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

A model for the diagnosis of anxiety in patients with epilepsy based on phase locking value and Lempel-Ziv complexity features of the electroencephalogram

OBJECTIVE

Anxiety disorders (AD) are critical factors that significantly (about one-fifth) impact the quality of life (QoL) in patients with epilepsy (PWE). Objective diagnostic methods have contributed to the identification of PWE susceptible to AD. This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value (PLV) and Lempel-Ziv Complexity (LZC) features of the electroencephalogram (EEG).

METHODS

EEG data from 131 patients with epilepsy (PWE) were enrolled in this study. Patients were divided into two groups, anxiety disorder (AD, n = 61) and non-anxiety disorder (NAD, n = 70), according to the Hamilton Rating Scale for Anxiety (HAM-A). Support vector machine (SVM) and K-Nearest-Neighbor(KNN) algorithms were used to construct three models - the PLVEEG, LZCEEG, and PLVEEG + LZCEEG feature models. Finally, the area under the receiver operating characteristic curve (AUC) and statistical analyses were performed to evaluate the model performance.

RESULTS

The efficiency of the KNN-based PLCEEG + LZCEEG feature model was the best, and the accuracy, precision, recall, F1-score, and AUC of the model after five-fold cross-validations scores were 87.89 %, 82.27 %, 98.33 %, 88.95 %, and 0.89, respectively. When the model efficiency was optimal, 29 EEG features were suggested. Further analysis of these features indicated 22 EEG features that were significantly different between the two groups, including 50 % features of the alpha (α)-band.

CONCLUSIONS

The PLVEEG + LZCEEG model features can identify AD in PWE. The PLVEEG and LZCEEG characteristics of the α-band may further be explored as potential biomarkers for AD in PWE.

GPU Implementation of the Improved CEEMDAN Algorithm for Fast and Efficient EEG Time-Frequency Analysis

Time-frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide the underlying mechanism for communication between neural assemblies. Traditional methods of analysis, such as Short-Time FFT and Wavelet Transforms, are not ideal for this task due to the time-frequency uncertainty principle and their reliance on predefined basis functions. Empirical Mode Decomposition and its variants are more suited to this task as they are able to extract the instantaneous frequency and phase information but are too time consuming for practical use. Our aim was to design and develop a massively parallel and performance-optimized GPU implementation of the Improved Complete Ensemble EMD with the Adaptive Noise (CEEMDAN) algorithm that significantly reduces the computational time (from hours to seconds) of such analysis. The resulting GPU program, which is publicly available, was validated against a MATLAB reference implementation and reached over a 260× speedup for actual EEG measurement data, and provided predicted speedups in the range of 3000-8300× for longer measurements when sufficient memory was available. The significance of our research is that this implementation can enable researchers to perform EMD-based EEG analysis routinely, even for high-density EEG measurements. The program is suitable for execution on desktop, cloud, and supercomputer systems and can be the starting point for future large-scale multi-GPU implementations.

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.

Recording EEG in Cochlear Implant Users: Guidelines for Experimental Design and Data Analysis for Optimizing Signal Quality and Minimizing Artifacts

Cochlear implants (CI) are neural prostheses that can restore hearing in individuals with severe to profound hearing loss. Although CIs significantly improve quality of life, clinical outcomes are still highly variable. An important part of this variability is explained by the brain reorganization following cochlear implantation. Therefore, clinicians and researchers are seeking objective measurements to investigate post-implantation brain plasticity. Electroencephalography (EEG) is a promising technique because it is objective, non-invasive, and implant-compatible, but is nonetheless susceptible to massive artifacts generated by the prosthesis's electrical activity. CI artifacts can blur and distort brain responses; thus, it is crucial to develop reliable techniques to remove them from EEG recordings. Despite numerous artifact removal techniques used in previous studies, there is a paucity of documentation and consensus on the optimal EEG procedures to reduce these artifacts. Herein, and through a comprehensive review process, we provide a guideline for designing an EEG-CI experiment minimizing the effect of the artifact. We provide some technical guidance for recording an accurate neural response from CI users and discuss the current challenges in detecting and removing CI-induced artifacts from a recorded signal. The aim of this paper is also to provide recommendations to better appraise and report EEG-CI findings.

Motion and Muscle Artifact Removal Validation Using an Electrical Head Phantom, Robotic Motion Platform, and Dual Layer Mobile EEG

Motion and muscle artifacts can undermine signal quality in electroencephalography (EEG) recordings during locomotion. We evaluated approaches for recovering ground-truth artificial brain signals from noisy EEG recordings. We built an electrical head phantom that broadcast four brain and four muscle sources. Head movements were generated by a robotic motion platform. We recorded 128-channel dual layer EEG and 8-channel neck electromyography (EMG) from the head phantom during motion. We evaluated ground-truth electrocortical source signal recovery from artifact contaminated data using Independent Component Analysis (ICA) to determine: (1) the number of isolated noise sensor recordings needed to capture and remove motion artifacts, (2) the ability of Artifact Subspace Reconstruction to remove motion and muscle artifacts at contrasting artifact detection thresholds, (3) the number of neck EMG sensor recordings needed to capture and remove muscle artifacts, and (4) the ability of Canonical Correlation Analysis to remove muscle artifacts. We also evaluated source signal recovery by combining the best practices identified in aims 1-4. By including isolated noise and EMG recordings in the ICA decomposition, we more effectively recovered ground-truth artificial brain signals. A reduced subset of 32-noise and 6-EMG channels showed equivalent performance compared to including the complete arrays. Artifact Subspace Reconstruction improved source separation, but this was contingent on muscle activity amplitude. Canonical Correlation Analysis also improved source separation. Merging noise and EMG recordings into the ICA decomposition, with Artifact Subspace Reconstruction and Canonical Correlation Analysis preprocessing, improved source signal recovery. This study expands on previous head phantom experiments by including neck muscle source activity and evaluating artificial electrocortical spectral power fluctuations synchronized with gait events.

Group-level cortical and muscular connectivity during perturbations to walking and standing balance

Maintaining balance is a complex process requiring multisensory processing and coordinated muscle activation. Previous studies have indicated that the cortex is directly involved in balance control, but less information is known about cortical flow of signals for balance. We studied source-localized electrocortical effective connectivity dynamics of healthy young subjects (29 subjects: 14 male and 15 female) walking and standing with both visual and physical perturbations to their balance. The goal of this study was to quantify differences in group-level corticomuscular connectivity responses to sensorimotor perturbations during walking and standing. We hypothesized that perturbed visual input during balance would transiently decrease connectivity between occipital and parietal areas due to disruptive visual input during sensory processing. We also hypothesized that physical pull perturbations would increase cortical connections to central sensorimotor areas because of higher sensorimotor integration demands. Our findings show decreased occipito-parietal connectivity during visual rotations and widespread increases in connectivity during pull perturbations focused on central areas, as expected. We also found evidence for communication from cortex to muscles during perturbed balance. These results show that sensorimotor perturbations to balance alter cortical networks and can be quantified using effective connectivity estimation.

Cochlear implant artifact reduction on one channel Mismatch Negativity recordings based on Ensemble Empirical Mode Decomposition and Independent Component Analysis

Artifact generated by cochlear implants has been a problem for being able to register Mismatch Negativity (MMN) response. There are methods for reducing the artifact using multiple channels from the EEG but in this paper are presented the first results of a method using only the channel with the artifact using Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis (ICA). The first results showed that it was possible to get the MMN registers from the group of normal recordings and partially with the group of recordings from patients with cochlear implant. It is possible to suggest that EEMD in conjunction with ICA can be used for studies searching MMN.

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.

Transient visual perturbations boost short-term balance learning in virtual reality by modulating electrocortical activity

Immersive virtual reality can expose humans to novel training and sensory environments, but motor training with virtual reality has not been able to improve motor performance as much as motor training in real-world conditions. An advantage of immersive virtual reality that has not been fully leveraged is that it can introduce transient visual perturbations on top of the visual environment being displayed. The goal of this study was to determine whether transient visual perturbations introduced in immersive virtual reality modify electrocortical activity and behavioral outcomes in human subjects practicing a novel balancing task during walking. We studied three groups of healthy young adults (5 male and 5 female for each) while they learned a balance beam walking task for 30 min under different conditions. Two groups trained while wearing a virtual reality headset, and one of those groups also had half-second visual rotation perturbations lasting ~10% of the training time. The third group trained without virtual reality. We recorded high-density electroencephalography (EEG) and movement kinematics. We hypothesized that virtual reality training with perturbations would increase electrocortical activity and improve balance performance compared with virtual reality training without perturbations. Our results confirmed the hypothesis. Brief visual perturbations induced increased theta spectral power and decreased alpha spectral power in parietal and occipital regions and improved balance performance in posttesting. Our findings indicate that transient visual perturbations during immersive virtual reality training can boost short-term motor learning by inducing a cognitive change, minimizing the negative effects of virtual reality on motor training. NEW & NOTEWORTHY We found that transient visual perturbations in virtual reality during balance training can boost short-term motor learning by inducing a cognitive change, overcoming the negative effects of immersive virtual reality. As a result, subjects training in immersive virtual reality with visual perturbations have equivalent performance improvement as training in real-world conditions. Visual perturbations elicited cortical responses in occipital and parietal regions and may have improved the brain's ability to adapt to variations in sensory input.

Differentiation in Theta and Beta Electrocortical Activity between Visual and Physical Perturbations to Walking and Standing Balance

Human balance is a complex process in healthy adults, requiring precisely timed coordination among sensory information, cognitive processing, and motor control. It has been difficult to quantify brain dynamics during human balance control due to limitations in brain-imaging modalities. The goal of this study was to determine whether by using high-density electroencephalography (EEG) and independent component analysis, we can identify common cortical responses to visual and physical balance perturbations during walking and standing. We studied the responses of 30 healthy young adults to sensorimotor perturbations that challenged their balance. Subjects performed four 10 min trials of beam walking and tandem stance while either being mediolaterally pulled at the waist or viewing brief 20° field-of-view rotations in virtual reality. We recorded high-density EEG, motion capture, lower leg electromyography (EMG), and neck EMG. We hypothesized that both physical pull and visual rotation perturbations would elicit time-frequency fluctuations in theta (4-8 Hz) and beta (13-30 Hz) bands, with increased occipito-parietal activity during visual rotations compared with pull perturbations. Our results confirmed this hypothesis. For both perturbations, we found early theta synchronization and late alpha-beta (8-30 Hz) desynchronization following perturbation onset. This pattern was strongest in occipito-parietal areas during visual perturbations and strongest in sensorimotor areas during pull perturbations. These results suggest a similar time-frequency electrocortical pattern when humans respond to sensorimotor conflict, but with substantive differences in the brain areas involved for visual versus physical perturbations. Our findings may have important implications for assessing and training balance in individuals with and without motor disabilities.

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.

Toward an Improvement of the Analysis of Neural Coding

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.

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.

A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation

OBJECTIVE

We propose a combination of a constrained independent component analysis (cICA) with an ensemble empirical mode decomposition (EEMD) to analyze electroencephalographic recordings from depressed or schizophrenic subjects during olfactory stimulation.

APPROACH

EEMD serves to extract intrinsic modes (IMFs) underlying the recorded EEG time. The latter then serve as reference signals to extract the most similar underlying independent component within a constrained ICA. The extracted modes are further analyzed considering their power spectra.

MAIN RESULTS

The analysis of the extracted modes reveals clear differences in the related power spectra between the disease characteristics of depressed and schizophrenic patients. Such differences appear in the high frequency γ-band in the intrinsic modes, but also in much more detail in the low frequency range in the α-, θ- and δ-bands.

SIGNIFICANCE

The proposed method provides various means to discriminate both disease pictures in a clinical environment.

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.

Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition

BACKGROUND

Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches.

NEW METHOD

In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution.

RESULTS

The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis.

COMPARISON WITH EXISTING METHODS

Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity.

CONCLUSIONS

Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods.

Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data.