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

Mario Ortiz, Eduardo Iáñez, José L. Contreras-Vidal, José M. Azorín

Research output: Contribution to journalArticle

Abstract

The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation 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, showcasing it as a viable tool for future BMI algorithms based on motor related events.

Original languageEnglish (US)
Article number48
JournalFrontiers in Neurorobotics
Volume14
DOIs
StatePublished - Aug 27 2020

Keywords

  • brain-machine interface
  • electroencephalography
  • empirical mode decomposition
  • exoskeleton
  • frequency analysis
  • motor imagery

ASJC Scopus subject areas

  • Biomedical Engineering
  • Artificial Intelligence

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