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

Reduction or enhancement? Repetition effects on early brain potentials during visual word recognition are frequency dependent

Most studies on word repetition have demonstrated that repeated stimuli yield reductions in brain activity. Despite the well-known repetition reduction effect, some literature reports repetition enhancements in electroencephalogram (EEG) activities. However, although studies of object and face recognition have consistently demonstrated both repetition reduction and enhancement effects, the results of repetition enhancement effects were not consistent in studies of visual word recognition. Therefore, the present study aimed to further investigate the repetition effect on the P200, an early event-related potential (ERP) component that indexes the coactivation of lexical candidates during visual word recognition. To achieve a high signal-to-noise ratio, EEG signals were decomposed into various modes by using the Hilbert-Huang transform. Results demonstrated a repetition enhancement effect on P200 activity in alpha-band oscillation and that lexicality and orthographic neighborhood size would influence the magnitude of the repetition enhancement effect on P200. These findings suggest that alpha activity during visual word recognition might reflect the coactivation of orthographically similar words in the early stages of lexical processing. Meantime, there were repetition reduction effects on ERP activities in theta-delta band oscillation, which might index that the lateral inhibition between lexical candidates would be omitted in repetition.

Neural correlates of understanding emotional words in late childhood

Prior studies involving adults have shown that words can elicit emotional processing, with emotion-label (e.g., happiness) and emotion-laden words (e.g., gift) having distinct processes. However, limited studies have explored the developmental changes in these processes in relation to emotional valence. To address this question, this exploratory study measured event-related potentials (ERPs) in 11-14-year-old children/adolescents (N = 25) and adults (N = 23) while performing an emotional categorization task. The stimuli used were two-character Chinese words, with factors for word type (emotion-label versus emotion-laden) and valence (positive versus negative). To confirm word emotionality, neutral words were also included and compared with all emotional words. The results showed that adults exhibited reduced N400 amplitudes to emotion-label words compared to emotion-laden ones in both positive and negative valence contexts. The differentiation was only sustained for negative valence in the late positive component (LPC). Similar scalp distributions of the effects of word type were found in children/adolescents; however, they exhibited a more prolonged processing of all emotional words than adults. These results suggest that the processing of emotion-label and emotion-laden words are distinct in late childhood, and this discrepancy varies with emotional valence and increasing age.

Speech comprehension in noisy environments: Evidence from the predictability effects on the N400 and LPC

Introduction

Speech comprehension involves context-based lexical predictions for efficient semantic integration. This study investigated how noise affects the predictability effect on event-related potentials (ERPs) such as the N400 and late positive component (LPC) in speech comprehension.

Methods

Twenty-seven listeners were asked to comprehend sentences in clear and noisy conditions (hereinafter referred to as "clear speech" and "noisy speech," respectively) that ended with a high-or low-predictability word during electroencephalogram (EEG) recordings.

Results

The study results regarding clear speech showed the predictability effect on the N400, wherein low-predictability words elicited a larger N400 amplitude than did high-predictability words in the centroparietal and frontocentral regions. Noisy speech showed a reduced and delayed predictability effect on the N400 in the centroparietal regions. Additionally, noisy speech showed a predictability effect on the LPC in the centroparietal regions.

Discussion

These findings suggest that listeners achieve comprehension outcomes through different neural mechanisms according to listening conditions. Noisy speech may be comprehended with a second-pass process that possibly functions to recover the phonological form of degraded speech through phonetic reanalysis or repair, thus compensating for decreased predictive efficiency.

A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection

Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches.

Alpha and high gamma phase amplitude coupling during motor imagery and weighted cross-frequency coupling to extract discriminative cross-frequency patterns

Motor imagery modulates specific neural oscillations like actual movement does. Representatively, suppression of the alpha power (e.g., event-related desynchronization [ERD]) is the typical pattern of motor imagery in the motor cortex. However, in addition to this amplitude-based feature, the coupling across frequencies includes important information about the brain functions and the existence of such complex information has been reported in various invasive studies. Yet, the interaction across multiple frequencies during motor imagery processing is still unclear and has not been widely studied, particularly concerning the non-invasive signals. In this study, we provide empirical evidence of the comodulation between the phase of alpha rhythm and the amplitude of high gamma rhythm during the motor imagery process. We used electroencephalography (EEG) in our investigation during the imagination of left- or right-hand movement recorded from 52 healthy subjects, and quantified the ERD of alpha and phase-amplitude coupling (PAC) which is a relative change of modulation index to the base line period (before the cue). As a result, we found that the coupling between the phase of alpha (8-12 Hz) and the amplitude of high gamma (70-120 Hz) and this PAC decreases during motor imagery and then rebounds to the baseline like alpha ERD (r = 0.29 to 0.42). This correlation between PAC and ERD was particularly stronger in the ipsilateral area. In addition, trials that demonstrated higher alpha power during the ready period (before the cue) showed a larger ERD during motor imagery and similarly, trials with higher modulation index during the ready period yielded a greater decrease in PAC during imagery. In the classification analysis, we found that the effective phase frequency that showed better decoding accuracy in left and right-hand imagery, varied across subjects. Motivated by result, we proposed a weighted cross-frequency coupling (WCFC) method that extracts the maximal discriminative feature by combining band power and CFC. In the evaluation, WCFC with only two electrodes yielded a performance comparable to the conventional algorithm with 64 electrodes in classifying left and right-hand motor imagery. These results indicate that the phase-amplitude frequency plays an important role in motor imagery, and that optimizing this frequency ranges is crucial for extracting information features to decode the motor imagery types.

Event-related components are structurally represented by intrinsic event-related potentials

The detection of event-related potentials (ERPs) through electroencephalogram (EEG) analysis is a well-established method for understanding brain functions during a cognitive process. To increase the signal-to-noise ratio (SNR) and stationarity of the data, ERPs are often filtered to a wideband frequency range, such as 0.05–30 Hz. Alternatively, a natural-filtering procedure can be performed through empirical mode decomposition (EMD), which yields intrinsic mode functions (IMFs) for each trial of the EEG data, followed by averaging over trials to generate the event-related modes. However, although the EMD-based filtering procedure has advantages such as a high SNR, suitable waveform shape, and high statistical power, one fundamental drawback of the procedure is that it requires the selection of an IMF (or a partial sum of a range of IMFs) to determine an ERP component effectively. Therefore, in this study, we propose an intrinsic ERP (iERP) method to overcome the drawbacks and retain the advantages of event-related mode analysis for investigating ERP components. The iERP method can reveal multiple ERP components at their characteristic time scales and suitably cluster statistical effects among modes by using a tailored definition of each mode’s neighbors. We validated the iERP method by using realistic EEG data sets acquired from a face perception task and visual working memory task. By using these two data sets, we demonstrated how to apply the iERP method to a cognitive task and incorporate existing cluster-based tests into iERP analysis. Moreover, iERP analysis revealed the statistical effects between (or among) experimental conditions more effectively than the conventional ERP method did.

To Go or Not to Go: Degrees of Dynamic Inhibitory Control Revealed by the Function of Grip Force and Early Electrophysiological Indices

A critical issue in executive control is how the nervous system exerts flexibility to inhibit a prepotent response and adapt to sudden changes in the environment. In this study, force measurement was used to capture “partial” unsuccessful trials that are highly relevant in extending the current understanding of motor inhibition processing. Moreover, a modified version of the stop-signal task was used to control and eliminate potential attentional capture effects from the motor inhibition index. The results illustrate that the non-canceled force and force rate increased as a function of stop-signal delay (SSD), offering new objective indices for gauging the dynamic inhibitory process. Motor response (time and force) was a function of delay in the presentation of novel/infrequent stimuli. A larger lateralized readiness potential (LRP) amplitude in go and novel stimuli indicated an influence of the novel stimuli on central motor processing. Moreover, an early N1 component reflects an index of motor inhibition in addition to the N2 component reported in previous studies. Source analysis revealed that the activation of N2 originated from inhibitory control associated areas: the right inferior frontal gyrus (rIFG), pre-motor cortex, and primary motor cortex. Regarding partial responses, LRP and error-related negativity (ERNs) were associated with error correction processes, whereas the N2 component may indicate the functional overlap between inhibition and error correction. In sum, the present study has developed reliable and objective indices of motor inhibition by introducing force, force-rate and electrophysiological measures, further elucidating our understandings of dynamic motor inhibition and error correction.

Revealing the Electrophysiological Correlates of Working Memory-Load Effects in Symmetry Span Task With HHT Method

Complex span task is one of the commonly used cognitive tasks to evaluate an individual's working memory capacity (WMC). It is a dual task consisting of a distractor subtask and a memory subtask. Though multiple studies have utilized complex span tasks, the electrophysiological correlates underlying the encoding and retrieval processes in working memory span task remain uninvestigated. One previous study that assessed electroencephalographic (EEG) measures utilizing complex span task found no significant difference between its working memory loads, a typical index observed in other working memory tasks (e.g., n-back task and digital span task). The following design constructs of the paradigm might have been the reason. (1) The fixed-time limit of the distractor subtask may have hindered the assessment of individual WMC precisely. (2) Employing a linear-system-favoring EEG data analysis method for a non-linear system such as the human brain. In the current study, the participants perform the Raven Advanced Progressive Matrices (RAMP) task on 1 day and the symmetry span (Sspan) task on the other. Prior to the formal Sspan task, the participants were instructed to judge 15 simple symmetry questions as quickly as possible. A participant-specific time-limit is chartered from these symmetry questions. The current study utilizes the Sspan task sequential to a distractor subtask. Instead of the fixed time-limit exercised in the previous study, the distractor subtask of the current study was equipped with the participant-specific time-limit obtained from the symmetry questions. This could provide a precise measure of individual WMC. This study investigates if the complex span task resonates EEG patterns similar to the other working memory tasks in terms of working memory-load by utilizing ensemble empirical mode decomposition (EEMD) of Hilbert-Huang transform (HHT). Prior expectations were to observe a decrement in the P300 component of event-related mode (ERM) and a decrement in the power of alpha and beta band frequency with increasing working memory-load. We observed a significantly higher P300 amplitude for the low-load condition compared to the high-load condition over the circumscribed brain network across F4 and C4 electrodes. Time-frequency analysis revealed a significant difference between the high- and low-load conditions at alpha and beta band over the frontal, central, and parietal channels. The results from our study demonstrate precise differences in EEG data pertaining to varied memory-load differences in the complex span task. Thus, assessing complex span tasks with the HHT-based analysis may aid in achieving a better signal to noise ratio and effect size for the results in working memory EEG studies.

Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization

EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Generally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decomposing (MEMD) and inverse solution algorithm with an embedded unsupervised eye blink remover in order to localize the epileptogenic zone accurately. For this purpose, we constructed realistic forward models using MRI and boundary element method (BEM) for each patient to obtain results that are more realistic. We also developed an unsupervised algorithm utilizing a wavelet method to remove eye blink artifacts. Additionally, we applied MEMD, which is one of the recent and suitable feature extraction methods for non-linear, non-stationary, and multivariate signals such as EEG, to extract the signal of interest. We examined the localization results using the two most reliable linear distributed inverse methods in the literature: weighted minimum norm estimation (wMN) and standardized low resolution tomography (sLORETA). Results affirm the success of the proposed algorithm with the highest agreement compared to MRI reference by a specialist. Fusion of MEMD and sLORETA results in approximately zero localization error in terms of spatial difference with the validated MRI reference. High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics.

Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI₁₋₃₀. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions.

Spatio-temporal patterns of cognitive control revealed with simultaneous electroencephalography and functional magnetic resonance imaging

Optimal performance depends in part on the ability to inhibit the automatic processing of irrelevant information and also on the adjusting the level of control from one trial to the next. In this study, we investigated the spatio-temporal neural correlates of cognitive control using simultaneous functional magnetic resonance imaging and electroencephalography, while 22 participants (10 women) performed a numerical Stroop task. We investigated the spatial and temporal dynamic of the conflict adaptation effects (i.e., reduced interference on items that follow an incongruent stimulus compared to after a congruent stimulus). Joint independent component analysis linked the N200 component to activation of anterior cingulate cortex (ACC) and the conflict slow potential to widespread activations within the fronto-parietal executive control network. Connectivity analyses with psychophysiological interactions and dynamic causal modeling demonstrated coordinated engagement of the cognitive control network after the processing of an incongruent item, and this was correlated with better behavioral performance. Our results combined high spatial and temporal resolution to propose the following network of conflict adaptation effect and specify the time course of activation within this model: first, the anterior insula and inferior frontal gyrus are activated when incongruence is detected. These regions then signal the need for higher control to the ACC, which in turn activates the fronto-parietal executive control network to improve the performance on the next trial.

Meditation Effects on the Control of Involuntary Contingent Reorienting Revealed With Electroencephalographic and Behavioral Evidence

Prior studies have reported that meditation may improve cognitive functions and those related to attention in particular. Here, the dynamic process of attentional control, which allows subjects to focus attention on their current interests, was investigated. Concentrative meditation aims to cultivate the abilities of continuous focus and redirecting attention from distractions to the object of focus during meditation. However, it remains unclear how meditation may influence attentional reorientation, which involves interaction between both top-down and bottom-up processes. We aimed to investigate the modulating effect of meditation on the mechanisms of contingent reorienting by employing a rapid serial visual presentation (RSVP) task in conjunction with electrophysiological recording. We recruited 26 meditators who had an average of 2.9 years of meditation experience and a control group comprising 26 individuals without any prior experience of meditation. All subjects performed a 30-min meditation and a rest condition with data collected pre- and post-intervention, with each intervention given on different days. The state effect of meditation improved overall accuracy for all subjects irrespective of their group. A group difference was observed across interventions, showing that meditators were more accurate and more efficient at attentional suppression, represented by a larger Pd (distractor positive) amplitude of event related modes (ERMs), for target-like distractors than the control group. The findings suggested that better attentional control with respect to distractors might be facilitated by acquiring experience of and skills related to meditation training.

Frequency-selective alteration in the resting-state corticostriatal-thalamo-cortical circuit correlates with symptoms severity in first-episode drug-naive patients with schizophrenia

Schizophrenia is a prototypical disorder of brain connectivity with altered neural activity in regions extending throughout the brain. Regions, including the subcortex and cortex, present activity mainly within a specific frequency band in resting-state. Whether these altered resting-state functional connections also present frequency specificity is unknown. In the present study, empirical mode decomposition, which is a pure data-driven method suitable for nonlinear and nonstationary signals, was used to decompose blood-oxygen-level-dependent (BOLD) signals into different intrinsic frequency bands. Our study included 42 first-episode drug-naive patients with schizophrenia and 38 controls. Significant aberration in functional connectivity was observed only at a higher frequency range (the peak spectral density power was 0.06Hz). In this frequency band, patients with schizophrenia showed significantly increased functional connections between the bilateral cuneus and right supplementary motor area, reduced connections within the basal ganglia, and reduced connections between the dorsal striatum and left supplementary motor area. The dysfunction of the frontal gyrus significantly correlated with the dysfunction of the basal ganglia. Notably, these altered connections were significantly correlated with symptom severity. Our results demonstrate that frequency-selective altered corticostriatal-thalamo-cortical circuits in patients with schizophrenia are associated with symptoms severity.

A tool to automatically analyze electromagnetic tracking data from high dose rate brachytherapy of breast cancer patients

During High Dose Rate Brachytherapy (HDR-BT) the spatial position of the radiation source inside catheters implanted into a female breast is determined via electromagnetic tracking (EMT). Dwell positions and dwell times of the radiation source are established, relative to the patient’s anatomy, from an initial X-ray-CT-image. During the irradiation treatment, catheter displacements can occur due to patient movements. The current study develops an automatic analysis tool of EMT data sets recorded with a solenoid sensor to assure concordance of the source movement with the treatment plan. The tool combines machine learning techniques such as multi-dimensional scaling (MDS), ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA) and particle filter (PF) to precisely detect and quantify any mismatch between the treatment plan and actual EMT measurements. We demonstrate that movement artifacts as well as technical signal distortions can be removed automatically and reliably, resulting in artifact-free reconstructed signals. This is a prerequisite for a highly accurate determination of any deviations of dwell positions from the treatment plan.

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

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.

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.

Effects of orthographic consistency and homophone density on Chinese spoken word recognition

Studies of alphabetic language have shown that orthographic knowledge influences phonological processing during spoken word recognition. This study utilized the Event-Related Potentials (ERPs) to differentiate two types of phonology-to-orthography (P-to-O) mapping consistencies in Chinese, namely homophone density and orthographic consistency. The ERP data revealed an orthographic consistency effect in the frontal-centrally distributed N400, and a homophone density effect in central-posteriorly distributed late positive component (LPC). Further source analyses using the standardized low-resolution electromagnetic tomography (sLORETA) demonstrated that the orthographic effect was not only localized in the frontal and temporal-parietal regions for phonological processing, but also in the posterior visual cortex for orthographic processing, while the homophone density effect was found in middle temporal gyrus for lexical-semantic selection, and in the temporal-occipital junction for orthographic processing. These results suggest that orthographic information not only shapes the nature of phonological representations, but may also be activated during on-line spoken word recognition.

Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition

BACKGROUND

Functional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience.

NEW METHOD

To deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise.

RESULTS

We revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD.

COMPARISON WITH EXISTING METHOD(S)

Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t-score distribution.

CONCLUSIONS

We found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.

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.