Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study

The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some of the brain rhythms experiment during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimat...

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Published inFrontiers in neurorobotics Vol. 14; p. 48
Main Authors Ortiz, Mario, Iáñez, Eduardo, Contreras-Vidal, José L., Azorín, José M.
Format Journal Article
LanguageEnglish
Published Lausanne Frontiers Research Foundation 27.08.2020
Frontiers Media S.A
Subjects
Acoustics
Algorithms
brain-machine interface
Decomposition
EEG
Electrodes
electroencephalography
empirical mode decomposition
Exoskeleton
Fourier analysis
frequency analysis
Interfaces
Mental task performance
motor imagery
Neuroscience
Oscillations
Robotics
Signal processing
Wavelet transforms
Germany
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Summary:The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some of the brain rhythms experiment during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation by using the Fourier analysis. This paper explores the decomposition of the brain rhythms based on the Empirical Mode Decomposition, as an alternative for the analysis of electroencephalographic (EEG) signals, due to its adaptive capability to the local oscillations of the data, showing it as a viable tool for future BMI algorithms based on motor related events.
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Edited by: Surjo R. Soekadar, Charité–Universitätsmedizin Berlin, Germany
Reviewed by: Suparerk Janjarasjitt, Ubon Ratchathani University, Thailand; Giacinto Barresi, Italian Institute of Technology (IIT), Italy
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2020.00048