The convolutional neural network (CNN) model is an active research topic in the field of EEG signals analysis. However, the classification effect of CNN on EEG signals of amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) is not ideal. Even if EEG signals are transformed into multispectral images that are more closely matched with the model, the best classification performance can not be achieved. Therefore, to improve the performance of CNN toward EEG multispectral image classification, a multi-view convolutional neural network (MVCNN) classification model based on inceptionV1 is designed in this study. This model mainly improves and optimizes the convolutional layers and stochastic gradient descent (SGD) in the convolutional architecture model. Firstly, based on the discreteness of EEG multispectral image features, the multi-view convolutional layer structure was proposed. Then the learning rate change function of the SGD was optimized to increase the classification performance. The multi-view convolutional nerve was used in an EEG multispectral classification task involving 19 aMCI with T2DM and 20 normal controls. The results showed that compared with the traditional classification models, MVCNN had a better stability and accuracy. Therefore, MVCNN could be used as an effective feature classification method for aMCI with T2DM.

中文翻译：

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基于多视图卷积神经网络的2型糖尿病轻度轻度认知障碍静息状态脑电信号特征分类方法。

卷积神经网络（CNN）模型是EEG信号分析领域中一个活跃的研究主题。但是，CNN对2型糖尿病（T2DM）的轻度轻度认知障碍（aMCI）的EEG信号的分类效果并不理想。即使将EEG信号转换为与模型更匹配的多光谱图像，也无法获得最佳分类性能。因此，为提高CNN对EEG多光谱图像分类的性能，本研究设计了一种基于InceptionV1的多视图卷积神经网络（MVCNN）分类模型。该模型主要改进和优化了卷积体系结构模型中的卷积层和随机梯度下降（SGD）。首先，基于脑电多光谱图像特征的离散性，提出了多视图卷积层结构。然后，对SGD的学习率变化函数进行了优化，以提高分类性能。多视图卷积神经用于脑电图多谱分类任务，涉及19 aMCI，T2DM和20名正常对照。结果表明，与传统分类模型相比，MVCNN具有更好的稳定性和准确性。因此，MVCNN可以作为具有T2DM的aMCI的有效特征分类方法。结果表明，与传统分类模型相比，MVCNN具有更好的稳定性和准确性。因此，MVCNN可以作为具有T2DM的aMCI的有效特征分类方法。结果表明，与传统分类模型相比，MVCNN具有更好的稳定性和准确性。因此，MVCNN可以作为具有T2DM的aMCI的有效特征分类方法。