A new descriptor of neuroelectrical activity during BCI-assisted Motor Imagery-based training in stroke patients

被引:0
|
作者
Petti, M. [1 ,2 ]
Mattia, D. [3 ]
Pichiorri, F. [3 ]
Toppi, J. [1 ,2 ]
Salinari, S. [1 ]
Babiloni, F. [3 ,4 ]
Astolfi, L. [1 ,2 ]
Cincotti, F. [1 ,2 ]
机构
[1] Univ Roma La Sapienza, Dept Comp Control & Management Engn, I-00185 Rome, Italy
[2] Fdn Santa Lucia Hosp, Rome, Italy
[3] Fdn Santa Lucia, Rome, Italy
[4] Univ Roma La Sapienza, Dept Physiol & Pharmacol, I-00185 Rome, Italy
关键词
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
In BCI applications for stroke rehabilitation, BCI systems are used with the aim of providing patients with an instrument that is capable of monitoring and reinforcing EEG patterns generated by motor imagery (MI). In this study we proposed an offline analysis on data acquired from stroke patients subjected to a BCI-assisted MI training in order to define an index for the evaluation of MI-BCI training session which is independent from the settings adopted for the online control and which is able to describe the properties of neuroelectrical activations across sessions. Results suggest that such index can be adopted to sort the trails within a session according to the adherence to the task.
引用
收藏
页码:1267 / 1269
页数:3
相关论文
共 36 条
  • [1] An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
    Duan, Xu
    Xie, Songyun
    Xie, Xinzhou
    Obermayer, Klaus
    Cui, Yujie
    Wang, Zhenzhen
    [J]. FRONTIERS IN HUMAN NEUROSCIENCE, 2021, 15
  • [2] Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly
    Javier Gomez-Pilar
    Rebeca Corralejo
    Luis F. Nicolas-Alonso
    Daniel Álvarez
    Roberto Hornero
    [J]. Medical & Biological Engineering & Computing, 2016, 54 : 1655 - 1666
  • [3] Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly
    Gomez-Pilar, Javier
    Corralejo, Rebeca
    Nicolas-Alonso, Luis F.
    Alvarez, Daniel
    Hornero, Roberto
    [J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2016, 54 (11) : 1655 - 1666
  • [4] Self-Paced Training on Motor Imagery-based BCI for Minimal Calibration Time
    Kim, Seon-Min
    Lee, Min-Ho
    Lee, Seong-Whan
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2017, : 2297 - 2301
  • [5] Neurophysiological substrates of stroke patients with motor imagery-based brain-computer interface training
    Li, Mingfen
    Liu, Ye
    Wu, Yi
    Liu, Sirao
    Jia, Jie
    Zhang, Liqing
    [J]. INTERNATIONAL JOURNAL OF NEUROSCIENCE, 2014, 124 (06) : 403 - 415
  • [6] Cortical activity during motor execution, motor imagery, and imagery-based online feedback
    Miller, Kai J.
    Schalk, Gerwin
    Fetz, Eberhard E.
    den Nijs, Marcel
    Ojemann, Jeffrey G.
    Rao, Rajesh P. N.
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2010, 107 (09) : 4430 - 4435
  • [7] Evaluation of Motor Imagery-Based BCI methods in neurorehabilitation of Parkinson's Disease patients
    Miladinovic, A.
    Ajcevic, M.
    Busan, P.
    Jarmolowska, J.
    Silveri, G.
    Deodato, M.
    Mezzarobba, S.
    Battaglini, P. P.
    Accardo, A.
    [J]. 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20, 2020, : 3058 - 3061
  • [8] A tensor-based scheme for stroke patients' motor imagery EEG analysis in BCI-FES rehabilitation training
    Liu, Ye
    Li, Mingfen
    Zhang, Hao
    Wang, Hang
    Li, Junhua
    Jia, Jie
    Wu, Yi
    Zhang, Liqing
    [J]. JOURNAL OF NEUROSCIENCE METHODS, 2014, 222 : 238 - 249
  • [9] Electrophysiological brain activity during the control of a motor imagery-based brain–computer interface
    Frolov A.A.
    Aziatskaya G.A.
    Bobrov P.D.
    Luykmanov R.K.
    Fedotova I.R.
    Húsek D.
    Snašel V.
    [J]. Human Physiology, 2017, 43 (5) : 501 - 511
  • [10] 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
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2021, 29 : 1583 - 1592