ArticlePloS one2016
Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Application of Empirical Mode Decomposition for Decoding Perception of Faces Using Magnetoencephalography.Sensors (Basel, Switzerland) · 2021Article
- Unmixing Oscillatory Brain Activity by EEG Source Localization and Empirical Mode Decomposition.Computational intelligence and neuroscience · 2019Article
- [Abnormal electroencephalogram features extraction of autistic children based on wavelet transform combined with empirical modal decomposition].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2018Article
- Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study.Frontiers in integrative neuroscience · 2018Article
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Authors and funding
6 authors.
Funding
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Abstract
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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