Huang Weijian, Wang Li, Yan Zhenxiong, Liu Yanjun
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:192-195. doi: 10.1109/EMBC44109.2020.9176361.
The brain-computer interface (BCI) based on electroencephalography (EEG) converts the subject's intentions into control signals. For the BCI, the study of motor imagery has been widely used. In recent years, a classification method based on a convolutional neural network (CNNs) has been proposed. However, most of the existing methods use a single convolution scale on CNN, and another problem that affects the results is limited training data. To solve these problems, we propose a mixed-scale CNN architecture, and a data augmentation method is used to classify the EEG of motor imagery. After classifying the BCI competition IV dataset 2b, the average classification accuracy is 81.52%. Compared with the existing methods, our method has a better classification result. This method effectively solves the problems existing in the existing CNN-based motor imagery classification methods, and it improves the classification accuracy.
基于脑电图（EEG）的脑机接口（BCI）将受试者的意图转换为控制信号。对于BCI，运动想象的研究已被广泛应用。近年来，一种基于卷积神经网络（CNN）的分类方法被提出。然而，现有的大多数方法在CNN上使用单一的卷积尺度，另一个影响结果的问题是训练数据有限。为了解决这些问题，我们提出了一种混合尺度的CNN架构，并使用一种数据增强方法对运动想象的EEG进行分类。对BCI竞赛IV数据集2b进行分类后，平均分类准确率为81.52%。与现有方法相比，我们的方法具有更好的分类结果。该方法有效解决了现有基于CNN的运动想象分类方法中存在的问题，提高了分类准确率。
