Effect of biased feedback on motor imagery learning in BCI-teleoperation system

被引:49
|
作者
Alimardani, Maryam [1 ,2 ]
Nishio, Shuichi [2 ]
Ishiguro, Hiroshi [1 ,2 ]
机构
[1] Osaka Univ, Grad Sch Engn Sci, Dept Syst Innovat, Osaka, Japan
[2] Adv Telecommun Res Inst Int, 2-2 Hikaridai,Seika-cho,Soraku-gun, Kyoto, Japan
关键词
body ownership illusion; BCI-teleoperation; motor imagery learning; feedback effect; training;
D O I
10.3389/fnsys.2014.00052
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Feedback design is an important issue in motor imagery BCI systems. Regardless, to date it has not been reported how feedback presentation can optimize co-adaptation between a human brain and such systems. This paper assesses the effect of realistic visual feedback on users' BCI performance and motor imagery skills. We previously developed a tele-operation system for a pair of humanlike robotic hands and showed that BCI control of such hands along with first-person perspective visual feedback of movements can arouse a sense of embodiment in the operators. In the first stage of this study, we found that the intensity of this ownership illusion was associated with feedback presentation and subjects' performance during BCI motion control. In the second stage, we probed the effect of positive and negative feedback bias on subjects' BCI performance and motor imagery skills. Although the subject specific classifier, which was set up at the beginning of experiment, detected no significant change in the subjects' online performance, evaluation of brain activity patterns revealed that subjects' self-regulation of motor imagery features improved due to a positive bias of feedback and a possible occurrence of ownership illusion. Our findings suggest that in general training protocols for BCIs, manipulation of feedback can play an important role in the optimization of subjects' motor imagery skills.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] The effect of feedback presentation on motor imagery performance during BCI-teleoperation of a humanlike robot
    Alimardani, Maryam
    Shuichi, Nishio
    Ishiguro, Hiroshi
    2014 5th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), 2014, : 403 - 408
  • [2] Investigating the cortical effect of false positive feedback on motor learning in motor imagery based rehabilitative BCI training
    Jeong, Hojun
    Song, Minsu
    Jang, Sung-Ho
    Kim, Jonghyun
    JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2025, 22 (01)
  • [3] Towards Identifying Optimal Biased Feedback for Various User States and Traits in Motor Imagery BCI
    Mladenovic, Jelena
    Frey, Jeremy
    Pramij, Smeety
    Mattout, Jeremie
    Lotte, Fabien
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2022, 69 (03) : 1101 - 1110
  • [4] Improving motor imagery BCI with user response to feedback
    Mousavi M.
    Koerner A.S.
    Zhang Q.
    Noh E.
    de Sa V.R.
    Brain-Computer Interfaces, 2017, 4 (1-2): : 74 - 86
  • [5] Enhanced Motor Imagery Training Using a Hybrid BCI With Feedback
    Yu, Tianyou
    Xiao, Jun
    Wang, Fangyi
    Zhang, Rui
    Gu, Zhenghui
    Cichocki, Andrzej
    Li, Yuanqing
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2015, 62 (07) : 1706 - 1717
  • [6] Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback
    Xu, Baoguo
    Li, Wenlong
    He, Xiaohang
    Wei, Zhiwei
    Zhang, Dalin
    Wu, Changcheng
    Song, Aiguo
    ELECTRONICS, 2020, 9 (01)
  • [7] Task Transfer Learning for EEG Classification in Motor Imagery-Based BCI System
    Zheng, Xuanci
    Li, Jie
    Ji, Hongfei
    Duan, Lili
    Li, Maozhen
    Pang, Zilong
    Zhuang, Jie
    Rongrong, Lu
    Tianhao, Gao
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2020, 2020
  • [8] BCI-teleoperated androids; A study of embodiment and its effect on motor imagery learning
    Alimardani, M.
    Nishio, S.
    Ishiguro, H.
    INES 2015 - IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENGINEERING SYSTEMS, 2015, : 347 - 352
  • [9] A comparison of algorithms for motor imagery for BCI under different sensory feedback conditions
    Leonardis, Daniele
    Frisoli, Antonio
    Barsotti, Michele
    Vanello, Nicola
    Bergamasco, Massimo
    2012 4TH IEEE RAS & EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB), 2012, : 1010 - 1015
  • [10] Zero-Shot Learning for EEG Classification in Motor Imagery-Based BCI System
    Duan, Lili
    Li, Jie
    Ji, Hongfei
    Pang, Zilong
    Zheng, Xuanci
    Lu, Rongrong
    Li, Maozhen
    Zhuang, Jie
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2020, 28 (11) : 2411 - 2419