Subject-Specific Methodology in the Frequency Scanning Phase of SSVEP-Based BCI

被引:1
|
作者
Rejer, Izabela [1 ]
Cieszynski, Lukasz [1 ]
机构
[1] West Pomeranian Univ Technol Szczecin, Fac Comp Sci & Informat Technol, Szczecin, Poland
关键词
SSVEP; BCI; Brain Computer Interface; EEG;
D O I
10.1007/978-3-319-48429-7_12
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Steady State Visual Evoked Potentials (SSVEPs) often used in Brain Computer Interfaces (BCIs) differ across subjects. That is why a new SSVEP-based BCI user should always start the session from the frequency scanning phase. During this phase the stimulation frequencies evoking the most prominent SSVEPs are determined. In our opinion not only the stimulation frequencies specific for the given user should be chosen in the scanning phase but also the methodology used for SSVEP detection. The paper reports the results of a survey whose aim was to find out whether using subject specific methodology for identifying stimulation frequencies would increase the number of frequencies found. We analyzed three factors: length of time window used for power spectrum calculation, combination of channels, and number of harmonics used for SSVEP detection. According to the outcome of the experiment (performed with 6 subjects) the mean drop in the number of SSVEPs detected with any other but the best combination of factors was very large for all subjects (from 31.52 % for subject S3 to 51.76 % for subject S4).
引用
收藏
页码:123 / 132
页数:10
相关论文
共 50 条
  • [41] Calibration-free SSVEP-based BCI Switch
    Sastry, R., V
    Karthik, S.
    Adithya, R.
    Ravi, Aravind
    Indrapriyadarsini, S.
    Panwar, Gagandeep
    Ramakrishnan, A. G.
    [J]. 2019 IEEE 16TH INDIA COUNCIL INTERNATIONAL CONFERENCE (IEEE INDICON 2019), 2019,
  • [42] Subject-independent, SSVEP-based BCI: trading off among accuracy, responsiveness and complexity
    Mora, N.
    De Munari, I.
    Ciampolini, P.
    [J]. 2015 7TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2015, : 146 - 149
  • [43] Using Modular Neural Network to SSVEP-based BCI
    Chen, Yeou-Jiunn
    Chen, Shih-Chung
    Wu, Chung-Min
    [J]. PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION (ICASI), 2016,
  • [44] Control of the robotic arm system with an SSVEP-based BCI
    Fu, Rongrong
    Feng, Xiaolei
    Wang, Shiwei
    Shi, Ye
    Jia, Chengcheng
    Zhao, Jing
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2024, 35 (05)
  • [45] A comprehensive benchmark dataset for SSVEP-based hybrid BCI
    Sadeghi, Sahar
    Maleki, Ali
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2022, 200
  • [46] The Effect of Harmonics Count on SSVEP-Based BCI Results
    Kancaoglu, Murat
    Kuntalp, Mehmet
    [J]. 2019 INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS CONFERENCE (ASYU), 2019, : 110 - 113
  • [47] Information Bottleneck as Optimisation Method for SSVEP-Based BCI
    Ingel, Anti
    Vicente, Raul
    [J]. FRONTIERS IN HUMAN NEUROSCIENCE, 2021, 15
  • [48] A high-ITR SSVEP-based BCI speller
    Chen, Xiaogang
    Chen, Zhikai
    Gao, Shangkai
    Gao, Xiaorong
    [J]. BRAIN-COMPUTER INTERFACES, 2014, 1 (3-4) : 181 - 191
  • [49] An SSVEP-Based BCI System for SMS in a Mobile Phone
    Lin, Jzau-Sheng
    Wang, Mei
    Lia, Pei-Yu
    Li, Zejin
    [J]. APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY, 2014, 513-517 : 412 - 415
  • [50] A multimodal approach to estimating vigilance in SSVEP-based BCI
    Wang, Kangning
    Qiu, Shuang
    Wei, Wei
    Zhang, Yukun
    Wang, Shengpei
    He, Huiguang
    Xu, Minpeng
    Jung, Tzyy-Ping
    Ming, Dong
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2023, 225