A Novel Combination of Time Phase and EEG Frequency Components for SSVEP-Based BCI

被引:0
|
作者
Jin, Jing [1 ]
Zhang, Yu [1 ,2 ]
Wang, Xingyu [1 ]
机构
[1] East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China
[2] RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama, Japan
来源
关键词
Brain-computer interfaces (BCIs); Electroencephalogram (EEG); Steady-state visual evoked potential (SSVEP); Time phase; BRAIN-COMPUTER INTERFACE;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The steady-state visual evoked potential (SSVEP) has been widely applied in brain-computer interfaces (BCIs), such as letter or icon selection and device control. Most of these BCIs used different flickering frequencies to evoke SSVEP with different frequency components that were used as control commands. In this paper, a novel method combining the time phase and EEG frequency components is presented and validated with nine healthy subjects. In this method, four different frequency components of EEG were classified out from four time phases. When the SSVEP is evoked and what is the frequency of the SSVEP is determined by the linear discriminant analysis (LDA) classifier in the same time to locate the target image. The results from offline analysis show that this method yields good performance both in classification accuracy and information transfer rate (ITR).
引用
收藏
页码:273 / +
页数:2
相关论文
共 50 条
  • [1] A Study on SSVEP-Based BCI
    ZhengHua Wu is with School of Computer Science EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina DeZhong Yao is with the Key Laboratory for NeuroInformation of Ministry of EducationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
    [J]. Journal of Electronic Science and Technology of China, 2009, 7 (01) : 7 - 11
  • [2] A Study on SSVEP-Based BCI
    Zheng-Hua Wu is with School of Computer Science Engineering
    [J]. Journal of Electronic Science and Technology, 2009, 7 (01) : 7 - 11
  • [3] Subject-Specific Methodology in the Frequency Scanning Phase of SSVEP-Based BCI
    Rejer, Izabela
    Cieszynski, Lukasz
    [J]. HARD AND SOFT COMPUTING FOR ARTIFICIAL INTELLIGENCE, MULTIMEDIA AND SECURITY, 2017, 534 : 123 - 132
  • [4] A phase-coding framework for SSVEP-based BCI
    Yang, Qiuling
    Wu, Pingdong
    [J]. SIXTH INTERNATIONAL CONFERENCE ON ELECTRONICS AND INFORMATION ENGINEERING, 2015, 9794
  • [5] Fusing Canonical Coefficients for Frequency Recognition in SSVEP-Based BCI
    Liu, Tiejun
    Zhang, Yangsong
    Wang, Lu
    Li, Jianfu
    Xu, Peng
    Yao, Dezhong
    [J]. IEEE ACCESS, 2019, 7 : 52467 - 52472
  • [6] Novel Moisture Retention Sponge Electrodes for Developing a Wireless EEG SSVEP-based BCI System
    Ko, Li-Wei
    Chang, Yang
    Wu, Pei-Lun
    Lu, Yi-Chen
    Yeh, Chia-Lung
    Chen, Yun-Ju
    [J]. 2018 INTERNATIONAL AUTOMATIC CONTROL CONFERENCE (CACS), 2018,
  • [7] Hybrid Frequency and Phase Coding for a High-Speed SSVEP-Based BCI Speller
    Chen, Xiaogang
    Wang, Yijun
    Nakanishi, Masaki
    Jung, Tzyy-Ping
    Gao, Xiaorong
    [J]. 2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2014, : 3993 - 3996
  • [8] Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm
    Chen, Xiaogang
    Zhao, Bing
    Wang, Yijun
    Gao, Xiaorong
    [J]. JOURNAL OF NEURAL ENGINEERING, 2019, 16 (02)
  • [9] Stimulator selection in SSVEP-based BCI
    Wu, Zhenghua
    Lai, Yongxiu
    Xia, Yang
    Wu, Dan
    Yao, Dezhong
    [J]. MEDICAL ENGINEERING & PHYSICS, 2008, 30 (08) : 1079 - 1088
  • [10] An Error Aware SSVEP-based BCI
    Kalaganis, Fotis
    Chatzilari, Elisavet
    Georgiadis, Kostas
    Nikolopoulos, Spiros
    Laskaris, Nikos
    Kompatsiaris, Yiannis
    [J]. 2017 IEEE 30TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), 2017, : 775 - 780