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
相关论文
共 50 条
  • [1] MOTOR IMAGERY FOR EEG BIOMETRICS USING CONVOLUTIONAL NEURAL NETWORK
    Das, Rig
    Maiorana, Emanuele
    Campisi, Patrizio
    2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, : 2062 - 2066
  • [2] Attention-Based Parallel Multiscale Convolutional Neural Network for Visual Evoked Potentials EEG Classification
    Gao, Zhongke
    Sun, Xinlin
    Liu, Mingxu
    Dang, Weidong
    Ma, Chao
    Chen, Guanrong
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2021, 25 (08) : 2887 - 2894
  • [3] Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain
    Wei, Zuochen
    Zou, Junzhong
    Zhang, Jian
    Xu, Jianqiang
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2019, 53
  • [4] Automated diagnosis of schizophrenia using EEG microstates and Deep Convolutional Neural Network
    Lillo, Eric
    Mora, Marco
    Lucero, Boris
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 209
  • [5] Estimating Similarity Between Individual EEG Datasets Using a Convolutional Neural Network
    Kozyrskiy, Bogdan L.
    Ovchinnikova, Anastasia O.
    Shishkin, Sergei L.
    2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 96 - 101
  • [6] EEG-based mild depression recognition using convolutional neural network
    Xiaowei Li
    Rong La
    Ying Wang
    Junhong Niu
    Shuai Zeng
    Shuting Sun
    Jing Zhu
    Medical & Biological Engineering & Computing, 2019, 57 : 1341 - 1352
  • [7] Comparison of Motor Imagery EEG Classification using Feedforward and Convolutional Neural Network
    Majoros, Tamas
    Oniga, Stefan
    IEEE EUROCON 2021 - 19TH INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES, 2021, : 25 - 29
  • [8] Scalp EEG-Based Pain Detection Using Convolutional Neural Network
    Chen, Duo
    Zhang, Haihong
    Kavitha, Perumpadappil Thomas
    Loy, Fong Ling
    Ng, Soon Huat
    Wang, Chuanchu
    Phua, Kok Soon
    Tjan, Soon Yin
    Yang, Su-Yin
    Guan, Cuntai
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2022, 30 : 274 - 285
  • [9] EEG-based mild depression recognition using convolutional neural network
    Li, Xiaowei
    La, Rong
    Wang, Ying
    Niu, Junhong
    Zeng, Shuai
    Sun, Shuting
    Zhu, Jing
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2019, 57 (06) : 1341 - 1352
  • [10] Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG
    Moghaddari, Majid
    Lighvan, Mina Zolfy
    Danishvar, Sebelan
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2020, 197
12345