Bidimensional ensemble empirical mode decomposition of functional biomedical images taken during a contour integration task

Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task

Lately, Ensemble Empirical Mode Decomposition (EEMD) techniques receive growing interest in biomedical data analysis. Event-Related Modes (ERMs) represent features extracted by an EEMD from electroencephalographic (EEG) recordings. We present a new approach for source localization of EEG data based on combining ERMs with inverse models. As the first step, 64 channel EEG recordings are pooled according to six brain areas and decomposed, by applying an EEMD, into their underlying ERMs. Then, based upon the problem at hand, the most closely related ERM, in terms of frequency and amplitude, is combined with inverse modeling techniques for source localization. More specifically, the standardized low resolution brain electromagnetic tomography (sLORETA) procedure is employed in this work. Accuracy and robustness of the results indicate that this approach deems highly promising in source localization techniques for EEG data.

Multi-dimensional complete ensemble empirical mode decomposition with adaptive noise applied to laser speckle contrast images

Laser speckle contrast imaging (LSCI) is a noninvasive full-field optical technique which allows analyzing the dynamics of microvascular blood flow. LSCI has attracted attention because it is able to image blood flow in different kinds of tissue with high spatial and temporal resolutions. Additionally, it is simple and necessitates low-cost devices. However, the physiological information that can be extracted directly from the images is not completely determined yet. In this work, a novel multi-dimensional complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) is introduced and applied in LSCI data recorded in three physiological conditions (rest, vascular occlusion and post-occlusive reactive hyperaemia). MCEEMDAN relies on the improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and our algorithm is specifically designed to analyze multi-dimensional data (such as images). Over the recent multi-dimensional ensemble empirical mode decomposition (MEEMD), MCEEMDAN has the advantage of leading to an exact reconstruction of the original data. The results show that MCEEMDAN leads to intrinsic mode functions and residue that reveal hidden patterns in LSCI data. Moreover, these patterns differ with physiological states. MCEEMDAN appears as a promising way to extract features in LSCI data for an improvement of the image understanding.

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

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.

Ensemble Empirical Mode Decomposition Analysis of EEG Data Collected during a Contour Integration Task

We discuss a data-driven analysis of EEG data recorded during a combined EEG/fMRI study of visual processing during a contour integration task. The analysis is based on an ensemble empirical mode decomposition (EEMD) and discusses characteristic features of event related modes (ERMs) resulting from the decomposition. We identify clear differences in certain ERMs in response to contour vs noncontour Gabor stimuli mainly for response amplitudes peaking around 100 [ms] (called P100) and 200 [ms] (called N200) after stimulus onset, respectively. We observe early P100 and N200 responses at electrodes located in the occipital area of the brain, while late P100 and N200 responses appear at electrodes located in frontal brain areas. Signals at electrodes in central brain areas show bimodal early/late response signatures in certain ERMs. Head topographies clearly localize statistically significant response differences to both stimulus conditions. Our findings provide an independent proof of recent models which suggest that contour integration depends on distributed network activity within the brain.