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

Al-Baddai, Saad; Al-Subari, Karema; Tomé, Ana Maria; Volberg, Gregor; Hanslmayr, Simon; Hammwöhner, Rainer

doi:10.1016/j.bspc.2014.04.011

Cited by 10 publications

(7 citation statements)

References 44 publications

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“…Then the next trial started after having received the response of the proband or after a time-out of 3 [ s ] in case the subject did not respond. This EEG data [4, 49] was recorded jointly with fMRI data as described in [3, 49]. …”

Section: Methodsmentioning

confidence: 99%

“…Thus EEG provides dynamic information on submillisecond time scales which can be combined favorably with fMRI measurements which provide complementary high resolution information on small spatial scales in the millimeter range [3–7]. EEG reflects voltages generated mostly by excitatory postsynaptic potentials (EPSPs) from apical dendrites of massively synchronized neocortical pyramidal cells.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental paradigm used in our previous study [3, 4] was a contour integration task, applied to a group of 19 probands and 300 trials each. A large set of Gabor stimuli was presented repeatedly which occasionally contained a contour made up by a subset of collinearly oriented Gabor patches.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…In this manuscript we focus on the EEG data. The fMRI results are reported in a separate work [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…In a recent study [ 27 ], we explored the potential of two-dimensional ensemble empirical mode decomposition (2DEEMD) to extract characteristic textures, so-called bidimensional intrinsic mode functions (BIMFs), from the fMRI-related part of the current, combined EEG-fMRI data sets which where taken while performing a contour integration task. To identify most informative textures, i. e. BIMFs, a support vector machine (SVM) as well as a random forest (RF) classifier were trained for two different stimulus/response conditions.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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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.

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A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions

et al. 2019

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

Cited by 10 publications

References 44 publications

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

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

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

A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions

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