fund for Sustainable Development Project under grant KCXFZ202012211736130.
