卷积神经网络在脑疲劳检测中的研究

脑疲劳是由于持续进行脑力劳动导致的一种状态,脑电被认为是脑疲劳状态检测的最佳工具。如何选取合适的脑疲劳特征成为脑疲劳检测的关键问题,传统模式识别中手动提取特征会产生信息损失,针对脑电的时空特性,本文设计了具有时域卷积核、空间域卷积核的深层卷积神经网络和浅层卷积神经网络两种网络结构,将特征提取和状态分类合二为一,对正常态与疲劳态脑电数据进行分类,可视化了卷积神经网络的空间域卷积核。结果表明,浅层卷积神经网络平均分类正确率为98.868%,深层卷积神经网络平均分类正确率为98.217%,均高于传统分类方法,通过空间域卷积核的可视化,能够了解不同导联在网络中的参与程度,验证了该模型在脑疲劳检测任务中具有很高的有效性,同时为脑疲劳检测提供了新思路。

Research on Convolutional Neural Network in Mental Fatigue Recognition

Mental fatigue is a condition caused by prolonged mental work. Electroencephalogram(EEG) is often used as the preferred tool for detecting mental fatigue. The key problem in the mental fatigue recognition is how to search for suitable EEG features. For the problem of information loss caused by the manual extraction of features in traditional pattern recognition, the convolutional neural network in deep learning was introduced. Due to the temporal and spatial characteristics of EEG, deep convolution neural network and shallow convolutional neural network with time domain convolution kernel and spatial domain convolution kernel were designed in this paper. They combined feature extraction and classification. Both of them classified normal and fatigued EEG data and we visualize the spatial domain convolution kernel of convolutional neural networks. The results show that the average classification accuracy of shallow convolution neural network is 98.868%, and the average classification accuracy of deep convolutional neural network classification is 98.217% . Both of them are higher than the accuracy of the traditional classification method. We can find the degree of participation of different electrodes in the network by means of visualization of the spatial domain convolution kernel. This shows that the model is highly effective in mental fatigue recognition tasks. The idea, at the same time, verifies that convolutional neural network provides a new method for the detection of mental fatigue state.