EEG Artifacts Handling in a Real Practical Brain-Computer Interface Controlled Vehicle

被引:34
作者
Jafarifarmand, Aysa [1 ]
Badamchizadeh, Mohammad Ali [1 ]
机构
[1] Univ Tabriz, Fac Elect & Comp Engn, Tabriz 5165677861, Iran
关键词
Brain-computer interface (BCI); electroencephalogram (EEG); motor imagery (MI); artifacts; BCI-based RC car; MOTOR IMAGERY; SPEED;
D O I
10.1109/TNSRE.2019.2915801
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
One of the main issues restricting the practical efficiency of brain-computer interface (BCI) systems is the inevitable occurrence of physiological artifacts during electroencephalography (EEG) recordings. The effects of the artifacts are, however, mostly discarded in practical BCI systems, due to the time-consuming and complicated computational processes. This paper presents the influences of the artifacts and the efficiency of reducing these influences in a practical BCI. Ocular and muscular artifacts are considered due to the high-amplitude and frequent presence. The paradigm is designed based on the mental controlling of a radio-control (RC) car. Two motor imagery commands, containing the imagination of movement of left/right hand, are used to navigate the BCI-based RC car to turn left/right. The results indicate that the artifacts can highly affect the system performance; reducing their influence significantly improves the efficiency.
引用
收藏
页码:1200 / 1208
页数:9
相关论文
共 31 条
[1]   Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface [J].
Batula, Alyssa M. ;
Kim, Youngmoo E. ;
Ayaz, Hasan .
BIOMED RESEARCH INTERNATIONAL, 2017, 2017
[2]   EEG-Based Brain-Controlled Mobile Robots: A Survey [J].
Bi, Luzheng ;
Fan, Xin-An ;
Liu, Yili .
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2013, 43 (02) :161-176
[3]   Improving Motor Imagery Classification With a New BCI Design Using Neuro-Fuzzy S-dFasArt [J].
Cano-Izquierdo, Jose-Manuel ;
Ibarrola, Julio ;
Almonacid, Miguel .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2012, 20 (01) :2-7
[4]   The Removal of Ocular Artifacts from EEG Signals Using Adaptive Filters Based on Ocular Source Components [J].
Chan, Hsiao-Lung ;
Tsai, Yu-Tai ;
Meng, Ling-Fu ;
Wu, Tony .
ANNALS OF BIOMEDICAL ENGINEERING, 2010, 38 (11) :3489-3499
[5]   High-speed spelling with a noninvasive brain-computer interface [J].
Chen, Xiaogang ;
Wang, Yijun ;
Nakanishi, Masaki ;
Gao, Xiaorong ;
Jung, Tzyy-Ping ;
Gao, Shangkai .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2015, 112 (44) :E6058-E6067
[6]  
Coyle D., 2011, Proceedings of 2011 IEEE Symposium on Computational Intelligence,Cognitive Algorithms, Mind, and Brain, P1, DOI [DOI 10.1109/CCMB.2011.5952128, 10.1109/CCMB.2011.5952128]
[7]   A Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System for Handling Nonstationarities in EEG Signals for BCI [J].
Das, Ankit Kumar ;
Sundaram, Suresh ;
Sundararajan, Narasimhan .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2016, 24 (06) :1565-1577
[8]   Towards correlation-based time window selection method for motor imagery BCIs [J].
Feng, Jiankui ;
Yin, Erwei ;
Jin, Jing ;
Saab, Rami ;
Daly, Ian ;
Wang, Xingyu ;
Hu, Dewen ;
Cichocki, Andrzej .
NEURAL NETWORKS, 2018, 102 :87-95
[9]   SVM-based Brain-Machine Interface for controlling a robot arm through four mental tasks [J].
Hortal, E. ;
Planelles, D. ;
Costa, A. ;
Ianez, E. ;
Ubeda, A. ;
Azorin, J. M. ;
Fernandez, E. .
NEUROCOMPUTING, 2015, 151 :116-121
[10]   Electroencephalography (EEG)-Based Brain-Computer Interface (BCI): A 2-D Virtual Wheelchair Control Based on Event-Related Desynchronization/Synchronization and State Control [J].
Huang, Dandan ;
Qian, Kai ;
Fei, Ding-Yu ;
Jia, Wenchuan ;
Chen, Xuedong ;
Bai, Ou .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2012, 20 (03) :379-388