The Use of fMRI for the Evaluation of the Effect of Training in Motor Imagery BCI Users

被引:0
|
作者
Slenes, Gabriel F. [1 ]
Beltramini, Guilherme C. [2 ]
Lima, Fabricio O.
Li, Li M. [3 ]
Castellano, Gabriela [2 ]
机构
[1] Univ Campinas UNICAMP, Med Sci Sch FCM, Campinas, SP, Brazil
[2] Univ Campinas UNICAMP, IFGW, Campinas, SP, Brazil
[3] Univ Campinas UNICAMP, FCM, Campinas, SP, Brazil
基金
巴西圣保罗研究基金会;
关键词
BRAIN; INTERFACES;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The development of brain computer interfaces (BCIs) for patient rehabilitation is a growing field of research. The BCI experimental paradigms consist mainly of selective attention BCI models and motor imagery (MI) BCIs. Selective attention models require an external stimulus (screen) but achieve high rates of classification accuracy fairly quickly. MI systems do not require external stimuli but require extensive training. The goal of our study was to gauge how much a short training paradigm requiring seven six-minute sessions of video attention would change fMRI BOLD activity between two sessions of MI - one acquired before and another after the training protocol took place. The study used four MIs: 1) right hand 2) left hand 3) feet 4) tongue; and it was carried out on ten subjects. We found an increase of the BOLD response after training, both in amplitude and spatial extent, for the majority (6 out of 10) of subjects and MIs (all except left hand). Our results corroborate other literature results regarding the effect of training in MI based BCIs.
引用
收藏
页码:686 / 690
页数:5
相关论文
共 50 条
  • [31] Motor imagery performance evaluation using hybrid EEG-NIRS for BCI
    Khan, M. Jawad
    Hong, Keum-Shik
    Naseer, Noman
    Bhutta, M. Raheel
    2015 54TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE), 2015, : 1150 - 1155
  • [32] The facilitating effect of clinical hypnosis on motor imagery: An fMRI study
    Mueller, Katharina
    Bacht, Katrin
    Schramm, Stephanie
    Seitz, Ruediger J.
    BEHAVIOURAL BRAIN RESEARCH, 2012, 231 (01) : 164 - 169
  • [33] Temperament Predictors of Motor Imagery Control in BCI
    Zapala, Dariusz
    Malkiewicz, Monika
    Francuz, Piotr
    Kolodziej, Marcin
    Majkowski, Andrzej
    JOURNAL OF PSYCHOPHYSIOLOGY, 2020, 34 (04) : 246 - 254
  • [34] Motor Imagery Based BCI for a Maze Game
    Bordoloi, Simanta
    Sharmah, Ujjal
    Hazarika, Shyamanta M.
    4TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN COMPUTER INTERACTION (IHCI 2012), 2012,
  • [35] Online motor-imagery based BCI
    Dolezal, J.
    Cerny, V.
    St'astny, J.
    2012 INTERNATIONAL CONFERENCE ON APPLIED ELECTRONICS, 2012, : 65 - 68
  • [36] Multiclass Motor Imagery Classification for BCI Application
    Rashid, Md. Mamun Or
    Ahmad, Mohiuddin
    2016 INTERNATIONAL WORKSHOP ON COMPUTATIONAL INTELLIGENCE (IWCI), 2016, : 35 - 40
  • [37] EEG Classification for Multiclass Motor Imagery BCI
    Liu, Chong
    Wang, Hong
    Lu, Zhiguo
    2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2013, : 4450 - 4453
  • [38] Effect of biased feedback on motor imagery learning in BCI-teleoperation system
    Alimardani, Maryam
    Nishio, Shuichi
    Ishiguro, Hiroshi
    FRONTIERS IN SYSTEMS NEUROSCIENCE, 2014, 8
  • [39] Analysis and classification of hybrid BCI based on motor imagery and speech imagery
    Wang, Li
    Liu, Xiaochu
    Liang, Zhongwei
    Yang, Zhao
    Hu, Xiao
    MEASUREMENT, 2019, 147
  • [40] Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI
    Jin, Jing
    Fang, Hua
    Daly, Ian
    Xiao, Ruocheng
    Miao, Yangyang
    Wang, Xingyu
    Cichocki, Andrzej
    INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2021, 31 (07)