基于深度信念网络的运动想象脑电信号识别

Recognition of Motor Imagery EEG Based on Deep Belief Network

目前脑电信号的识别中,特征提取和分类是分开独立完成的.为了简化识别过程和提高识别效果,本文提出一种运用深度信念网络(DBN)进行脑电信号识别的方法.DBN模型由多层RBM(restricted Boltzmann machine)网络和一层BP(backpropagation)网络构成,通过RBM实现对脑电信号的分层特征提取,以确保获得最优特征向量,再由一层BP网络对RBM输出的特征向量进行分类,从而实现脑电信号的识别.本文使用Emotiv脑电采集仪采集的运动想象脑电数据进行实验.实验表明,深度信念网络对粗糙的脑电数据具有强大的特征学习能力,对运动想象脑电信号的识别率优于支持向量机(SVM),简化了脑电识别流程,提高了识别率,为脑—机接口中脑电信号的识别提供了一种新颖的思路.

Currently, feature extraction and classification are carried out separately in electroencephalogram （EEG） recognition. To simplify the identification process and improve the classification results, we propose a novel method that applies a deep belief network（DBN） to EEG recognition. Deep belief networks consist of several layers of restricted Bohzmann machines （RBMs） and one layer of a back-propagation network （BP）. In the process of EEG recognition, RBNIs are used to extract features from the EEG data to ensure that the most effective feature vectors are obtained. These feature vectors are then classified by the BP network. We collected motor imagery EEG data with an Emotiv EEG acquisition instrument, and used them to design the experiment. The experimental results show that DBNs have a strong ability for feature learning from raw EEG data. The recognition rate of motor imagery EEG data based on a DBN is better than that from a support vector machine （SVM）. The proposed method simplifies the EEG recognition process, improves the recognition rate, and provides a novel technique for the brain computer interface（BCI） recognition of EEG signals.