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
下载
收藏
页码:273 / +
页数:2
相关论文
共 50 条
  • [31] The element of user training for SSVEP-based BCI
    Szalowski, Artur
    Picovici, Dorel
    2019 30TH IRISH SIGNALS AND SYSTEMS CONFERENCE (ISSC), 2019,
  • [32] Boggle: An SSVEP-Based BCI Web Browser
    Camilleri, Alison
    Porter, Chris
    Camilleri, Tracey
    COMPUTER-HUMAN INTERACTION RESEARCH AND APPLICATIONS, CHIRA 2020, 2022, 1609 : 100 - 123
  • [33] Finding Optimal Frequency and Spatial Filters Accompanying Blind Signal Separation of EEG Data for SSVEP-based BCI
    Jukiewicz, Marcin
    Buchwald, Mikolaj
    Cysewska-Sobusiak, Anna
    INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS, 2018, 64 (04) : 439 - 444
  • [34] Control of an electrical prosthesis with an SSVEP-based BCI
    Mueller-Putz, Gernot R.
    Pfurtscheller, Gert
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2008, 55 (01) : 361 - 364
  • [35] A new SSVEP-based BCI utilizing frequency and space to encode visual targets
    Zhang, Min
    Wang, Zhenyu
    Hu, Honglin
    SCIENCE CHINA-INFORMATION SCIENCES, 2020, 63 (08)
  • [36] Frequency Detection for SSVEP-Based BCI using Deep Canonical Correlation Analysis
    Vu, Hanh
    Koo, Bonkon
    Choi, Seungjin
    2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2016, : 1983 - 1987
  • [37] Comparison between dry and wet EEG electrodes in an SSVEP-based BCI for robot navigation
    Samara, Maria
    Farmaki, Cristina
    Zacharioudakis, Nikolaos
    Pediaditis, Matthew
    Krana, Myrto
    Sakkalis, Vangelis
    2022 IEEE 22ND INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE 2022), 2022, : 333 - 338
  • [38] A new SSVEP-based BCI utilizing frequency and space to encode visual targets
    Min ZHANG
    Zhenyu WANG
    Honglin HU
    Science China(Information Sciences), 2020, 63 (08) : 257 - 259
  • [39] Fatigue Evaluation Using Multi-Scale Entropy of EEG in SSVEP-Based BCI
    Peng, Yufan
    Wong, Chi Man
    Wang, Ze
    Wan, Feng
    Vai, Mang, I
    Mak, Peng Un
    Hu, Yong
    Rosa, Agostinho C.
    IEEE ACCESS, 2019, 7 : 108200 - 108210
  • [40] FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS
    Zhang, Yu
    Zhou, Guoxu
    Jin, Jing
    Wang, Xingyu
    Cichocki, Andrzej
    INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2014, 24 (04)