基于多尺度动态卷积与注意力机制的抑郁脑电信号分类方法OA

Depression is a serious mental disorder that negatively affects the patient's quality of life and social functioning.In or-der to explore an electroencephalogram-based classification method for depression to improve the accuracy of early diagnosis of depression,this paper designes a deep learning model called MDATCNet,which exploits a multi-scale dynamic convolution module capturing the rich features of signals in both spatial and frequency dimensions.To further enhance the representation of the model,this paper integrates the multi-head self-attention mechanism,which allows the model to adaptively focus on the fea-tures that are most helpful for decision-making.Then,the time convolutional layer is responsible for mining the time series pat-terns in the time series data.Finally,the features are passed to a Softmax classifier to classify EEG signals.The feasibility of the model is evaluated on the public depression dataset using the ten-fold cross-validation method,and the recognition accuracy,sensitivity and specificity of the method based on MDATCNet in EEG can achieve 94.71%,99.37%,and 90.34%,respectively,and the experimental results show that the proposed model can effectively help the early diagnosis of depression.