K Al-Subari1, S Al-Baddai2, A M Tomé3, M Goldhacker4, R Faltermeier5, E W Lang6. 1. CIML Group, Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany; Institute of Information Science, University of Regensburg, Germany. 2. CIML Group, Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany; Institute of Information Science, University of Regensburg, Germany. Electronic address: saad.al-baddai@ur.de. 3. IEETA, DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal. 4. CIML Group, Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany; Institute of Experimental Psychology, University of Regensburg, Germany. 5. Clinic of Neurosurgery, University Hospital Regensburg, Germany. 6. CIML Group, Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany.
Abstract
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.
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.
Authors: Karema Al-Subari; Saad Al-Baddai; Ana Maria Tomé; Gregor Volberg; Bernd Ludwig; Elmar W Lang Journal: PLoS One Date: 2016-12-09 Impact factor: 3.240
Authors: Javier Alegre-Cortés; Cristina Soto-Sánchez; Ana L Albarracín; Fernando D Farfán; Mikel Val-Calvo; José M Ferrandez; Eduardo Fernandez Journal: Front Neuroinform Date: 2018-01-10 Impact factor: 4.081
Authors: Shao-Yang Tsai; Satish Jaiswal; Chi-Fu Chang; Wei-Kuang Liang; Neil G Muggleton; Chi-Hung Juan Journal: Front Integr Neurosci Date: 2018-05-15