Literature DB >> 34051665

Mu oscillations and motor imagery performance: A reflection of intra-individual success, not inter-individual ability.

Yvonne Y Chen1, Kathryn J M Lambert2, Christopher R Madan3, Anthony Singhal4.   

Abstract

Mu oscillations (8-13 Hz), recorded over the human motor cortex, have been shown to consistently suppress during both the imagination and performance of movements; however, its functional significance in the imagery process is currently unclear. Here we examined human electroencephalographic (EEG) oscillations in the context of motor imagery performance as measured by imagery success within participants and imagery ability between participants. We recorded continuous EEG activity while participants performed the Test of Ability in Movement Imagery (TAMI), an objective test of motor imagery task. Results demonstrated that mu oscillatory activity significantly decreased during successful as compared to unsuccessful imagery trials. However, the extent of reduction in mu oscillations did not correlate with overall imagery ability as measured by the total TAMI score. These findings provide further support for the involvement of mu oscillations in indexing motor imagery performance and suggest that mu oscillations may reflect important processes related to imagery accuracy, processes likely related to those underlying overt motor production and motor understanding.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Electroencephalography(EEG); Imagery ability; Imagery success; Motor imagery; mu rhythm

Year:  2021        PMID: 34051665     DOI: 10.1016/j.humov.2021.102819

Source DB:  PubMed          Journal:  Hum Mov Sci        ISSN: 0167-9457            Impact factor:   2.161


  2 in total

1.  An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces.

Authors:  Sotirios Papadopoulos; James Bonaiuto; Jérémie Mattout
Journal:  Front Neurosci       Date:  2022-01-12       Impact factor: 4.677

2.  Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm.

Authors:  Hongli Yu; Sidi Ba; Yuxue Guo; Lei Guo; Guizhi Xu
Journal:  Brain Sci       Date:  2022-01-30
  2 in total

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