Performance variation in motor imagery brain-computer interface: A brief review

被引:224
作者
Ahn, Minkyu [1 ]
Jun, Sung Chan [2 ]
机构
[1] Brown Univ, Dept Neurosci, Providence, RI 02912 USA
[2] Gwangju Inst Sci & Technol, Sch Informat & Commun, Kwangju 500712, South Korea
基金
新加坡国家研究基金会;
关键词
Brain computer interface; BCI-illiteracy; Performance variation; Prediction; Motor imagery; ALPHA; CLASSIFICATION; COMMUNICATION; OSCILLATIONS; POTENTIALS; PEOPLE; POWER;
D O I
10.1016/j.jneumeth.2015.01.033
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Brain-computer interface (BCI) technology has attracted significant attention over recent decades, and has made remarkable progress. However, BCI still faces a critical hurdle, in that performance varies greatly across and even within subjects, an obstacle that degrades the reliability of BCI systems. Understanding the causes of these problems is important if we are to create more stable systems. In this short review, we report the most recent studies and findings on performance variation, especially in motor imagery-based BCI, which has found that low-performance groups have a less-developed brain network that is incapable of motor imagery. Further, psychological and physiological states influence performance variation within subjects. We propose a possible strategic approach to deal with this variation, which may contribute to improving the reliability of BCI. In addition, the limitations of current work and opportunities for future studies are discussed. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:103 / 110
页数:8
相关论文
共 79 条
  • [11] A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals
    Bashashati, Ali
    Fatourechi, Mehrdad
    Ward, Rabab K.
    Birch, Gary E.
    [J]. JOURNAL OF NEURAL ENGINEERING, 2007, 4 (02) : R32 - R57
  • [12] A spelling device for the paralysed
    Birbaumer, N
    Ghanayim, N
    Hinterberger, T
    Iversen, I
    Kotchoubey, B
    Kübler, A
    Perelmouter, J
    Taub, E
    Flor, H
    [J]. NATURE, 1999, 398 (6725) : 297 - 298
  • [13] Blankertz B., 2008, Advances in Neural Information Processing Systems, P113
  • [14] Neurophysiological predictor of SMR-based BCI performance
    Blankertz, Benjamin
    Sannelli, Claudia
    Haider, Sebastian
    Hammer, Eva M.
    Kuebler, Andrea
    Mueller, Klaus-Robert
    Curio, Gabriel
    Dickhaus, Thorsten
    [J]. NEUROIMAGE, 2010, 51 (04) : 1303 - 1309
  • [15] Combined EEG-fNIRS Decoding of Motor Attempt and Imagery for Brain Switch Control: An Offline Study in Patients With Tetraplegia
    Blokland, Yvonne
    Spyrou, Loukianos
    Thijssen, Dick
    Eijsvogels, Thijs
    Colier, Willy
    Floor-Westerdijk, Marianne
    Vlek, Rutger
    Bruhn, Jorgen
    Farquhar, Jason
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2014, 22 (02) : 222 - 229
  • [16] Brunner C., 2008, Int. J. Bioelectromagn, V10, P52, DOI DOI 10.1.1.330.3349
  • [17] Burde W., 2006, 3 INT BRAIN COMPUT I, P108
  • [18] A brain-computer interface with vibrotactile biofeedback for haptic information
    Chatterjee, Aniruddha
    Aggarwal, Vikram
    Ramos, Ander
    Acharya, Soumyadipta
    Thakor, Nitish V.
    [J]. JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2007, 4 (1)
  • [19] Cho H., 2012, P 3 TOBI WORKSHOP, P31
  • [20] The functional significance of absolute power with respect to event-related desynchronization
    Doppelmayr, MM
    Klimesch, W
    Pachinger, T
    Ripper, B
    [J]. BRAIN TOPOGRAPHY, 1998, 11 (02) : 133 - 140