A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies

被引:35
作者
Chen, Xiaogang [1 ,2 ,3 ]
Wang, Yijun [4 ]
Zhang, Shangen [3 ]
Gao, Shangkai [3 ]
Hu, Yong [1 ,2 ,5 ]
Gao, Xiaorong [3 ]
机构
[1] Chinese Acad Med Sci, Inst Biomed Engn, Tianjin 300192, Peoples R China
[2] Peking Union Med Coll, Tianjin 300192, Peoples R China
[3] Tsinghua Univ, Sch Med, Dept Biomed Engn, Beijing 100084, Peoples R China
[4] Chinese Acad Sci, Inst Semicond, State Key Lab Integrated Optoelect, Beijing 100083, Peoples R China
[5] Univ Hong Kong, Dept Orthopaed & Traumatol, Pokfulam, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
steady-state visual evoked potential; brain-computer interface; intermodulation frequencies; luminance; chromatic; COMPUTER; RESPONSES;
D O I
10.1088/1741-2552/aa5989
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its easy system configuration, high information transfer rate (ITR) and little user training. However, due to the limitations of brain responses and the refresh rate of a monitor, the available stimulation frequencies for practical BCI application are generally restricted. Approach. This study introduced a novel stimulation method using intermodulation frequencies for SSVEP-BCIs that had targets flickering at the same frequency but with different additional modulation frequencies. The additional modulation frequencies were generated on the basis of choosing desired flickering frequencies. The conventional frame-based 'on/off' stimulation method was used to realize the desired flickering frequencies. All visual stimulation was present on a conventional LCD screen. A 9-target SSVEP-BCI based on intermodulation frequencies was implemented for performance evaluation. To optimize the stimulation design, three approaches (C: chromatic; L: luminance; CL: chromatic and luminance) were evaluated by online testing and offline analysis. Main results. SSVEP-BCIs with different paradigms (C, L, and CL) enabled us not only to encode more targets, but also to reliably evoke intermodulation frequencies. The online accuracies for the three paradigms were 91.67% (C), 93.98% (L), and 96.41% (CL). The CL condition achieved the highest classification performance. Significance. These results demonstrated the efficacy of three approaches (C, L, and CL) for eliciting intermodulation frequencies for multiclass SSVEP-BCIs. The combination of chromatic and luminance characteristics of the visual stimuli is the most efficient way for the intermodulation frequency coding method.
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页数:9
相关论文
共 30 条
  • [1] [Anonymous], P 48 ACM SE C OXF MI
  • [2] Figure-ground interaction in the human visual cortex
    Appelbaum, Lawrence G.
    Wade, Alex R.
    Pettet, Mark W.
    Vildavski, Vladimir Y.
    Norcia, Anthony M.
    [J]. JOURNAL OF VISION, 2008, 8 (09):
  • [3] An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method
    Bin, Guangyu
    Gao, Xiaorong
    Yan, Zheng
    Hong, Bo
    Gao, Shangkai
    [J]. JOURNAL OF NEURAL ENGINEERING, 2009, 6 (04)
  • [4] The psychophysics toolbox
    Brainard, DH
    [J]. SPATIAL VISION, 1997, 10 (04): : 433 - 436
  • [5] High-speed spelling with a noninvasive brain-computer interface
    Chen, Xiaogang
    Wang, Yijun
    Nakanishi, Masaki
    Gao, Xiaorong
    Jung, Tzyy-Ping
    Gao, Shangkai
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2015, 112 (44) : E6058 - E6067
  • [6] 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
  • [7] Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface
    Chen, Xiaogang
    Wang, Yijun
    Gao, Shangkai
    Jung, Tzyy-Ping
    Gao, Xiaorong
    [J]. JOURNAL OF NEURAL ENGINEERING, 2015, 12 (04)
  • [8] Brain-computer interface based on intermodulation frequency
    Chen, Xiaogang
    Chen, Zhikai
    Gao, Shangkai
    Gao, Xiaorong
    [J]. JOURNAL OF NEURAL ENGINEERING, 2013, 10 (06)
  • [9] Specialized color modules in macaque extrastriate cortex
    Conway, Bevil R.
    Moeller, Sebastian
    Tsao, Doris Y.
    [J]. NEURON, 2007, 56 (03) : 560 - 573
  • [10] Visual and Auditory Brain-Computer Interfaces
    Gao, Shangkai
    Wang, Yijun
    Gao, Xiaorong
    Hong, Bo
    [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2014, 61 (05) : 1436 - 1447