High-resolution EEG Research Articles

Anxious/depressed individuals exhibit disrupted frontotemporal synchrony during cognitive conflict encoding.

Anxiety and depressive disorders are associated with cognitive control deficits, yet their underlying neural mechanisms remain poorly understood. Here, we used high-resolution stereotactic EEG (sEEG) to determine how anxiety and/or depression modulates neural and behavioral responses when cognitive control is engaged in individuals with medically refractory epilepsy undergoing sEEG monitoring for surgical evaluation. We analyzed sEEG data recorded from frontotemporal regions of 29 participants (age range: 19-55, mean age: 35.5, female: 16/29) while they performed a Multi-Source Interference Task (MSIT) designed to elicit cognitive conflict. Neurobehavioral interviews, symptom rating scales, and clinical documentation were used to categorize participants as demonstrating anxiety and/or depression symptoms (A/D, n=13) or as epilepsy controls (EC, n=16). Generalized linear mixed-effects (GLME) models were used to analyze behavioral and neural data. Models of oscillatory power were used to identify brain regions within conflict-encoding networks in which coherence and phase locking values (PLV) were examined in A/D and EC. A/D participants demonstrated a greater conflict effect (response time slowing with higher cognitive load), without impairment in response time (RT) or accuracy compared to EC. A/D participants also showed significantly enhanced conflict-evoked theta (4-8Hz) and alpha (8-15Hz) power in the dorsolateral prefrontal cortex (dlPFC) and amygdala as well as widespread broadband activity in the lateral temporal lobe (LTL) compared to EC. Additionally, theta coherence and PLV between dlPFC-LTL and dlPFC-amygdala were reduced by conflict in A/D. Our findings suggest individuals with anxiety/depression symptoms exhibit heightened frontotemporal oscillatory activity and disrupted frontotemporal synchrony during cognitive conflict encoding, which may indicate a greater need for cognitive resources due to ineffective cognitive processing. These results highlight a potential role of frontotemporal circuits in conflict encoding that are altered in anxiety/depression, and may further inform future therapeutic interventions aimed at enhancing cognitive control in these populations.

High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting

Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution. Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N = 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated. Main results. For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA. Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.

MASER: Enhancing EEG Spatial Resolution With State Space Modeling.

Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes often suffer from low spatial resolution, hindering the accurate capture of intricate brain activity patterns. To address this issue, we propose MASER, a novel super-resolution approach for EEG recording. In MASER, we design the eMamba block for extracting EEG features based on the principles of state space models (SSMs). We further stack eMamba blocks to form a low-resolution feature extractor and a high-resolution signal predictor, which enhances the feature representation. During the training of MASER, we fully consider the characteristics of multidimensional biological series signals, incorporating a smoothness constraint loss to achieve more consistent high-resolution reconstructions. MASER pioneers EEG-oriented state space modeling, effectively capturing the temporal dynamics and latent states, thereby revealing complex neural interactions over time. Extensive experiments show that the proposed MASER outperforms the state-of-the-art methods in super-resolution quality on two public EEG datasets, with normalized mean square error reduced by 16.25% and Pearson correlation improved by 1.13%. Moreover, a case study of motor imagery recognition highlights the advantages conferred by high-resolution EEG signals. With a 4x increase in spatial resolution by MASER, the recognition accuracy improves by 5.74%, implying a significant performance elevation in brain-computer interface (BCI) command mapping. By enhancing the spatial resolution of EEG signals, MASER makes EEG-based applications more accessible, reducing cost and setup time while maintaining high performance across various domains such as gaming, education, and healthcare.

Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.

Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.

Deep EEG Superresolution via Correlating Brain Structural and Functional Connectivities.

Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of milliseconds, but its spatial resolution has often been lagging behind the increasing demands in neuroscience research or subject to limitations imposed by emerging neuroengineering scenarios, especially those centering on consumer EEG devices. Current superresolution (SR) methods generally do not suffice in the reconstruction of high-resolution (HR) EEG as it remains a grand challenge to properly handle the connection relationship amongst EEG electrodes (channels) and the intensive individuality of subjects. This study proposes a deep EEG SR framework correlating brain structural and functional connectivities (Deep-EEGSR), which consists of a compact convolutional network and an auxiliary fully connected network for filter generation (FGN). Deep-EEGSR applies graph convolution adapting to the structural connectivity amongst EEG channels when coding SR EEG. Sample-specific dynamic convolution is designed with filter parameters adjusted by FGN conforming to functional connectivity of intensive subject individuality. Overall, Deep-EEGSR operates on low-resolution (LR) EEG and reconstructs the corresponding HR acquisitions through an end-to-end SR course. The experimental results on three EEG datasets (autism spectrum disorder, emotion, and motor imagery) indicate that: 1) Deep-EEGSR significantly outperforms the state-of-the-art counterparts with normalized mean squared error (NMSE) decreased by 1% - 6% and the improvement of signal-to-noise ratio (SNR) up to 1.2 dB and 2) the SR EEG manifests superiority to the LR alternative in ASD discrimination and spatial localization of typical ASD EEG characteristics, and this superiority even increases with the scale of SR.

Alerting attention is sufficient to induce a phase-dependent behavior that can be predicted by frontal EEG.

Recent studies suggest that attention is rhythmic. Whether that rhythmicity can be explained by the phase of ongoing neural oscillations, however, is still debated. We contemplate that a step toward untangling the relationship between attention and phase stems from employing simple behavioral tasks that isolate attention from other cognitive functions (perception/decision-making) and by localized monitoring of neural activity with high spatiotemporal resolution over the brain regions associated with the attentional network. In this study, we investigated whether the phase of electroencephalography (EEG) oscillations predicts alerting attention. We isolated the alerting mechanism of attention using the Psychomotor Vigilance Task, which does not involve a perceptual component, and collected high resolution EEG using novel high-density dry EEG arrays at the frontal region of the scalp. We identified that alerting attention alone is sufficient to induce a phase-dependent modulation of behavior at EEG frequencies of 3, 6, and 8 Hz throughout the frontal region, and we quantified the phase that predicts the high and low attention states in our cohort. Our findings disambiguate the relationship between EEG phase and alerting attention.

The relationship between the interictal epileptiform discharge source connectivity and cortical structural couplings in temporal lobe epilepsy.

The objective of this study was to explore the relation between interictal epileptiform discharge (IED) source connectivity and cortical structural couplings (SCs) in temporal lobe epilepsy (TLE). High-resolution 3D-MRI and 32-sensor EEG data from 59 patients with TLE were collected. Principal component analysis was performed on the morphological data on MRI to obtain the cortical SCs. IEDs were labeled from EEG data and averaged. The standard low-resolution electromagnetic tomography analysis was performed to locate the source of the average IEDs. Phase-locked value was used to evaluate the IED source connectivity. Finally, correlation analysis was used to compare the IED source connectivity and the cortical SCs. The features of the cortical morphology in left and right TLE were similar across four cortical SCs, which could be mainly described as the default mode network, limbic regions, connections bilateral medial temporal, and connections through the ipsilateral insula. The IED source connectivity at the regions of interest was negatively correlated with the corresponding cortical SCs. The cortical SCs were confirmed to be negatively related to IED source connectivity in patients with TLE as detected with MRI and EEG coregistered data. These findings suggest the important role of intervening IEDs in treating TLE.

How salience enhances inhibitory control: An analysis of electro-cortical mechanisms

Stop-signal tasks (SSTs) combined with human electro-cortical recordings (Event-Related Potentials, ERPs) have revealed mechanisms associated with successful stopping (relative to failed), presumably contributing to inhibitory control. The corresponding ERP signatures have been labeled stop N1 (+/- 100-ms latency), stop N2 (200 ms), and stop P3 (160–250 ms), and argued to reflect more sensory-specific (N1) versus more generic (N2, P3) mechanisms. However, stop N1 and stop N2, as well as latencies of stop-P3, appear to be quite inconsistent across studies. The present work addressed the possible influence of stop-signal salience, expecting high salience to induce clear stop N1s but reduced stop N2s, and short-latency stop P3s. Three SST varieties were combined with high-resolution EEG. An imperative visual (go) stimulus was occasionally followed by a subsequent (stop) stimulus that signalled to withhold the just initiated response. Stop-Signal Reaction Times (SSRTs) decreased linearly from visual-low to visual-high-salience to auditory. Auditory Stop N1 was replicated. A C1-like visual evoked potential (latency < 100 ms) was observed only with high salience, but not robustly associated with successful versus failed stops. Using the successful-failed contrast a visual stop-N1 analogue (112–156 ms post-stop-signal) was identified, as was right-frontal stop N2, but neither was sensitive to salience. Stop P3 had shorter latency for high than for low salience, and the extent of the early high-salience stop P3 correlated inversely with SSRT. These results suggest that salience-enhanced inhibitory control as manifest in SSRTs is associated with generic rather than sensory-specific electrocortical mechanisms.

A large and rich EEG dataset for modeling human visual object recognition

The human brain achieves visual object recognition through multiple stages of linear and nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions. Through computational modeling we established the quality of this dataset in five ways. First, we trained linearizing encoding models that successfully synthesized the EEG responses to arbitrary images. Second, we correctly identified the recorded EEG data image conditions in a zero-shot fashion, using EEG synthesized responses to hundreds of thousands of candidate image conditions. Third, we show that both the high number of conditions as well as the trial repetitions of the EEG dataset contribute to the trained models’ prediction accuracy. Fourth, we built encoding models whose predictions well generalize to novel participants. Fifth, we demonstrate full end-to-end training of randomly initialized DNNs that output EEG responses for arbitrary input images. We release this dataset as a tool to foster research in visual neuroscience and computer vision.

EEG resting-state functional connectivity: evidence for an imbalance of external/internal information integration in autism

BackgroundAutism spectrum disorder (ASD) is associated with atypical neural activity in resting state. Most of the studies have focused on abnormalities in alpha frequency as a marker of ASD dysfunctions. However, few have explored alpha synchronization within a specific interest in resting-state networks, namely the default mode network (DMN), the sensorimotor network (SMN), and the dorsal attention network (DAN). These functional connectivity analyses provide relevant insight into the neurophysiological correlates of multimodal integration in ASD.MethodsUsing high temporal resolution EEG, the present study investigates the functional connectivity in the alpha band within and between the DMN, SMN, and the DAN. We examined eyes-closed EEG alpha lagged phase synchronization, using standardized low-resolution brain electromagnetic tomography (sLORETA) in 29 participants with ASD and 38 developing (TD) controls (age, sex, and IQ matched).ResultsWe observed reduced functional connectivity in the ASD group relative to TD controls, within and between the DMN, the SMN, and the DAN. We identified three hubs of dysconnectivity in ASD: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. These three regions also presented decreased current source density in the alpha band.ConclusionThese results shed light on possible multimodal integration impairments affecting the communication between bottom-up and top-down information. The observed hypoconnectivity between the DMN, SMN, and DAN could also be related to difficulties in switching between externally oriented attention and internally oriented thoughts.

Evaluation of skull conductivity using SCALE head tissue conductivity estimation using EEG.

Inaccurate estimation of skull conductivity is the largest impediment to high-resolution EEG source imaging because of its strong influence and wide variability across individuals. Nonetheless, there is yet no widely applied method for noninvasively measuring individual skull conductivity. We presented a skull conductivity and source location estimation algorithm (SCALE) for simultaneously estimating skull conductivity and the cortical distributions of 18-20 effective sources derived from the EEG data by independent component analysis (ICA). SCALE combines a realistic Finite Element Method (FEM) head model built from a magnetic resonance (MR) head image with the effective source scalp maps to estimate brain-to-skull conductivity ratio (BSCR) and to map the effective sources on the cortical surface. To estimate the robustness of SCALE BSCR estimates, we applied SCALE to MR image and high-density EEG data from ten participants, five having data from 2-3 different tasks and sessions. As expected, across participants SCALE BSCR estimates differed widely (mean 32.8, range 18-78). Within-participant SCALE BSCR estimates were far more consistent than between participants. By incorporating SCALE-optimized distributed EEG source localization, stable functional imaging of cortical EEG effective sources can become routine, giving relatively low-cost EEG imaging a spatial resolution compatible with other brain imaging results and uniquely capable for studying brain dynamics supporting thought and action in laboratory, virtual, and natural environments.

Investigation of automatic spindle detection in sleep EEG signals contaminated with noise and artifact sources

AbstractElectroencephalography (EEG) serves as the gold standard for noninvasive diagnosis of different types of sleep disorders such as sleep apnea, insomnia, narcolepsy, restless leg syndrome, and parasomnias. In this study, a novel automated cascade filter is introduced as a preprocessing tool for suppressing all noise and artifact interferences from sleep EEG signals before detecting sleep spindles. The multi-stage filter employs the Multi-Kernel Normalized Least Mean Square with Coherence-based Sparsification (MKNLMS-CS) algorithm in the first step to remove all artifact interferences while applying the 1-D patch-based Non-Local Means (NLM) algorithm in the subsequent step to remove all noise components. Three state-of-the-art automated spindle detection algorithms, namely Mc-Sleep, Spinky, and Spindler, are examined in EEG signals contaminated with noise and artifact components individually and concurrently. The spindle detection performance is investigated with real EEG data taken from the well-known DREAMS database, and the experimental results demonstrate the importance of the proposed multi-stage filter in enhancing the performance of spindle detection using the three spindle detection algorithms. This elucidates the robustness of the suggested multi-stage filter in providing high-resolution sleep EEG data from noisy EEG recordings. Also, experimental results reveal that Spinky algorithm outperforms Mc-Sleep and Spindler methods in detecting spindles for filtered EEG signals using several evaluation metrics, including accuracy (94.8% versus 92.0% and 94.6%), precision (53.4% versus 36.4% and 47.5%), specificity (97.3% versus 93.9% and 96.1%) and F1-score (58.2% versus 41.3% and 50.9%), respectively. This shows that combining the proposed multi-stage filter with Spinky algorithm outperforms the other two methods in detecting spindles in EEG signals, and it represents an efficient automated spindle detection system that achieves high diagnosis performance in terms of accuracy (94.8%), specificity (97.3%), and F1-score (58.2%).

Evolution of cross-frequency coupling between endogenous oscillations over the temporal cortex in very premature neonates.

Temporal theta activity in coalescence with slow-wave (TTA-SW) is one of the first neurobiomarkers of the neurodevelopment of perisylvian networks in the electroencephalography (EEG). Dynamic changes in the microstructure and activity within neural networks are reflected in the EEG. Slow oscillation slope can reflect synaptic strength, and cross-frequency coupling (CFC), associated with several putative functions in adults, can reflect neural communication. Here, we investigated the evolution of CFC, in terms of SW theta phase-amplitude coupling (PAC), during the course of very early development between 25 and 32 weeks of gestational age in 23 premature neonates. We used high-resolution EEG and dipole models as spatial filters to extract the source waveforms corresponding to TTA-SW. We also carried out nonlinear phase-dependent correlation measurements to examine whether the characteristics of the SW slopes are associated with TTA-SW coupling. We show that neurodevelopment leads to temporal accumulation of the SW theta PAC toward the trough of SW. Steepness of the negative going slope of SW determined the degree of this coupling. Systematic modulation of SW-TTA CFC during development is a signature of the complex development of local cortico-cortical perisylvian networks and distant thalamo-cortical neural circuits driving this nested activity over the perisylvian networks.

Rhythm Perception in Premature Neonates Through a Hierarchy of Cortical Processing: An Electrophysiological Study

Electroencephalography is the only clinically available technique that can address the premature neonate normal and pathological functional development week after week. The changes in the electroencephalogram (EEG) result from gradual structural and functional modifications that arise during the last trimester of pregnancy. Here, we review the structural changes over time that underlie the establishment of functional immature neural networks, the impact of certain anatomical specificities (fontanelles, connectivity, etc.) on the EEG, limitations in EEG interpretation, and the utility of high-resolution EEG (HR-EEG) in premature newborns (a promising technique with a high degree of spatiotemporal resolution). In particular, we classify EEG features according to whether they are manifestations of endogenous generators (i.e. theta activities that coalesce with a slow wave or delta brushes) or come from a broader network. Furthermore, we review publications on EEG in premature animals because the data provide a better understanding of what is happening in premature newborns. We then discuss the results and limitations of functional connectivity analyses in premature newborns. Lastly, we report on the magnetoelectroencephalographic studies of brain activity in the fetus. A better understanding of complex interactions at various structural and functional levels during normal neurodevelopment (as assessed using electroencephalography as a benchmark method) might lead to better clinical care and monitoring for premature neonates.

Cortical Sources of Resting State EEG Rhythms in Alzheimer’s, Parkinson’s, and Lewy Body Diseases

This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer’s disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both.For the classification exercise, the ANNs had two architectures that included stacked (autoencoding) hidden layers recreating input data in the output. The classification was based on LORETA source estimates from rsEEG activity recorded with 10–20 montage system (19 electrodes) and standard sMRI variables in 89 ADD and 45 healthy control participants taken from a national database.The ANN with stacked autoencoders and a deep leaning model representing both ADD and control participants showed classification accuracies in discriminating them of 80%, 85%, and 89% using rsEEG, sMRI, and rsEEG + sMRI features, respectively. The two ANNs with stacked autoencoders and a deep leaning model specialized for either ADD or control participants showed classification accuracies of 77%, 83%, and 86% using the same input features.The two architectures of ANNs using stacked (autoencoding) hidden layers consistently reached moderate to high accuracy in the discrimination between ADD and healthy control participants as a function of the rsEEG and sMRI features employed.The present results encourage future multi-centric, prospective and longitudinal cross-validation studies using high resolution EEG techniques and harmonized clinical procedures towards clinical applications of the present ANNs.

Impact of skull-to-brain conductivity ratio for high resolution EEG source localization

Objective. To measure the impact of skull-to-brain conductivity ratios on interictal spikes source localizations, using high resolution EEG (HR EEG). In previous studies, two ratios were mainly employed: 1/80 and 1/40. Consequences of the employed ratios on source localization results are poorly studied. Methods. Twenty patients with drug-resistant epilepsy were studied using HR EEG (sixty-four scalp electrodes). For each patient, three-layers realistic head models based on individual MRI were elaborated using boundary element model. For each interictal spike, source localization was performed six times, using six skull-to-brain conductivity ratios (1/80, 1/50, 1/40, 1/30, 1/20 and 1/10), exploring all the spectrum of values reported in the literature. We then measured distances between the different sources obtained and between the sources and the anterior commissure (in order to estimate sources depth). Results. We measured a mean distance of 5.3 mm (sd: 3 mm) between the sources obtained with 1/40 versus 1/80 ratio. This distance increased when the discrepancy between the two evaluated ratios increased. We measured a mean distance of 14.2 mm (sd: 4.9 mm) between sources obtained with 1/10 ratio versus 1/80 ratio. Sources localized using 1/40 ratio were 4.3 mm closer to the anterior commissure than sources localized using 1/80 ratio. Significance. Skull-to-brain conductivity ratio is an often-neglected parameter in source localization studies. The different ratios mainly used in the litterature (1/80 and 1/40) lead to significant differences in source localizations. These variations mainly occur in source depth. A more accurate estimation of skull-to-brain conductivity is needed to increase source localization accuracy. Abbreviations. ECD: equivalent current dipole; EIT: electric impedance tomography, HR EEG: High resolution Electroencephalography, IIS: Inter ictal spikes, MEG: Magnetoencephalography, MRI: Magnetic resonance imaging, mS/m: milli-Siemens/m, S/m: Siemens/m, SD: Standard deviation.

High-Resolution EEG Source Localization in Segmentation-Free Head Models Based on Finite-Difference Method and Matching Pursuit Algorithm.

Electroencephalogram (EEG) is a method to monitor electrophysiological activity on the scalp, which represents the macroscopic activity of the brain. However, it is challenging to identify EEG source regions inside the brain based on data measured by a scalp-attached network of electrodes. The accuracy of EEG source localization significantly depends on the type of head modeling and inverse problem solver. In this study, we adopted different models with a resolution of 0.5 mm to account for thin tissues/fluids, such as the cerebrospinal fluid (CSF) and dura. In particular, a spatially dependent conductivity (segmentation-free) model created using deep learning was developed and used for more realist representation of electrical conductivity. We then adopted a multi-grid-based finite-difference method (FDM) for forward problem analysis and a sparse-based algorithm to solve the inverse problem. This enabled us to perform efficient source localization using high-resolution model with a reasonable computational cost. Results indicated that the abrupt spatial change in conductivity, inherent in conventional segmentation-based head models, may trigger source localization error accumulation. The accurate modeling of the CSF, whose conductivity is the highest in the head, was an important factor affecting localization accuracy. Moreover, computational experiments with different noise levels and electrode setups demonstrate the robustness of the proposed method with segmentation-free head model.

A new automated multi-stage system of non-local means and multi-kernel adaptive filtering techniques for EEG noise and artifacts suppression

Context. Electroencephalography (EEG) signals are contaminated with diverse types of noises and artifacts, which greatly distort EEG recording and increase the difficulty in obtaining accurate diagnosis. Objective. This paper investigates, for the first time, multi-kernel normalized least mean square with coherence-based sparsification (MKNLMS-CS) algorithm for suppressing different artifact components, and the 1D patch-based non-local means (NLM) algorithm for eliminating white and colored noises. Approach. A novel multi-stage system based on combining the NLM algorithm with the MKNLMS-CS algorithm is proposed for eliminating different noise and artifact sources by targeting each noise or artifact component in a single stage. Main Results. The proposed approach is applied to clinical real EEG data, and the results reveal the superior performance of the proposed system in removing white and colored noises, suppressing different artifact components, preserving the important and tiny features of the original EEG signal, and keeping the morphology of EEG frequency components. Significance. The proposed multi-stage design succeeds not only to suppress different artifact components and noise sources under low and high noise conditions, but also to achieve accurate sleep spindle detection from the filtered high-quality EEG signals. This demonstrates the usefulness of the proposed approach for obtaining high-resolution EEG signal from noisy and contaminated EEG recordings.

Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis.

The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the expert classification of the ICs by visual inspection. Brain dynamics in two different groups of denoised EEG signals, reconstructed after having removed the artifactual ICs identified by either visual inspection or the automated methods, was assessed by calculating microstate topographies, microstate metrics (duration, occurrence, and coverage), and directional predominance (based on transition probabilities). No statistically significant differences between the expert and the automated classification of the artifactual ICs were found (p > 0.05). Cronbach’s α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. The total EEG signal variance explained by the sets of global microstate templates was about 80% for all denoised EEG datasets, with no significant differences between groups. For the differently denoised EEG datasets in the two recording conditions, we found that the global microstate templates and the sequences of global microstates were very similar (p < 0.01). Descriptive statistics and Cronbach’s α of microstate metrics highlighted no significant differences and excellent consistency between groups (p > 0.5). These results confirm the ability of the optimized fingerprint method and the ARCI approach to effectively remove physiological artifacts from EEG recordings while preserving global brain dynamics. They also suggest that microstate analysis could represent a novel approach for assessing the ability of an EEG denoising method to remove artifacts without altering brain dynamics.

A Hybrid Volume-Surface-Wire Integral Equation for the Anisotropic Forward Problem in Electroencephalography

Solving the electroencephalography (EEG) forward problem is a fundamental step in a wide range of applications including biomedical imaging techniques based on inverse source localization. State-of-the-art electromagnetic solvers resort to a computationally expensive volumetric discretization of the full head to account for its complex and heterogeneous electric profile. The more efficient, popular in biomedical imaging circles, but unfortunately oversimplifying Boundary Element Method (BEM) relies instead on a piecewise-uniform approximation that severely curbs its application in high resolution EEGs. This contribution lifts the standard BEM contraints by treating the local anisotropies with adequate wire and thin volume integral equations that are tailored to specific structures of the fibrous white matter and the inhomogeneous skull. The proposed hybrid integral equation formulation thereby avoids the full volumetric discretization of the head medium and allows for a realistic and efficient BEM-like solution of the anisotropic EEG forward problem. The accuracy and flexibility of the proposed formulation is demonstrated through numerical experiments involving both canonical and realistic MRI-based head models.