A study on the effect of electrical stimulation as a user stimuli for motor imagery classification in Brain-Machine Interface

被引：0
|
作者：
Bhattacharyya, Saugat [1 ]
Clerc, Maureen [2 ]
Hayashibe, Mitsuhiro [1 ]
机构：
[1] Univ Montpellier, INRIA, LIRMM, BCI LIFT Project,CAMIN Team, F-34059 Montpellier, France
[2] Inria Sophia Antipolis, Athena Team, BCI LIFT Project, Sophia Antipolis, France
来源：
EUROPEAN JOURNAL OF TRANSLATIONAL MYOLOGY | 2016年 / 26卷 / 02期
关键词：
Electrical Stimulation; Brain machine interfacing; Motor Imagery; Electroencephalography;
D O I：
暂无
中图分类号：
R-3 [医学研究方法]; R3 [基础医学];
学科分类号：
1001 ;
摘要：
Functional Electrical Stimulation (FES) provides a neuroprosthetic interface to non-recovered muscle groups by stimulating the affected region of the human body. FES in combination with Brain-machine interfacing (BMI) has a wide scope in rehabilitation because this system directly links the cerebral motor intention of the users with its corresponding peripheral muscle activations. In this paper, we examine the effect of FES on the electroencephalography (EEG) during motor imagery (left- and right-hand movement) training of the users. Results suggest a significant improvement in the classification accuracy when the subject was induced with FES stimuli as compared to the standard visual one.
引用
收藏
页码：165 / 168
页数：4
相关论文
共 50 条
  • [1] A CLASSIFICATION METHOD OF DIFFERENT MOTOR IMAGERY TASKS BASED ON FRACTAL FEATURES FOR BRAIN-MACHINE INTERFACE
    Phothisonothai, Montri
    Nakagawa, Masahiro
    JOURNAL OF INTEGRATIVE NEUROSCIENCE, 2009, 8 (01) : 95 - 122
  • [2] Motor imagery classification in brain-machine interface with machine learning algorithms: Classical approach to multi-layer perceptron model
    Sharma, Rahul
    Kim, Minju
    Gupta, Akansha
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 71
  • [3] Quantifying the role of motor imagery in brain-machine interfaces
    Marchesotti, Silvia
    Bassolino, Michela
    Serino, Andrea
    Bleuler, Hannes
    Blanke, Olaf
    SCIENTIFIC REPORTS, 2016, 6
  • [4] Neural Stimulation with an Endovascular Brain-Machine Interface
    Opie, Nicholas L.
    John, Sam E.
    Gerboni, Giulia
    Rind, Gil S.
    Lovell, Timothy J. H.
    Ronayne, Stephen M.
    Wong, Yan. T.
    May, Clive N.
    Grayden, David B.
    Oxley, Thomas J.
    2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2019, : 738 - 741
  • [5] Quantifying the role of motor imagery in brain-machine interfaces
    Silvia Marchesotti
    Michela Bassolino
    Andrea Serino
    Hannes Bleuler
    Olaf Blanke
    Scientific Reports, 6
  • [6] Brain-Machine Neurofeedback: Robotics or Electrical Stimulation?
    Guggenberger, Robert
    Heringhaus, Monika
    Gharabaghi, Alireza
    FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2020, 8 (08):
  • [7] Development of a Real-Time Motor-Imagery-Based EEG Brain-Machine Interface
    Gorjup, Gal
    Vrabic, Rok
    Stoyanov, Stoyan Petrov
    Andersen, Morten Ostergaard
    Manoonpong, Poramate
    NEURAL INFORMATION PROCESSING (ICONIP 2018), PT VII, 2018, 11307 : 610 - 622
  • [8] The Effect of Transcranial Electrical Stimulation on the Brain Connectivity of Motor Imagery
    Peng, Maoqin
    Lai, Danwei
    INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY, 2021, 168 : S231 - S232
  • [9] Adaptive Classification for Brain-Machine Interface with Reinforcement Learning
    Matsuzaki, Shuichi
    Shiina, Yusuke
    Wada, Yasuhiro
    NEURAL INFORMATION PROCESSING, PT I, 2011, 7062 : 360 - 369
  • [10] EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system
    Zheng, Minmin
    Yang, Banghua
    Xie, Yunlong
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2020, 58 (07) : 1515 - 1528
12345