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 language | English (US) |
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Article number | 48 |
Pages (from-to) | 48 |
Journal | Frontiers in Neurorobotics |
Volume | 14 |
DOIs | |
State | Published - Aug 27 2020 |
Keywords
- brain-machine interface
- electroencephalography
- empirical mode decomposition
- exoskeleton
- frequency analysis
- motor imagery
ASJC Scopus subject areas
- Biomedical Engineering
- Artificial Intelligence