Visually Evoked Potential for EEG Biometrics using Convolutional Neural Network

被引：0
|
作者：
Das, Rig [1 ]
Maiorana, Emanuele [1 ]
Campisi, Patrizio [1 ]
机构：
[1] Roma Tre Univ, Dept Engn, Sect Appl Elect, Via Vito Volterra 62, I-00146 Rome, Italy
来源：
2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2017年
关键词：
Electroencephalography; Visually evoked potential; Convolutional neural network; CLASSIFICATION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper we investigate the performance of electroencephalographic (EEG) signals, elicited by means of visual stimuli, for biometric identification. A deep learning method such as convolutional neural network (CNN), is used for automatic discriminative feature extraction and individual identification. Experiments are performed on a longitudinal database comprising of EEG data acquired from 40 subjects over two distinct sessions separated by a week time. The experimental results testify the existence of repeatable discriminative characteristics in individuals' EEG signals.
引用
收藏
页码：951 / 955
页数：5
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