西安电子科技大学学报（自然科学版）2024，Vol.51Issue(2)：182-195,14.DOI:10.19665/j.issn1001-2400.20230211
多尺度卷积结合Transformer的抑郁脑电分类研究
Study of EEG classification of depression by multi-scale convolution combined with the Transformer
摘要
Abstract
In the process of using the deep learning model to classify the EEG signals of depression,aiming at the problem of insufficient feature extraction in single-scale convolution and the limitation of the convolutional neural network in perceiving the global dependence of EEG signals,a multi-scale dynamic convolution network module and the gated transformer encoder module are designed respectively,which are combined with the temporal convolution network,and a hybrid network model MGTTCNet is proposed to classify the EEG signals of patients with depression and healthy controls.First,multi-scale dynamic convolution is used to capture the multi-scale time-frequency information of EEG signals from spatial and frequency domains.Second,the gated transformer encoder is used to learn global dependencies in EEG signals,which effectively enhances the ability of the network to express relevant EEG signal features using the multi-head attention mechanism.Third,the temporal convolution network is used to extract temporal features available for EEG signals.Finally,the extracted abstract features are fed into the classification module for classification.The proposed model is experimentally validated on the public data set MODMA using the Hold-out method and the 10-Fold Cross Validation method,with the classification accuracy being 98.51%and 98.53%,respectively.Compared with the baseline single-scale model EEGNet,the classification accuracy of the proposed model is increased by 1.89%and 1.93%,the F1 value is increased by 2.05%and 2.08%,and the kappa coefficient values are increased by 0.0381 and 0.0385,respectively.Meanwhile,the ablation experiments verify the effectiveness of each module designed in this paper.关键词
脑电信号/抑郁分类/深度学习/Transformer/时间卷积网络Key words
electroencephalography/depression classification/deep learning/Transformer/temporal convolutional networks分类
信息技术与安全科学引用本文复制引用
翟凤文,孙芳林,金静..多尺度卷积结合Transformer的抑郁脑电分类研究[J].西安电子科技大学学报（自然科学版）,2024,51(2):182-195,14.基金项目
甘肃省自然基金(21JR11RA062) （21JR11RA062）
甘肃省高校创新基金(2022A-047) （2022A-047）
