The Efficiency of the Brain-Computer Interfaces Based on Motor Imagery with Tactile and Visual Feedback

被引:17
|
作者
Lukoyanov M.V. [1 ,3 ]
Gordleeva S.Y. [1 ]
Pimashkin A.S. [1 ]
Grigor’ev N.A. [1 ]
Savosenkov A.V. [1 ]
Motailo A. [1 ]
Kazantsev V.B. [1 ]
Kaplan A.Y. [1 ,2 ]
机构
[1] Lobachevskii Nizhny Novgorod State University, Nizhny Novgorod
[2] Moscow State University, Moscow
[3] Nizhny Novgorod State Medical Academy, Nizhny Novgorod
基金
俄罗斯科学基金会;
关键词
brain–computer interface; EEG; motor imagery; mu rhythm; pattern classification; rehabilitation; sensorimotor; stroke;
D O I
10.1134/S0362119718030088
中图分类号
学科分类号
摘要
In this study we compared tactile and visual feedbacks for the motor imagery-based brain–computer interface (BCI) in five healthy subjects. A vertical green bar from the center of the fixing cross to the edge of the screen was used as visual feedback. Vibration motors that were placed on the forearms of the right and the left hands and on the back of the subject’s neck were used as tactile feedback. A vibration signal was used to confirm the correct classification of the EEG patterns of the motor imagery of right and left hand movements and the rest task. The accuracy of recognition in the classification of the three states (right hand movement, left hand movement, and rest) in the BCI without feedback exceeded the random level (33% for the three states) for all the subjects and was rather high (67.8% ± 13.4% (mean ± standard deviation)). Including the visual and tactile feedback in the BCI did not significantly change the mean accuracy of recognition of mental states for all the subjects (70.5% ± 14.8% for the visual feedback and 65.9% ± 12.4% for the tactile feedback). The analysis of the dynamics of the movement imagery skill in BCI users with the tactile and visual feedback showed no significant differences between these types of feedback. Thus, it has been found that the tactile feedback can be used in the motor imagery-based BCI instead of the commonly used visual feedback, which greatly expands the possibilities of the practical application of the BCI. © 2018, Pleiades Publishing, Inc.
引用
收藏
页码:280 / 288
页数:8
相关论文
共 50 条
  • [31] A novel strategy for driving car brain-computer interfaces: Discrimination of EEG-based visual-motor imagery
    Zhou, Zhouzhou
    Gong, Anmin
    Qian, Qian
    Su, Lei
    Zhao, Lei
    Fu, Yunfa
    [J]. TRANSLATIONAL NEUROSCIENCE, 2021, 12 (01) : 482 - 493
  • [32] Motor imagery based brain-computer interface: a study of the effect of positive and negative feedback
    Gonzalez-Franco, Mar
    Yuan, Peng
    Zhang, Dan
    Hong, Bo
    Gao, Shangkai
    [J]. 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2011, : 6323 - 6326
  • [33] Achieving a hybrid brain-computer interface with tactile selective attention and motor imagery
    Ahn, Sangtae
    Ahn, Minkyu
    Cho, Hohyun
    Jun, Sung Chan
    [J]. JOURNAL OF NEURAL ENGINEERING, 2014, 11 (06)
  • [34] Feature Extraction and Classification of Motor Imagery EEG Signals in Motor Imagery for Sustainable Brain-Computer Interfaces
    Lu, Yuyi
    Wang, Wenbo
    Lian, Baosheng
    He, Chencheng
    [J]. SUSTAINABILITY, 2024, 16 (15)
  • [35] A Motor Imagery Based Brain-Computer Interface Speller
    Xia, Bin
    Yang, Jing
    Cheng, Conghui
    Xie, Hong
    [J]. ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT II, 2013, 7903 : 413 - 421
  • [36] Biased feedback in brain-computer interfaces
    Barbero, Alvaro
    Grosse-Wentrup, Moritz
    [J]. JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2010, 7
  • [37] Biased feedback in brain-computer interfaces
    Álvaro Barbero
    Moritz Grosse-Wentrup
    [J]. Journal of NeuroEngineering and Rehabilitation, 7
  • [38] A protocol for Brain-Computer Interfaces based on Musical Notes Imagery
    Montevilla, Anna
    Sahonero-Alvarez, Guillermo
    [J]. 2021 IEEE LATIN AMERICAN CONFERENCE ON COMPUTATIONAL INTELLIGENCE (LA-CCI), 2021,
  • [39] Privacy-Preserving Domain Adaptation for Motor Imagery-Based Brain-Computer Interfaces
    Xia, Kun
    Deng, Lingfei
    Duch, Wlodzislaw
    Wu, Dongrui
    [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2022, 69 (11) : 3365 - 3376
  • [40] Advanced TSGL-EEGNet for Motor Imagery EEG-Based Brain-Computer Interfaces
    Deng, Xin
    Zhang, Boxian
    Yu, Nian
    Liu, Ke
    Sun, Kaiwei
    [J]. IEEE ACCESS, 2021, 9 : 25118 - 25130