Computational NeuroscienceEMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition

- A new EEGLAB plug-in toolbox for extracting both IMFs and ERMs of EEG signals.
- Intuitive graphic interface for implementing and visualizing different types of EMD algorithms.
- EMDLAB is a user-friendly toolbox for analysing and visualizing EEG study.
- Relative Hilbert and Fourier spectrum of extracted modes is provided in EMDLAB toolbox.

BackgroundEmpirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field.New methodEMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis.ResultsEMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox.Comparison with existing methodsEMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal.ConclusionsEMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.