TY - VIDEO A1 - Al-Subari, Karema A1 - Al-Baddai, Saad A1 - Tomé, Ana Maria A1 - Goldhacker, Markus A1 - Faltermeier, Rupert A1 - Lang, Elmar Wolfgang T1 - EMDLAB-toolbox- tutorial video Y1 - 2015 UR - https://www.researchgate.net/publication/280492172_EMDLAB-toolbox-_tutorial_video ER - TY - JOUR A1 - Goldhacker, Markus A1 - Al-Subari, Karema A1 - Al-Baddai, Saad A1 - Tomé, Ana Maria A1 - Faltermeier, Rupert A1 - Lang, Elmar Wolfgang T1 - EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition JF - Journal of Neuroscience Methods N2 - Background: Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field. New method: EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis. Results: EMDLAB 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 methods: EMDLAB 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. Conclusions: EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study. Y1 - 2015 U6 - https://doi.org/10.1016/j.jneumeth.2015.06.020 VL - 253 IS - September SP - 193 EP - 205 PB - Elsevier CY - AMsterdam ER -