计算机技术与发展2023，Vol.33Issue(12)：178-184,7.DOI:10.3969/j.issn.1673-629X.2023.12.025
基于Swin Transformer的四维脑电情绪识别
Swin Transformer-based 4-D EEG Emotion Recognition
摘要
Abstract
In recent years,electroencephalogram(EEG)-based emotion recognition has focused on the use of convolutional neural networks,recurrent neural networks and deep belief network models.These methods can use global differences to distinguish between different emotional states,but ignore the effect of local EEG changes on emotional states.To address these issues,we use a 4-dimensional EEG emotion recognition model based on the Swin Transformer.The model can better capture both small local spatial features and complex time-series features.Compared with other emotion recognition methods,the model proposed improves the feature connectivity between different blocks through a self-attention mechanism based on shifted windows,which makes the model more modelable and also reduces the computational complexity.In addition,we use the public emotion EEG dataset SEED to evaluate the feasibility and effectiveness of this model,with an accuracy of 94.73%±1.72% for single-subject emotion triple classification and 89.63%±3.42% for cross-subject emotion triple classification,and the testing speed can reach the level of real-time processing.The experimental results show that 4-D EEG emotion recognition based on the Swin Transformer model can achieve high emotion classification accuracy and fast testing speed even with small sample training through local feature learning.关键词
深度学习/情绪识别/脑电图/特征融合/Swin TransformerKey words
deep learning/emotion recognition/electroencephalogram(EEG)/feature fusion/Swin Transformer分类
信息技术与安全科学引用本文复制引用
陈宗楠,金家瑞,潘家辉..基于Swin Transformer的四维脑电情绪识别[J].计算机技术与发展,2023,33(12):178-184,7.基金项目
国家自然科学基金项目(62076103) （62076103）
科技创新2030项目-"脑科学与类脑研究"重点项目(2022ZD0208900) （2022ZD0208900）
