TY - JOUR A1 - Wein, Simon A1 - Tomé, Ana Maria A1 - Goldhacker, Markus A1 - Greenlee, Mark W. A1 - Lang, Elmar Wolfgang T1 - A Constrained ICA-EMD Model for Group Level fMRI Analysis JF - Frontiers in Neuroscience N2 - Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components. Y1 - 2020 U6 - https://doi.org/10.3389/fnins.2020.00221 SN - 1662-453X SN - 1662-4548 VL - 14 SP - 1 EP - 10 PB - frontiers 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 - TY - CHAP A1 - Goldhacker, Markus A1 - Tomé, Ana Maria A1 - Greenlee, Mark W. A1 - Lang, Elmar Wolfgang T1 - Early meta-level: deeper understanding of connectivity-states and consequences for state definition T2 - 21st Annual Meeting of the Organization for Human Brain Mapping, June 14-18, 2015, Honolulu, Hawaii Y1 - 2015 U6 - https://doi.org/10.13140/RG.2.1.2561.3929 PB - Academic Press CY - San Diego, CA ER - 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 -