Mild Cognitive Impairment Classification Convolutional Neural Network with Attention Mechanism

Abstract

Mild cognitive impairment (MCI) is an aging disease mainly caused by memory impairment after the occurrence of brain lesions. EEG analysis is an effective non-invasive method to recognize brain activity and MCI. Due to the highly non-stationary characteristics of EEG, it is a challenging task to extract features from EEG signals and further improve classification performance for MCI. In this paper, we will present a novel deep learning approach for MCI based on convolutional neural networks (CNN) using EEG signals, where the CNN is used for feature extraction from EEG signals in cognitive tasks, a softmax function is utilized as classifier and creatively the attention mechanism is applied to one of the convolution operations. Experimental results show that the CNN with attention mechanism has an average accuracy of 79.66% (validation) after the accuracy of the validation set has stabilized, which is significantly higher than that of traditional convolutional networks. Compared with the highest accuracy of 70.09% in the other four existing approaches, it shows an obvious advantage. The proposed approach can enrich the convolution features of EEG, improve the model fitting ability and generalization performance, and realize the classification of MCI effectively.