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

Al-Subari, Karema; Al-Baddai, Saad; Tomé, Ana Maria; Goldhacker, Markus; Faltermeier, Rupert; Lang, Elmar

doi:10.1016/j.jneumeth.2015.06.020

Cited by 33 publications

(21 citation statements)

References 28 publications

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“…The data used in this study are EEG recordings collected during a contour integration task [4, 12]. The data was collected from 64 electrodes (BrainAmp MR plus, Brain Products, Gilching, Germany) placed according to the 10 − 10 system.…”

Section: Methodsmentioning

confidence: 99%

“…The EEG data were processed using the EEGLAB toolbox [59] and the recently integrated EMDLAB toolbox [12], before the data were analyzed by sLORETA. EEG artifacts (i. e. eye blink and eye movements, heart beat and muscle noise) were removed by independent component analysis (ICA) [60].…”

Section: Methodsmentioning

confidence: 99%

“…Many different source configurations can generate the same distribution of potentials and magnetic fields on the scalp [10, 11]. Thus, the analysis of such EEG data is quite involved, encompassing machine learning and signal processing techniques like feature extraction [4, 12] and inverse modeling [13]. For timely accounts of recent advancements and actual challenges in dynamic functional neuroimaging techniques, including electrophysiological source imaging, multimodal neuroimaging integrating fMRI with EEG/MEG, and functional connectivity imaging see the reviews of Bin He [14] and Jatoi et al [15].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, an informed decomposition approach, which built upon constrained optimization approaches [42] for independent components analysis, has been proposed to better model and separate distinct subspaces within EEG data [43]. While supervised techniques require expert knowledge, unsupervised methods such as exploratory matrix factorization (EMF) methods, variously known as blind source or signal separation (BSS) techniques [44] or empirical mode decomposition (EMD) methods [12, 45, 46] offer versatile tools for transforming the registered signals into more elusive and informative representations. It is one of the objectives of this study to investigate the specific advantages of applying such methods as preprocessing techniques, and applying source localization to the modes extracted from such methods instead of to the raw signals themselves.…”

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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Children’s ERPs used to have low signal-to-noise ratio (SNR). Previous studies have suggested that the ensemble empirical mode decomposition (EEMD) method can improve remarkably the SNR during measurements of ERPs (Cong et al, 2010; Al-Subari et al, 2015a,b; Chen et al, 2016; Hsu et al, 2016). For example, Hsu et al (2016) applied EEMD to reanalyze the dataset of Cheng et al (2013) and demonstrated that only one third of the original trials were required to replicate the mismatch negativity (MMN) effect with the approximate effect size.…”

Section: Methodsmentioning

confidence: 99%

The Acquisition of Orthographic Knowledge: Evidence from the Lexicality Effects on N400

Tzeng¹,

Hsu

Huang

et al. 2017

Front. Psychol.

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This study aimed to understand how reading ability shapes the lexicality effects on N400. Fifty-three typical developing children from the second to the sixth grades were asked to perform the pronounceability judgment task on a set of Chinese real characters (RC), pseudocharacters (PC) and non-characters (NC), as ERPs were recorded. The cluster-based permutation analysis revealed that children with low- to medium-reading ability showed greater negativity to NCs than to RCs and PCs in frontal sites from 300 to 450 ms, while children with high ability group showed a greater positivity to NCs than both RCs and PCs at central to posterior sites. Furthermore, the linear mixed model (LMM) analysis was applied to investigate the relationship between lexicality effects on N400 and reading-related behavioral assessments on a set of standardized tests (including character recognition, vocabulary size, phonological awareness, and working memory). The results found that in children with lower reading ability, the N400 elicited by NCs becomes more negative in the frontal sites. For children with higher reading ability, the N400 elicited by NCs became more positive than that elicited by RCs or PCs in the posterior sites. These findings demonstrate the developmental changes in the lexicality effects on N400 as children become more advanced readers and suggested that the lexicality effects on N400 can serve as neural markers for the evaluation of orthographic proficiency in reading development.

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Evaluating Memory and Cognition via a Wearable EEG System: A Preliminary Study

Miloulis

Kakkos

Dimitrakopoulos

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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

Cited by 33 publications

References 28 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

The Acquisition of Orthographic Knowledge: Evidence from the Lexicality Effects on N400

Evaluating Memory and Cognition via a Wearable EEG System: A Preliminary Study

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