中南民族大学学报（自然科学版）Issue(4)：85-89,93,6.
非监督特征学习方法在脑电身份识别中的应用
Unsupervised Feature Learning Method with Application to EEG signal based Personal Identification
摘要
Abstract
The multi-ganglion BP neural network based feature learning method, a kind of unsupervised methods, is applied to the feature extraction procedure of Imitating-Reading EEG based personal identification system.Five subjects participated in the Imitating-Reading ERP experiments.The dataset of each subject contains 400 trials of eight channel ( PO3, O1, Oz, O2, PO4, P4, P8, CP6 ) EEG signals ranging from 100ms to 400ms after the subject receiving target stimuli.The multi-ganglion BP neural network, which consists of six relative small-scale auto-encoders, is applied to extract the feature vectors from single-trial EEG signals and two, five, ten-trial averaging EEG signals respectively.The classification procedure is performed by support vector machine and the classification accuracy of the subjects exceeds 90%, when using five-trial averaging samples, considerably higher than using single-channel temporal feature extraction method.This study provides an unsupervised feature learning method for the application of EEG based personal identification system.关键词
模拟阅读/脑电信号特征提取/非监督特征学习/身份识别Key words
imitating-reading ERP/EEG feature extraction/unsupervised feature learning method/personal identification分类
信息技术与安全科学引用本文复制引用
官金安,高炜,周到,高军峰..非监督特征学习方法在脑电身份识别中的应用[J].中南民族大学学报（自然科学版）,2014,(4):85-89,93,6.基金项目
国家自然科学基金资助项目（91120017）；国家自然科学基金资助项目（81271659）；中央高校基本科研业务费资助项目 （）
