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
  • [21] The Impact of Different Visual Feedbacks in User Training on Motor Imagery Control in BCI
    Zapala, Dariusz
    Francuz, Piotr
    Zapala, Ewelina
    Kopis, Natalia
    Wierzgala, Piotr
    Augustynowicz, Pawel
    Majkowski, Andrzej
    Kolodziej, Marcin
    APPLIED PSYCHOPHYSIOLOGY AND BIOFEEDBACK, 2018, 43 (01) : 23 - 35
  • [22] Estimation of optimal location of EEG reference electrode for motor imagery based BCI using fMRI
    Choi, Sang Han
    Lee, Minho
    Wang, Yijun
    Hong, Bo
    2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15, 2006, : 3439 - +
  • [23] Enhancement of cortical activation for motor imagery during BCI-FES training
    Wang, Zhongpeng
    Chen, Long
    Yi, Weibo
    Gu, Bin
    Liu, Shuang
    An, Xingwei
    Xu, Minpeng
    Qi, Hongzhi
    He, Feng
    Wan, Baikun
    Ming, Dong
    2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2018, : 2527 - 2530
  • [24] Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users
    Tibrewal, Navneet
    Leeuwis, Nikki
    Alimardani, Maryam
    PLOS ONE, 2022, 17 (07):
  • [25] Assessing The Relevance Of Neurophysiological Patterns To Predict Motor Imagery-based BCI Users' Performance
    Tzdaka, Eidan
    Benaroch, Camille
    Jeunet, Camille
    Lotte, Fabien
    2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2020, : 2490 - 2495
  • [26] A BCI-Based Vibrotactile Neurofeedback Training Improves Motor Cortical Excitability During Motor Imagery
    Grigorev, Nikita A.
    Savosenkov, Andrey O.
    Lukoyanov, Maksim, V
    Udoratina, Anna
    Shusharina, Natalia N.
    Kaplan, Alexander Ya
    Hramov, Alexander E.
    Kazantsev, Victor B.
    Gordleeva, Susanna
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2021, 29 : 1583 - 1592
  • [27] Neurofeedback-based motor imagery training for brain-computer interface (BCI)
    Hwang, Han-Jeong
    Kwon, Kiwoon
    Im, Chang-Hwang
    JOURNAL OF NEUROSCIENCE METHODS, 2009, 179 (01) : 150 - 156
  • [28] Stimulus Effects on Subject-Specific BCI Classification Training using Motor Imagery
    Miloulis, Stavros Theofanis
    Kakkos, Ioannis
    Karampasi, Aikaterini
    Zorzos, Ioannis
    Ventouras, Errikos-Chaim
    Matsopoulos, George K.
    Asvestas, Panteleimon
    Kalatzis, Ioannis
    2021 INTERNATIONAL CONFERENCE ON E-HEALTH AND BIOENGINEERING (EHB 2021), 9TH EDITION, 2021,
  • [29] Assessment of Neurofeedback Training by means of Motor Imagery based-BCI for Cognitive Rehabilitation
    Gomez-Pilar, J.
    Corralejo, R.
    Nicolas-Alonso, L. F.
    Alvarez, D.
    Hornero, R.
    2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2014, : 3630 - 3633
  • [30] An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
    Duan, Xu
    Xie, Songyun
    Xie, Xinzhou
    Obermayer, Klaus
    Cui, Yujie
    Wang, Zhenzhen
    FRONTIERS IN HUMAN NEUROSCIENCE, 2021, 15