Low-density EEG Research Articles

Diagnostic yield of high-density versus low-density EEG: The effect of spatial sampling, timing and duration of recording

ObjectiveTo investigate the effect of spatial sampling and of recording duration on the diagnostic yield of EEG for identification of interictal epileptiform discharges (IEDs). Previous studies demonstrated that high-density (HD) recordings increased accuracy of localization compared to low-density (LD) recordings. MethodsWe have prospectively evaluated the effect of spatial sampling and of recording duration in patients who had short-term (ST) recordings with a HD array of 256 electrodes following long-term (LT) recordings with a LD array consisting of the standard IFCN array of 25 electrodes. IED clusters were identified in four datasets: LT-LD, ST-LD (spatially down-sampled to the standard IFCN array), ST-HD and a shortened (90 minutes) epoch of LT-LD. ResultsSixty consecutive patients were recruited. We identified 89 IED clusters totally. Two clusters were found by increasing spatial sampling from 25 to 256 electrodes. This modest increase was not statistically significant. Eight clusters were missed by reducing the recording duration to 90 minutes, as compared with the LT recordings (p = 0.003). ConclusionsRecording duration is more important for the diagnostic yield of EEGs than increasing spatial sampling beyond the standard IFCN electrode array. SignificanceThe standard IFCN electrode array provides sufficient spatial sampling for identification of the IEDs.

Plasticity of neonatal neuronal networks in very premature infants: Source localization of temporal theta activity, the first endogenous neural biomarker, in temporoparietal areas

Background Temporal theta slow-wave activity (TTA-SW) in premature infants is a specific signature of the early development of temporal networks, as it is observed at the turning point between nonsensory driven spontaneous local processing and cortical network functioning. Previous studies have demonstrated that preterms from 28 weeks of gestational age (wGA) are able to discriminate phonemes and voice, supporting the idea of a prior genetic structural or activity-dependent fingerprint that would prepare the auditory network to compute auditory information at the onset of thalamocortical connectivity. Objectives To specify the role in development and the specific location of TTA-SW. Methods We recorded TTA-SW in 26–32 wGA preterms. The rate of TTA-SW in response to click stimuli was evaluated using low-density EEG in 30 preterms. The sources of TTA-SW were localized by high-density EEG using different tissues conductivities, head models and mathematical models. Results We observed that TTA-SW is not sensory driven. Regardless of age, conductivities, head models and mathematical models, sources of TTA-SW were located adjacent to auditory and temporal junction areas. These sources become situated closer to the surface during development. Conclusion TTA-SW corresponds to spontaneous transient endogenous activities independent of sensory information at this period, which might participate in the implementation of auditory, language, memory, attention and or social cognition and represents a general interaction between the subplate and the cortical plate.

Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low-density EEG.

SummaryObjectiveElectrical source imaging (ESI) is used increasingly to estimate the epileptogenic zone (EZ) in patients with epilepsy. Directed functional connectivity (DFC) coupled to ESI helps to better characterize epileptic networks, but studies on interictal activity have relied on high‐density recordings. We investigated the accuracy of ESI and DFC for localizing the EZ, based on low‐density clinical electroencephalography (EEG).MethodsWe selected patients with the following: (a) focal epilepsy, (b) interictal spikes on standard EEG, (c) either a focal structural lesion concordant with the electroclinical semiology or good postoperative outcome. In 34 patients (20 temporal lobe epilepsy [TLE], 14 extra‐TLE [ETLE]), we marked interictal spikes and estimated the cortical activity during each spike in 82 cortical regions using a patient‐specific head model and distributed linear inverse solution. DFC between brain regions was computed using Granger‐causal modeling followed by network topologic measures. The concordance with the presumed EZ at the sublobar level was computed using the epileptogenic lesion or the resected area in postoperative seizure‐free patients.ResultsESI, summed outflow, and efficiency were concordant with the presumed EZ in 76% of the patients, whereas the clustering coefficient and betweenness centrality were concordant in 70% of patients. There was no significant difference between ESI and connectivity measures. In all measures, patients with TLE had a significantly higher (P < 0.05) concordance with the presumed EZ than patients with with ETLE. The brain volume accepted for concordance was significantly larger in TLE.SignificanceESI and DFC derived from low‐density EEG can reliably estimate the EZ from interictal spikes. Connectivity measures were not superior to ESI for EZ localization during interictal spikes, but the current validation of the localization of connectivity measure is promising for other applications.

EEG source localization comparing low-density EEG including subtemporal coverage with high density EEG (P4.5-004)

EEG source localization comparing low-density EEG including subtemporal coverage with high density EEG (P4.5-004)

From research to clinic: A sensor reduction method for high-density EEG neurofeedback systems

ObjectiveTo accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system. MethodsA linearly constrained minimum variance beamformer algorithm was used to select the 64 sensors which contributed most highly to the source signal. Monte Carlo-based sampling was then used to randomly generate a large set of reduced 32-sensors montages from the 64 beamformer-selected sensors. The reduced montages were then tested for their ability to reproduce the 128-sensors NF. The high-performing montages were then pooled and analyzed by a k-means clustering machine learning algorithm to produce an optimized reduced 32-sensors montage. ResultsNearly 4500 high-performing montages were discovered from the Monte Carlo sampling. After statistically analyzing this pool of high performing montages, a set of refined 32-sensors montages was generated that could reproduce the 128-sensors NF with greater than 80% accuracy for 72% of the test population. ConclusionOur Monte Carlo reduction method was used to create reliable reduced-sensors montages which could be used to deliver accurate NF in clinical settings. SignificanceA translational pathway is now available by which high-density EEG-based NF measures can be delivered using clinically accessible low-density EEG systems.

Detecting slow wave sleep and rapid eye movement stage using cortical effective connectivity

In recent neuroimaging research, there has been considerable interest in identifying neuromarkers of sleep. Automatic slow wave sleep (SWS) and rapid eye movement (REM) are two known phases of sleep. However, the level by which those changes contribute to brain interactions has not been well characterized. In recent years, it has been shown that brain connectivity measuring can be helpful in investigation of behavioral states of the brain. By considering the fact that brains have different states in different stages of sleep, the present work employs effective connectivity and machine-learning analysis to quantify and classify SWS and REM stages of sleep. We examine low-density 12-channel EEG data from 8 healthy participants during a full night of sleep. Data were epoched into 30-s windows and SWS and REM stages were labeled by a sleep consultant. Effective connectivity was quantified using a directed metric, generalized partial directed coherence, and measures were used as input features for a machine-learning system. A support vector machine classifier was used to solve 2 binary problems of REM vs. nREM and SWS vs. nSWS. Findings revealed an excellent balanced accuracy of 89.80 % in REM detection and 87.32 % in SWS detection. Overall, our work demonstrates a successful application of effective connectivity analysis and machine learning for sleep neuromarkers in EEG.

Compressibility of High-Density EEG Signals in Stroke Patients.

Stroke is a critical event that causes the disruption of neural connections. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. Intra- and extra-cellular currents are involved in the communication between neurons and the macroscopic effects of such currents can be detected at the scalp through electroencephalographic (EEG) sensors. EEG can be used to study the lesions in the brain indirectly, by studying their effects on the brain electrical activity. The primary goal of the present work was to investigate possible asymmetries in the activity of the two hemispheres, in the case one of them is affected by a lesion due to stroke. In particular, the compressibility of High-Density-EEG (HD-EEG) recorded at the two hemispheres was investigated since the presence of the lesion is expected to impact on the regularity of EEG signals. The secondary objective was to evaluate if standard low density EEG is able to provide such information. Eighteen patients with unilateral stroke were recruited and underwent HD-EEG recording. Each EEG signal was compressively sensed, using Block Sparse Bayesian Learning, at increasing compression rate. The two hemispheres showed significant differences in the compressibility of EEG. Signals acquired at the electrode locations of the affected hemisphere showed a better reconstruction quality, quantified by the Structural SIMilarity index (SSIM), than the EEG signals recorded at the healthy hemisphere (p < 0.05), for each compression rate value. The presence of the lesion seems to induce an increased regularity in the electrical activity of the brain, thus an increased compressibility.

An Algorithm for Automated, Noninvasive Detection of Cortical Spreading Depolarizations Based on EEG Simulations.

We present a novel signal processing algorithm for automated, noninvasive detection of cortical spreading depolarizations (CSDs) using electroencephalography (EEG) signals and validate the algorithm on simulated EEG signals. CSDs are waves of neurochemical changes that suppress the neuronal activity as they propagate across the brain's cortical surface. CSDs are believed to mediate secondary brain damage after brain trauma and cerebrovascular diseases like stroke. We address the following two key challenges in detecting CSDs from EEG signals: i) attenuation and loss of high spatial resolution information; and ii) cortical folds, which complicate tracking CSD waves. Our algorithm detects and tracks "wavefronts" of a CSD wave, and stitch together data across space and time to make a detection. To test our algorithm, we provide different models of CSD waves, including different widths of CSD suppressions and different patterns, and use them to simulate scalp EEG signals using head models of four subjects. Our results suggest that low-density EEG grids (40 electrodes) can detect CSD widths of 1.1cm on average, while higher density EEG grids (340 electrodes) can detect CSD patterns as thin as 0.43cm (less than minimum widths reported in prior works), among which single-gyrus CSDs are the hardest to detect because of their small suppression area. The proposed algorithm is a first step toward noninvasive, automated detection of CSDs, which can help in reducing secondary brain damages.

Functional connectivity analysis in EEG source space: The choice of method.

Functional connectivity (FC) is among the most informative features derived from EEG. However, the most straightforward sensor-space analysis of FC is unreliable owing to volume conductance effects. An alternative—source-space analysis of FC—is optimal for high- and mid-density EEG (hdEEG, mdEEG); however, it is questionable for widely used low-density EEG (ldEEG) because of inadequate surface sampling. Here, using simulations, we investigate the performance of the two source FC methods, the inverse-based source FC (ISFC) and the cortical partial coherence (CPC). To examine the effects of localization errors of the inverse method on the FC estimation, we simulated an oscillatory source with varying locations and SNRs. To compare the FC estimations by the two methods, we simulated two synchronized sources with varying between-source distance and SNR. The simulations were implemented for hdEEG, mdEEG, and ldEEG. We showed that the performance of both methods deteriorates for deep sources owing to their inaccurate localization and smoothing. The accuracy of both methods improves with the increasing between-source distance. The best ISFC performance was achieved using hd/mdEEG, while the best CPC performance was observed with ldEEG. In conclusion, with hdEEG, ISFC outperforms CPC and therefore should be the preferred method. In the studies based on ldEEG, the CPC is a method of choice.

Source reconstruction via the spatiotemporal Kalman filter and LORETA from EEG time series with 32 or fewer electrodes.

The clinical routine of non-invasive electroencephalography (EEG) is usually performed with 8-40 electrodes, especially in long-term monitoring, infants or emergency care. There is a need in clinical and scientific brain imaging to develop inverse solution methods that can reconstruct brain sources from these low-density EEG recordings. In this proof-of-principle paper we investigate the performance of the spatiotemporal Kalman filter (STKF) in EEG source reconstruction with 9-, 19- and 32- electrodes. We used simulated EEG data of epileptic spikes generated from lateral frontal and lateral temporal brain sources using state-of-the-art neuronal population models. For validation of source reconstruction, we compared STKF results to the location of the simulated source and to the results of low-resolution brain electromagnetic tomography (LORETA) standard inverse solution. STKF consistently showed less localization bias compared to LORETA, especially when the number of electrodes was decreased. The results encourage further research into the application of the STKF in source reconstruction of brain activity from low-density EEG recordings.

Towards affordable biomarkers of frontotemporal dementia: A classification study via network\u2019s information sharing

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer’s disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.

Plasticity of neonatal neuronal networks in very premature infants: Source localization of temporal theta activity, the first endogenous neural biomarker, in temporoparietal areas.

Temporal theta slow-wave activity (TTA-SW) in premature infants is a specific signature of the early development of temporal networks, as it is observed at the turning point between non-sensory driven spontaneous local processing and cortical network functioning. The role in development and the precise location of TTA-SW remain unknown. Previous studies have demonstrated that preterms from 28 weeks of gestational age (wGA) are able to discriminate phonemes and voice, supporting the idea of a prior genetic structural or activity-dependent fingerprint that would prepare the auditory network to compute auditory information at the onset of thalamocortical connectivity. They recorded TTA-SW in 26-32 wGA preterms. The rate of TTA-SW in response to click stimuli was evaluated using low-density EEG in 30 preterms. The sources of TTA-SW were localized by high-density EEG using different tissues conductivities, head models and mathematical models. They observed that TTA-SW is not sensory driven. Regardless of age, conductivities, head models and mathematical models, sources of TTA-SW were located adjacent to auditory and temporal junction areas. These sources become situated closer to the surface during development. TTA-SW corresponds to spontaneous transient endogenous activities independent of sensory information at this period which might participate in the implementation of auditory, language, memory, attention and or social cognition convergent and does not simply represent a general interaction between the subplate and the cortical plate. Hum Brain Mapp 38:2345-2358, 2017. © 2017 Wiley Periodicals, Inc.

The P3 cognitive ERP has at least some sensory modality-specific generators: Evidence from high-resolution EEG.

The P3 can arguably be named the most intensely studied ERP. In spite of more than 40 years of research, fundamental questions regarding the nature of its neural generators remain unresolved. While most studies conclude that the P3 is a true classical "endogenous" potential, and that its surface potential distribution remains constant across sensory modalities, these results are largely based on low-density EEG recordings, without the use of high-resolution methods such as the spherical spline Laplacian (SSL). Seventeen healthy participants performed a three-stimulus oddball task in visual and auditory modality while their EEG was recorded using a 128-channel system. Comparison of amplitude-normalized SSL estimated P3 brain-surface potentials, and analysis of spatial and temporal correlations revealed significant differences between visual and auditory evoked P3 topographies from target and distractor stimuli (but not target minus frequent or distractor minus frequent comparisons). Based on these results, we postulate the likely existence of at least some sensory modality-specific neuronal generators of the P3.

Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices

Today, Alzheimer’s disease (AD) diagnosis is carried out using subjective mental status examinations assisted in research by scarce and expensive neuroimaging scans and invasive laboratory tests; all of which render the diagnosis time-consuming, geographically confined and costly. Driven by these limitations, quantitative analysis of electroencephalography (EEG) has been proposed as a non-invasive and more convenient technique to study AD. Published works on EEG-based AD diagnosis typically share two main characteristics: EEG is manually selected by experienced clinicians to discard artefacts that affect AD diagnosis, and reliance on EEG devices with 20 or more electrodes. Recent work, however, has suggested promising results by using automated artefact removal (AAR) algorithms combined with medium-density EEG setups. Over the last couple of years, however, low-density, portable EEG devices have emerged, thus opening the doors for low-cost AD diagnosis in low-income countries and remote regions, such as the Canadian Arctic. Unfortunately, the performance of automated diagnostic solutions based on low-density portable devices is still unknown. The work presented here aims to fill this gap. We propose an automated EEG-based AD diagnosis system based on AAR and a low-density (7-channel) EEG setup. EEG data was acquired during resting-awake protocol from control and AD participants. After AAR, common EEG features, spectral power and coherence, are computed along with the recently proposed amplitude-modulation features. The obtained features are used for training and testing of the proposed diagnosis system. We report and discuss the results obtained with such system and compare the obtained performance with results published in the literature using higher-density EEG layouts.

The Role of Skull Modeling in EEG Source Imaging for Patients with Refractory Temporal Lobe Epilepsy.

We investigated the influence of different skull modeling approaches on EEG source imaging (ESI), using data of six patients with refractory temporal lobe epilepsy who later underwent successful epilepsy surgery. Four realistic head models with different skull compartments, based on finite difference methods, were constructed for each patient: (i) Three models had skulls with compact and spongy bone compartments as well as air-filled cavities, segmented from either computed tomography (CT), magnetic resonance imaging (MRI) or a CT-template and (ii) one model included a MRI-based skull with a single compact bone compartment. In all patients we performed ESI of single and averaged spikes marked in the clinical 27-channel EEG by the epileptologist. To analyze at which time point the dipole estimations were closer to the resected zone, ESI was performed at two time instants: the half-rising phase and peak of the spike. The estimated sources for each model were validated against the resected area, as indicated by the postoperative MRI. Our results showed that single spike analysis was highly influenced by the signal-to-noise ratio (SNR), yielding estimations with smaller distances to the resected volume at the peak of the spike. Although averaging reduced the SNR effects, it did not always result in dipole estimations lying closer to the resection. The proposed skull modeling approaches did not lead to significant differences in the localization of the irritative zone from clinical EEG data with low spatial sampling density. Furthermore, we showed that a simple skull model (MRI-based) resulted in similar accuracy in dipole estimation compared to more complex head models (based on CT- or CT-template). Therefore, all the considered head models can be used in the presurgical evaluation of patients with temporal lobe epilepsy to localize the irritative zone from low-density clinical EEG recordings.

Evaluation of spatial resolution and noise sensitivity of sLORETA method for EEG source localization using low-density headsets

Electroencephalography (EEG) has enjoyed considerable attention over the past century and has been applied for diagnosis of epilepsy, stroke, traumatic brain injury and other disorders where 3D localization of electrical activity in the brain is potentially of great diagnostic value. In this study we evaluate the precision and accuracy of spatial localization of electrical activity in the brain delivered by a popular reconstruction technique sLORETA applied to EEG data collected by two commonly used low-density headsets with 14 and 19 measurement channels, respectively. Numerical experiments were performed for a realistic head model obtained by segmentation of MRI images. The EEG source localization study was conducted with a simulated single active dipole, as well as with two spatially separated simultaneously active dipoles, as a function of dipole positions across the neocortex, with several different noise levels in the EEG signals registered on the scalp. The results indicate that while the reconstruction accuracy and precision of the sLORETA method are consistently high in the case of a single active dipole, even with the low-density EEG configurations considered in the present study, successful localization is much more problematic in the case of two simultaneously active dipoles. The quantitative analysis of the width of the reconstructed distributions of the electrical activity allows us to specify the lower bound for the spatial resolution of the sLORETA-based 3D source localization in the considered cases.

Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications

Objective. Recent studies have provided evidence that temporal envelope driven speech decoding from high-density electroencephalography (EEG) and magnetoencephalography recordings can identify the attended speech stream in a multi-speaker scenario. The present work replicated the previous high density EEG study and investigated the necessary technical requirements for practical attended speech decoding with EEG. Approach. Twelve normal hearing participants attended to one out of two simultaneously presented audiobook stories, while high density EEG was recorded. An offline iterative procedure eliminating those channels contributing the least to decoding provided insight into the necessary channel number and optimal cross-subject channel configuration. Aiming towards the future goal of near real-time classification with an individually trained decoder, the minimum duration of training data necessary for successful classification was determined by using a chronological cross-validation approach. Main results. Close replication of the previously reported results confirmed the method robustness. Decoder performance remained stable from 96 channels down to 25. Furthermore, for less than 15 min of training data, the subject-independent (pre-trained) decoder performed better than an individually trained decoder did. Significance. Our study complements previous research and provides information suggesting that efficient low-density EEG online decoding is within reach.

Combined EEG/MEG Can Outperform Single Modality EEG or MEG Source Reconstruction in Presurgical Epilepsy Diagnosis

We investigated two important means for improving source reconstruction in presurgical epilepsy diagnosis. The first investigation is about the optimal choice of the number of epileptic spikes in averaging to (1) sufficiently reduce the noise bias for an accurate determination of the center of gravity of the epileptic activity and (2) still get an estimation of the extent of the irritative zone. The second study focuses on the differences in single modality EEG (80-electrodes) or MEG (275-gradiometers) and especially on the benefits of combined EEG/MEG (EMEG) source analysis. Both investigations were validated with simultaneous stereo-EEG (sEEG) (167-contacts) and low-density EEG (ldEEG) (21-electrodes). To account for the different sensitivity profiles of EEG and MEG, we constructed a six-compartment finite element head model with anisotropic white matter conductivity, and calibrated the skull conductivity via somatosensory evoked responses. Our results show that, unlike single modality EEG or MEG, combined EMEG uses the complementary information of both modalities and thereby allows accurate source reconstructions also at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. EMEG is furthermore able to reveal the propagation pathway at later time points in agreement with sEEG, while EEG or MEG alone reconstructed only parts of it. Subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations can supply.

A statistically robust EEG re-referencing procedure to mitigate reference effect.

The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures that modify the recorded traces and seek to minimize the impact of reference electrode activity upon functions of the original EEG recordings. We provide a novel, statistically robust procedure that adapts a robust maximum-likelihood type estimator to the problem of reference estimation, reduces the influence of neural activity from the re-referencing operation, and maintains good performance in a wide variety of empirical scenarios. The performance of the proposed and existing re-referencing procedures are validated in simulation and with examples of EEG recordings. To facilitate this comparison, channel-to-channel correlations are investigated theoretically and in simulation. The proposed procedure avoids using data contaminated by neural signal and remains unbiased in recording scenarios where physical references, the common average reference (CAR) and the reference estimation standardization technique (REST) are not optimal. The proposed procedure is simple, fast, and avoids the potential for substantial bias when analyzing low-density EEG data.

42. Detection of emotional states based on scalp and source maps of EEG

Recent methods of emotional states detection are mostly based on low-density EEG recording and analysis of the scalp activity. This disadvantage should result in the low classification accuracy of emotions. We extend the research in this area to high-density EEG recordings (118 electrodes) and source localization methods. Our experiment consisted of 100 affective pictures (mostly IAPS dataset) standing for three different emotional categories (positive, neutral and negative). They were presented to 30 participants and the EEG signal was recorded. The spectral bands were calculated for scalp projection and further ICA decomposition and dipole fitting was applied for source localization. We analyzed classification errors for specific emotional categories. The goal of our study was to identify differences between low-density and high-density density EEG recordings. We were interested in the difference between scalp maps and the localized sources of the EEG signal. These differences should clarify possible sources of classification errors. We also analyzed whether differences in the spectral activity of specific emotions on the scalp correlates with the source signals. The advantages and disadvantages of specific methods are discussed.