Brain Topography

Whenever one attempts to comb a hairy ball flat, there will always be at least one tuft of hair at one point on the ball. This seemingly worthless sentence is an informal description of the hairy ball theorem, an invaluable mathematical weapon that has been proven useful to describe a variety of physical/biological processes/phenomena in terms of topology, rather than classical cause/effect relationships. In this paper we will focus on the electrical brain field - electroencephalogram (EEG). As a starting point we consider the recently-raised observation that, when electromagnetic oscillations propagate with a spherical wave front, there must be at least one point where the electromagnetic field vanishes. We show how this description holds also for the electric waves produced by the brain and detectable by EEG. Once located these zero-points in EEG traces, we confirm that they are able to modify the electric wave fronts detectable in the brain. This sheds new light on the functional features of a nonlinear, metastable nervous system at the edge of chaos, based on the neuroscientific model of Operational Architectonics of brain-mind functioning. As an example of practical application of this theorem, we provide testable previsions, suggesting the proper location of transcranial magnetic stimulations coils to improve the clinical outcomes of drug-resistant epilepsy.

The nervous activity of the brain takes place in higher-dimensional functional spaces. Indeed, recent claims advocate that the brain might be equipped with a phase space displaying four spatial dimensions plus time, instead of the classical three plus time. This suggests the possibility to investigate global visualization methods for exploiting four-dimensional maps of real experimental data sets. Here we asked whether, starting from the conventional neuro-data available in three dimensions plus time, it is feasible to find an operational procedure to describe the corresponding four-dimensional trajectories. In particular, we used quaternion orthographic projections for the assessment of electroencephalographic traces (EEG) from scalp locations. This approach makes it possible to map three-dimensional EEG traces to the surface of a four-dimensional hypersphere, which has an important advantage, since quaternionic networks make it feasible to enlighten temporally far apart nervous trajectories equipped with the same features, such as the same frequency or amplitude of electric oscillations. This leads to an incisive operational assessment of symmetries, dualities and matching descriptions hidden in the very structure of complex neuro-data signals.

Modern automated human head segmentations can generate high-resolution computational meshes involving many non-nested tissues. However, most source reconstruction software is limited to 3 -4 nested layers of low resolution and a small number of dipolar sources[~] 10, 000. Recently, we introduced modeling techniques for source reconstruction of magnetoencephalographic (MEG) signals using the reciprocal approach and the boundary element fast multipole method (BEM-FMM). The technique of BEM-FMM can process both nested and non-nested models with as many as 4 million surface elements. In this paper, we present an analogue technique for source reconstruction of electroencephalographic (EEG) signals based on cortical global basis functions. The present work uses Helmholtz reciprocity to relate the reciprocally-generated lead-field matrices to their direct counterpart, while resolving the issue of possible biases toward the reference electrode. Our methodology is tested with experimental EEG data collected from a cohort of 12, young and healthy, volunteers subjected to intermittent photic stimulation (IPS). Our novel high-resolution source reconstruction models can have impact on mental health screening as well as brain-computer inter-faces.

Neural oscillations excitability shape sensory, motor, and cognitive processes of the brain operation. To quantify the spectrum of brain magnetic and electrical recordings has become the main methodology to study neural oscillations. However, there is still lacking a valid approach, although the literatures on the spectrum decomposition is vast. In this work, we fit the neural spectrum by means of the Expectation Maximization algorithm, where the E step turns into a Winner filtering to separate multiple components and the M step is to fit each component by minimizing the smoothness penalized Whittle likelihood and shape-restricted regression, say, monotonicity, that is able to fit the diverse shape of each component. The decomposition allows characterizing the oscillation amplitude, resonance frequency, bandwidth, skewness, kurtosis, and slope of each component. This approach is termed as Xi rhythms, with Xi and rhythms standing for the background activity and multiple peaks. We apply it to: 1) multinational EEG database consisting of 535 subjects to create the quantitative spectrum norms (QSN); 2) large sample intracranial EEG (iEEG) dataset to infer the oscillations from recorded region to unrecorded areas within one subject and over inter-individuals and create the full brain high resolution statistical spectra parametric mapping. The statistical spectrum parameter mapping of iEEG promisingly provides an atlas and creates a norm for neural oscillations and quantitative electrophysiology study which can gain us more insightful understanding to brain dynamics, cognitive process and mental disorders.

In the resting state (closed or open eyes) the electroencephalogram (EEG) and the magnetoencephalogram (MEG) exhibit rhythmic brain activity is typically the 10 Hz alpha rhythm. It has a topographic frequency spectral distribution that is, quite similar for both modalities--something not surprising since both EEG and MEG are generated by the same basic oscillations in thalamocortical circuitry. However, different physical aspects underpin the two types of signals. Does this difference lead to a different distribution of reconstructed sources for EEG and MEG rhythms? This question is important for the transferal of results from one modality to the other but has surprisingly received scant attention till now. We address this issue by comparing eyes open EEG source spectra recorded from 70 subjects from the Cuban Human Brain Mapping project with the MEG of 70 subjects from the Human Connectome Project. Source spectra for each voxel and frequencies between 0-50Hz with 100 frequency points were obtained via a novel sparse-covariance inverse method (BC-VARETA) based on individualized BEM head models with subject-specific regularization parameters (noise to signal ratio). We performed a univariate permutation-based rank test among sources of both modalities and found out no differences. To carry out an unbiased comparison we computed sources from eLORETA and LCMV, performed the same permutation-based comparison, and found the same results we got with BC-VARETA.

In this study, we subject the conventional physiological interpretation of independent component analysis (ICA) applied to EEG, the small-patch model, to systematic falsification, and propose an alternative large-patch model. The small-patch model assumes that ICs correspond to localized cortical patches with < 1 cm{superscript 2}. However, this assumption has remained unvalidated. The small-patch model predicts that approximately 70% of sources are localized within sulci up to 15 mm deep, with rapidly changing dipole orientations across the cortex. In contrast, the large-patch model (>6-10 cm{superscript 2}) predicts relatively stable radial orientations accompanied by physiologically implausible source depths due to depth bias. First, we conducted a stimulation study using a forward-inverse modeling framework with a four-layer head conductor model. We confirmed that depth bias emerges when a single equivalent dipole is fitted to a potential field generated by a broad array of parallel dipoles. This observation led to the key hypothesis that the presence of depth bias in empirical data would favor the large-patch model. Second, we analyzed resting-state EEG from two European open datasets comprising 820 recordings (62-64 channels), yielding dipole depth and orientation distributions for nearly 15,000 qualified brain ICs. Results showed that more than 80% of ICs were localized at physiologically implausible depths (19-26 mm), favoring the large-patch model. A novel dipole-orientation analysis revealed broad, low-spatial-frequency structure in dipole orientations, further supporting the large-patch model. We conclude that the revised physiological interpretation of ICA aligns with electrophysiological literature and computational insights into EEG-specific spatial scaling laws. Significance statementIndependent component analysis (ICA) has been proposed as a promising tool for computational neuroscience using human scalp EEG. One of the original proponents introduced a physiological model suggesting that anatomically accurate neural sources could be directly recovered by applying ICA to EEG data. However, we found that this model assumes EEG generation within cortical patches smaller than 1 cm{superscript 2}, which has remained unvalidated for over a decade and requires revision. Using both simulation and empirical EEG datasets, we demonstrated that our alternative model, involving larger cortical patches (>6-10 cm{superscript 2}), better fits the electrophysiological generative model of scalp EEG signals. We conclude that our large-patch model provides an updated, more physiologically plausible interpretation of ICA results.

Considering the properties of the empirical mode decomposition to extract from a signal its natural oscillatory components known as intrinsic mode functions (IMFs), the spectral analysis of these IMFs could provide a novel alternative for the quantitative EEG analysis without a priori establish more or less arbitrary band limits. This approach has begun to be used in the last years for studies of EEG records of patients included in database repositories or including a low number of individuals or of limited EEG leads, but a detailed study in healthy humans has not yet been reported. Therefore, in this study the aims were to explore and describe the main spectral indices of the IMFs of the EEG in healthy humans using a method based on the FFT and another on the Hilbert-Huang transform (HHT). The EEG of 34 healthy volunteers was recorded and decomposed using a recently developed multivariate empirical mode decomposition algorithm. Extracted IMFs were submitted to spectral analysis with, and the results were compared with an ANOVA test. The first six decomposed IMFs from the EEG showed frequency values in the range of the classical bands of the EEG (1.5 to 56 Hz). Both methods showed in general similar results for mean weighted frequencies and estimations of power spectral density, although the HHT is recommended because of its better frequency resolution. It was shown the presence of the mode-mixing problem producing a slight overlapping of spectral frequencies mainly between the IMF3 and IMF4 modes.

Background: Electroencephalographic (EEG) potentials evoked by transcranial magnetic stimulation (TMS) offer a direct window into cortical dynamics. Yet, a systematic exploration of their morphological features, analogous to sensory-evoked potentials, is lacking, especially for stimulation outside the motor cortex. Aim: To obtain region-specific properties of frontal, parietal and occipital networks from the time course of TMS-evoked potentials (TEPs). Materials and Methods: We implemented and applied an automatic procedure to compute peak-to-peak amplitude, peak latency, and inter-peak interval of TEPs recorded from 40 neurotypical subjects stimulated over left occipital (n=25), parietal (n=25), and frontal (n=25) cortices. Results: Occipital TEPs showed the largest peak-to-peak amplitude and longest latency of the first waveform component, independently of stimulation intensity and consistent with the recruitment of a large patch of densely interconnected neurons. Concerning later components, both latency and inter-peak interval systematically decreased along the posterior-to-anterior axis, reflecting progressively faster recurrent dynamics from the alpha-dominated occipital circuitry to the tightly coupled loops between frontal cortex and subcortical structures. Parietal TEPs showed intermediate amplitude and latency measures, consistent with the heterogeneous cytoarchitectonic and connectional organization of the superior parietal cortex. Conclusions: Our findings suggest that TEP morphology is shaped by the distinct properties of the stimulated networks, with early amplitude reflecting the extent of local recruitment and later temporal features tracking the rhythm of recurrent activity. This work offers a mechanistically grounded and practically accessible approach, also released as a Python-based tool, that allows to characterize cortical reactivity across different brain-states and populations.

Originally applied to alpha oscillations in the 1970s, MS analysis has since been used to decompose mainly broadband EEG signals (e.g. 1-40 Hz). We hypothesized that MS decomposition within separate, narrow frequency bands could provide more fine-grained information for capturing the spatio-temporal complexity of multichannel EEG. In this study using a large open-access dataset (n=203), we decomposed EEG recordings into 4 classical frequency bands (delta, theta, alpha, beta) in order to compare their individual MS segmentations using mutual information as well as traditional MS measures (e.g. mean duration, time coverage). Firstly, we confirmed that MS topographies were spatially equivalent across all frequencies, matching the canonical broadband maps (A, B, C, D). Interestingly however, we observed strong informational independence of MS temporal sequences between spectral bands, together with significant divergence in traditional MS measures. For example, relative to broadband, alpha/beta band dynamics displayed greater time coverage of maps A & B, while map D was more prevalent in delta/theta bands. Moreover, by using a frequency-specificMS taxonomy (e.g. {theta}A, C), we were able to predict the eyes-open vs closed-behavioural state significantly better using alpha-band MS features compared with broadband ones (80% vs 73% accuracy). Overall, our findings demonstrate the value and validity of spectrally-specific MS analyses, which may prove useful for identifying new neural mechanisms in fundamental research and/or for biomarker discovery in clinical populations.

Language and consequently the ability to transmit and spread complex information is unique to the human species. The disruptive event of the introduction of large language models has shown that the ability to process language alone leads to incredible abilities and, to some extent, to intelligence. However, how language is processed in the human brain remains elusive. Many insights originate from fMRI studies, as the high spatial resolution of fMRI devices provides valid information about where things happen. Nevertheless, the limited temporal resolution prevents us from gaining a deep understanding on the underlying mechanisms. In this study, we performed combined EEG and MEG measurements in 29 healthy right-handed human subjects during the presentation of continuous speech. We compared the evoked potentials (ERPs and ERFs) for different word types in source space and sensor space across the whole brain. We found characteristic spatio-temporal patterns for different word types (nouns, verbs) especially at latencies of 300ms to 1 s. This is further emphasized by the fact that we observe these effects in two pre-defined sub-samples of the data set (exploration and validation sample). We expect this study to be the starting point for further evaluations of semantic and syntactic processing in the brain.

ObjectiveEvaluate the reliability of neural components obtained from the appli-cation of the group ICA (gICA) methodology to resting-state EEG datasets acquired from multiple sites. MethodsFive databases from three sites, covering a total of 292 healthy subjects, were analyzed. Each dataset was segmented into groups of 15 subjects, for a total of 19 groups. Data were pre-processed using an automatic pipeline leveraging robust average referencing, wavelet-ICA and automatic rejection of epochs. On each group, stable gICA decompositions were calculated using the ICASSO methodology through a range of orders of decompositions. Each order was characterized by reliability and neuralness metrics, which were evaluated to select a single order of decomposition. Finally, using the decompositions of the selected order, a clustering analysis was performed to find the common components across the 19 groups. Each cluster was characterized by the mean scalp map, its dipole generator with its localization in Talairach coordinates, the spectral behavior of the associated time-series of the components, the assigned ICLabel class and metrics that reflected their reproducibility. ResultsLower order of decompositions benefits the gICA methodology. At this, using an order of ten, the number of reproducible components with high neuronal information tends to be around nine. Of these, the bilateral motor, frontal medial, and occipital neuronal components were the most reproducible across the different datasets, appearing in more than 89% of the 19 groups evaluated. ConclusionWe developed a workflow that allows finding reproducible spatial filters between different data sets. This contributes to the improvement of the spatial resolution of the EEG as a brain mapping technique.

BackgroundEEG is the oldest tool for studying human cognitive function, but it is blamed to be useless because its poor spatial resolution despite it excellent temporal discrimination. Such comments arise from a reductionist point of view about the cerebral function. However, if the brain is assumed to be a distributed processing system recruiting different types of cells widely distribute over the cortex, then EEG temporal resolution and the many different tools available for its analysis, turn it the tool of choice to investigate human cognition. ProposalTo better understand the different types of information encoded in the recorded cortical electrical activity, a clear model of the cortical structure and function of the different cortical column layers is presented and discussed. Taking this model into consideration, different available techniques for EEG analysis are discussed, under the assumption that tool combination is a necessity to provide a full comprehension of dynamics of the cortical activity supporting human cognition. MethodologyThe present approach combines many of the existing methods of analysis to extract better and richer information from the EEG, and proposes additional analysis to better characterize many of the EEG components identified by these different methods. AnalysisData on language understanding published elsewhere are used to illustrate how to use this combined and complex EEG analysis to disclose important details of cognitive cerebral dynamics, which reveal that cognitive neural circuits are scale free networks supporting entrainment of a large number of cortical column assemblies distributed all over the cortex. ConclusionsReasoning is then assumed to result from a well orchestrated large scale entrainment

Humans are equipped with the so-called Mental Time Travel (MTT) ability, which allows them to consciously construct and elaborate past or future scenes. The mechanisms underlying MTT remain elusive. This study focused on the late positive potential (LPP) and alpha oscillations, considering that LPP covaries with the temporal continuity whereas the alpha oscillations index the temporal organization of perception. To that end, subjects were asked to focus on performing two mental functions engaging working memory, which involved mental self-projection into either the present-past (PP) border or the present-future (PF) border. To evaluate underlying mechanisms, the evoked frontal late positive potentials (LPP) as well as their cortical sources were analyzed via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique. The LPP amplitudes - in the left lateral prefrontal areas that were elicited during PF tasks - were significantly higher than those associated with PP, whereas opposite patterns were observed in the central and right prefrontal areas. Crucially, the LPP activations of both the PP and PF self-projections overlapped with the brains default mode network and related interacting areas. Finally, we found enhanced alpha-related activation with respect to PP in comparison to PF, predominantly over the right hemisphere central brain regions (specifically, the precentral gyrus). These findings confirm that the two types of self-projection, as reflected by the frontally-distributed LPP, share common cortical resources that recruit different brain regions in a balanced way. This balanced distribution of brain activation might signify that biological time tends to behave in a homeostatic way.

Ageing of the human brain was studied using large array of experimental data. The magnetic encephalograms and magnetic resonance images of the head were obtained from the open archive CamCAN. Bad data were rejected, then functional tomograms were found - the spatial distribution of elementary spectral components. Physiological noise was eliminated by joint analysis of the functional tomograms and magnetic resonance images. By massively solving the inverse problem, multichannel spectra were transformed into time series of the power of elementary current dipoles. Age-related changes in the electrical power of various brain rhythms were examined. It was found that the summary electrical activity of the brain is constant throughout a persons life. The electric power is redistributed during the lifetime: delta rhythm is diminishing, giving slow rise to all other rhythms.

The present study applied spectral Granger causality analysis to electroencephalographic (EEG) recordings obtained during Focused Attention Meditation (FAM), Open Monitoring Meditation (OMM), and Loving Kindness Meditation (LKM) in highly experienced meditators. The aim of the investigation was to uncover distinct connectivity signatures associated with each meditation style by examining the strength, frequency band, and direction of inter-regional information transfers. These differences were expected to highlight the neural grounds of the cognitive and affective state of each meditative practice. Multivariate Granger causality (GC) was computed from high-resolution EEG signals recorded from long-term meditators (n = 22) in four conditions: rest, FAM, OMM, and LKM. GC was analyzed in the frequency domain for key cortical regions (frontal and parietal) in the two hemispheres to compare frequency-specific directed connectivity between rest and each meditation type. Main results demonstrated that each meditation state produced highly specific alterations in information transfer relative to rest. In FAM, there was significant reduction in posterior-to-anterior GC in the alpha and beta bands, and decreased multi-spectral inter-hemispheric frontal GC pointing to attenuated bottom-up sensory and associative inputs. In OMM, multi-spectral GC was significantly increased from the left hemisphere to the right posterior cortex implying expanded awareness in the right posterior regions through enhanced top-down modulation by the left-hemisphere. The distinctive features of LKM profile were the inter-hemispheric symmetry, the posterior-anterior bi-directionality, and the specific beta-band engagement, implying a co-activation of systems that support an emotionally balanced stance, equanimity and pro-social attitude. These novel findings demonstrate that the direction and frequency specificity of information flows provide complementary insights into neural processes underlying distinct meditative states.

AO_SCPLOWBSTRACTC_SCPLOWThis study explores differences in spatiotemporal cortical dynamics between five people with chronic post-stroke aphasia and five healthy subjects. Electroencephalography was recorded during picture naming in both groups. Frequency-specific Global Field Power (GFP) results showed that the delta band has higher power to discriminate between healthy subjects and people with aphasia (PWA) than theta and alpha bands. EEG topologies computed at the time of GFP peaks in the delta band revealed strong activation oscillating between posterior and anterior areas in PWA. On the other hand, EEG topologies from healthy subjects were variable. Then, Spatial ERP (S-ERP) were developed to add spatial resolution to classical ERP analysis. S-ERP and associated analyses confirmed the previously observed oscillatory pattern in the delta frequency band among PWA during picture naming. This oscillating pattern was alternating between occipital and prefrontal areas with almost opposite phases, a characteristic not observed in healthy subjects. In addition, all PWA performed well on the picture naming task, suggesting that this oscillating pattern may be a cortical adaptation mechanism enabling them to succeed. The observation of large-scale oscillating delta activity across the scalp in all post-stroke subjects who have substantially recovered from aphasia holds the potential to inspire innovative rehabilitation methods employing non-invasive brain stimulation techniques such as transcranial alternating current stimulation (tACS).

Growing evidence suggests that travelling waves are functionally relevant for cognitive operations in the brain. Several electroencephalography (EEG) studies report on a perceptual alpha-echo, representing the brain response to a random visual flicker, propagating as a travelling wave across the cortical surface. In this study, we ask if the propagating activity of the alpha-echo is best explained by a set of discrete sources mixing at the sensor level rather than a cortical travelling wave. To this end, we presented participants with gratings modulated by random noise and simultaneously acquired the ongoing MEG. The perceptual alpha-echo was estimated using the temporal response function linking the visual input to the brain response. At the group level, we observed a spatial decay of the amplitude of the alpha-echo with respect to the sensor where the alpha-echo was the largest. Importantly, the propagation latencies consistently increased with the distance. Interestingly, the propagation of the alpha-echoes was predominantly centro-lateral, while EEG studies reported mainly posterior-frontal propagation. Moreover, the propagation speed of the alpha-echoes derived from the MEG data was around 10 m/s, which is higher compared to the 2 m/s reported in EEG studies. Using source modelling, we found an early component in the primary visual cortex and a phase-lagged late component in the parietal cortex, which may underlie the travelling alpha-echoes at the sensor level. We then simulated the alpha-echoes using realistic EEG and MEG forward models by placing two sources in the parietal and occipital cortices in accordance with our empirical findings. The two-source model could account for both the direction and speed of the observed alpha-echoes in the EEG and MEG data. Our results demonstrate that the propagation of the perceptual echoes observed in EEG and MEG data can be explained by two sources mixing at the scalp level equally well as by a cortical travelling wave. This conclusion however does not put into question continuous travelling waves reported in intracranial recordings.

Efficient language production requires rapid interactions between different brain areas. These interactions can be severely affected by brain lesions. However, the neurophysiological correlates of the spatiotemporal dynamics during language production are not well understood. The current pilot study explores differences in spatiotemporal cortical dynamics between five subjects with post-stroke aphasia and five control subjects. Electroencephalography was recorded during picture naming in both groups. Average-based analyses (event-related potential (ERP), frequency-specific Global Field Power (GFP)), reveal a strong synchronization of cortical oscillations, especially within the first 600ms post-stimulus, with a time shift between participants with aphasia and control subjects. ERPs and the corresponding brain microstates indicate coordinated brain activity alternating mainly between frontal and occipital zones. This behavior can be described as standing waves between two main sources. At the single-trial scale, traveling waves (TW) were identified from both phase and amplitude analyses. The spatiotemporal distribution of amplitude TW reveals subject-specific organization of several interconnected hubs. In patients with aphasia this spatial organization of TW reveals zones with no TW notably in the vicinity of stroke lesions. The present results provide important hints for the hypothesis that TW contribute to the synchronization and communication between different brain areas especially by interconnecting cortical hubs. Moreover, our findings show that cortical dynamics is affected by brain lesions. Contribution to the FieldO_LISpatiotemporal cortical dynamics of individual trials reveals the presence of phase and amplitude traveling waves. C_LIO_LIExploration of traveling waves on the 2D cortical surface reveals the presence of interconnected epicenters or hubs in all subjects. C_LIO_LIThe spatiotemporal distribution of traveling waves shows a higher density in the prefrontal area for people with aphasia than for healthy subjects. C_LIO_LIFor subjects with aphasia, a sparser density of traveling waves is observed in the approximated lesion area. C_LIO_LIEvent-related potential analyses reveal a consistent alternating activity between the frontal and occipital regions. C_LIO_LISubjects with aphasia present a larger and/or delayed contribution in the delta range in the GFP patterns compared to control subjects. C_LI

The objective of the present study was to characterize the effects of long-term meditation (LTM) on directed connectivity patterns during resting-state and meditative brain states. Specifically, it was aimed to identify major cortical sources of information flow and target regions of information influx, and to reveal which frequency-specific oscillatory networks are critically involved in directing information flux in experienced meditators. Multivariate Granger causality (GC) was computed from high-resolution EEG signals recorded from long-term (LTM, n = 22) and short-term meditators (STM, n = 17) in four conditions: rest, Focused Attention Meditation, Open Monitoring Meditation, and Loving Kindness Meditation. GC was analyzed in the time and frequency domains to assess frequency-specific networks supporting the directed connectivity between key cortical regions (frontal and parietal) in the two hemispheres. According to the results, long-practice meditation was characterized by a significant increase of information flow (1) from posterior to frontal cortical regions, and (2) across frontal regions of the two hemispheres. These dominant transfers were supported by multi-spectral oscillatory networks involving theta, alpha and beta frequency bands, with most prominent expression of GC alpha peak. This pattern of enhanced information transfer in LTM relative to STM was observed in both resting state and each meditation state. These results suggest that long-term meditation is associated with a shift in resting-state brain dynamics toward reduced reliance on slow, undirected intrinsic oscillations, and enhanced directional connectivity in frequencies linked to attention and cognitive control. The dominant posterior-to-anterior directionality points to a reorganization of cognitive control networks that may support the phenomenological qualities of extensive meditation (sustained attention, internal attention, present-moment awareness, and reduced cognitive elaboration). The similarity of between-group differences in directionality patterns across states points to a neuroplastic effect of long-term meditation and highlights meditation as a potential model for investigating adaptive neuroplasticity in large-scale brain networks.

BackgroundThe EEG constitutes a powerful neuroimaging technique for assessing functional brain impairment in COVID-19 patients. ObjectiveThe current investigation compared the EEG among COVID-19 survivors, close contacts and the Cuban EEG normative database, using semi-quantitative visual EEG inspection, quantitative and the current source density measures EEG analysis. MethodsThe resting-state EEG activity, quantitative QEEG, and VARETA inverse solution, were evaluated in 173 subjects: 87 patients confirmed cases by the positive reverse transcription polymerase chain reaction (RT-PCR), 86 close contacts (negative PCR) and the Cuban EEG normative database. All patients were physical, neurological, and clinically assessed using neurological retrospective survey and version 2.1 of the Schedules for Clinical Assessment in Neuropsychiatry (SCAN). ResultsThe GTE score showed significant differences in terms of frequency scores of backgrounds rhythmic activity, diffuse slow activity, and focal abnormality. The QEEG analysis showed a pattern of abnormality with respect to the Cuban EEG normative values, displaying an excess of alpha and beta activities in the fronto-central-parietal areas in both groups. The anomalies, of COVID-19 patients and close contacts, differs in the right fronto-centro parietal area. The COVID 19 group differed-s from the close control group in theta band of the right parieto-central. The symptomatic group of COVID-19 patients differs from asymptomatic patients in delta and theta activities of the parieto-central region. The sources of activation using VARETA showed a difference in cortical activation patterns at alpha and beta frequencies in the groups studied with respect to the normative EEG database. In beta frequency were localized in right middle temporal gyrus in both groups and right angular gyrus in Covid 19 group only. In alpha band, the regions were the left supramarginal gyrus for Covid 19 group and the left superior temporal gyrus for Control group. Greater activation was found in the right middle temporal gyrus at alpha frequency in COVID-19 patients than in their close contacts. ConclusionsBrain functions are impaired in long COVID-19 patients. QEEG and VARETA permit us to comprehend the susceptibility of particular brain regions exposed to viral illness. HighlightsO_LIBackground frequency abnormalities diffuse slow activity and focal abnormality associated with a pattern of excess oftheta, alpha and beta energies in in the right fronto-centro-parietal regions in QEEG analysis characterizedCOVID-19 patients. C_LIO_LIPatients with COVID-19 show more alpha and beta EEG activities related to normative EEG database. C_LIO_LIPatients with COVID-19 and close contacts show high cortical activation in temporo-parietal areas in alpha and beta bands compared to normative EEG database. C_LIO_LIPatients with COVID-19 (positive PCR) have high activation in the right middle frontal gyrus for alpha band related to close contacts. C_LI