基于卷积神经网络的多类运动想象脑电信号识别

针对传统多类运动想象（MI）脑电信号的识别方法须进行繁琐的预处理以及特征提取问题，提出基于深度学习的MI信号自动分类方法.在样本表示方面，提出将多通道脑电（EEG）信号转化为一维序列信号处理，在增加样本数量的同时又能够忽略与通道位置相关的空间信息的影响；根据输入信号的特点，采用多层一维卷积神经网络学习不同运动想象状态时脑电信号中的时频信息，自动完成特征提取和分类工作. 将所提出的方法在公共数据集上与多种方法进行比较，并完成对实际采集数据集的分类. 利用所提方法在不需要先验知识的条件下，对脑电信号进行端到端的学习. 结果表明该方法可以获得更高的多分类准确率以及降低个体差异对分类的影响. 所提出的方法有利于促进基于MI 的脑机接口系统的开发.

Recognition of multi-class motor imagery EEG signals based on convolutional neural network

An automatic classification method of motor imagery (MI) signals based on deep learning was proposed, aiming at the traditional multi-class MI recognition method of electroencephalogram (EEG) signals that requires cumbersome preprocessing and feature extraction. In terms of sample representation, the multi-channel EEG signals were converted into a one-dimensional sequence signal for processing, which increased the number of samples while neglecting the influence of spatial information related to the channel position. And according to the characteristics of the input signal, the multi-layer one-dimensional convolutional neural network was used to learn time-frequency information in EEG signals under different motor imagery states, and automatically complete the feature extraction and classification. The proposed method was compared with a variety of methods on the public dataset and the classification of the actual collected data was completed. The proposed method was used to do end-to-end learning of EEG signals without prior knowledge. Experimental results show that the method can obtain higher multi-classification accuracy and reduce the impact of individual differences on classification. The proposed method is conducive to the development of MI-based brain-computer interface systems.