Key result
Empirical Mode Decomposition of EEG signals successfully characterized power variations in high-frequency rhythms during motor imagery while using an exoskeleton, offering an alternative to traditional frequency filtering.
Why the study?
Brain rhythm power variations in brain-machine interfaces with robotic exoskeletons are frequently obtained via Fourier analysis, prompting exploration of Empirical Mode Decomposition as an adaptive alternative.
Observational (n=3)
No
Empirical Mode Decomposition is a viable alternative to traditional frequency filtering for analyzing EEG signals in brain-machine interfaces during motor imagery with exoskeletons.
May support adaptive EEG methods in exoskeleton BMI; hypothesis-generating and requires validation before clinical adoption.
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.
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Ortíz et al. (2020) conducted an observational in Healthy (Able-bodied) (n=3). Empirical Mode Decomposition (EMD) of EEG signals vs. Butterworth frequency filter / Relaxed state was evaluated on Variation of power of EEG rhythms during motor imagery compared to relaxed state. Empirical Mode Decomposition of EEG signals successfully characterized power variations in high-frequency rhythms during motor imagery while using an exoskeleton, offering an alternative to traditional frequency filtering.
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