Article

Standardized low resolution brain electromagnetic tomography (SLORETA): Technical details

February 2002
Methods and Findings in Experimental and Clinical Pharmacology 24 Suppl D(Suppl D):5-12

February 2002
24 Suppl D(Suppl D):5-12

Source
PubMed

Authors:

Scalp electric potentials (electroencephalograms) and extracranial magnetic fields (magnetoencephalograms) are due to the primary (impressed) current density distribution that arises from neuronal postsynaptic processes. A solution to the inverse problem--the computation of images of electric neuronal activity based on extracranial measurements--would provide important information on the time-course and localization of brain function. In general, there is no unique solution to this problem. In particular, an instantaneous, distributed, discrete, linear solution capable of exact localization of point sources is of great interest, since the principles of linearity and superposition would guarantee its trustworthiness as a functional imaging method, given that brain activity occurs in the form of a finite number of distributed hot spots. Despite all previous efforts, linear solutions, at best, produced images with systematic nonzero localization errors. A solution reported here yields images of standardized current density with zero localization error. The purpose of this paper is to present the technical details of the method, allowing researchers to test, check, reproduce and validate the new method.

EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints

Article

May 2024
MED BIOL ENG COMPUT

One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on \({L}_{1}\) and \({L}_{2}\) norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.

The Influence of Semantic Stimuli on Design Creativity: An EEG Study from the Perspective of Design Neurocognition

Conference Paper

Jul 2024

This study employed electroencephalogram (EEG) to collect brain activity while participants read different kinds of semantic stimuli. The standardized low-resolution brain electromagnetic tomography method was used to analyze the current source in both the lower alpha frequency band (8-10 Hz) and the upper alpha frequency band (10-13 Hz). This study focused on understanding how semantic stimuli with different degrees of abstraction influence design creativity and on exploring the activities of the brain when processing them. The results showed abstract stimuli evoked a larger current source in the upper alpha frequency band compared to concrete stimuli on 269 voxels distributed in 13 Brodmann areas and among three lobes (frontal, temporal, and occipital). Additionally, there was a positive correlation between the upper alpha current source and both novelty scores and surprise scores. These results substantiate the hypothesis that activities in the upper alpha band are conducive to enhancing design creativity. 2 M. Wang et al.

Effects of transcranial direct current stimulation on brain activity and cortical functional connectivity in children with autism spectrum disorders

Article

Full-text available

May 2024

Introduction Transcranial direct current stimulation (tDCS) has emerged as a therapeutic option to mitigate symptoms in individuals with autism spectrum disorder (ASD). Our study investigated the effects of a two-week regimen of tDCS targeting the left dorsolateral prefrontal cortex (DLPFC) in children with ASD, examining changes in rhythmic brain activity and alterations in functional connectivity within key neural networks: the default mode network (DMN), sensorimotor network (SMN), and dorsal attention network (DAN). Methods We enrolled twenty-six children with ASD and assigned them randomly to either an active stimulation group (n=13) or a sham stimulation group (n=13). The active group received tDCS at an intensity of 1mA to the left DLPFC for a combined duration of 10 days. Differences in electrical brain activity were pinpointed using standardized low-resolution brain electromagnetic tomography (sLORETA), while functional connectivity was assessed via lagged phase synchronization. Results Compared to the typically developing children, children with ASD exhibited lower current source density across all frequency bands. Post-treatment, the active stimulation group demonstrated a significant increase in both current source density and resting state network connectivity. Such changes were not observed in the sham stimulation group. Conclusion tDCS targeting the DLPFC may bolster brain functional connectivity in patients with ASD, offering a substantive groundwork for potential clinical applications.

Tracking EEG Network Dynamics through Transitions between Eyes-Closed, Eyes-Open, and Task States

Preprint

Full-text available

Apr 2024

Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.

Shared EEG correlates between non-REM parasomnia experiences and dreams

Article

Full-text available

May 2024

Sleepwalking and related parasomnias result from incomplete awakenings out of non-rapid eye movement sleep. Behavioral episodes can occur without consciousness or recollection, or in relation to dream-like experiences. To understand what accounts for these differences in consciousness and recall, here we recorded parasomnia episodes with high-density electroencephalography (EEG) and interviewed participants immediately afterward about their experiences. Compared to reports of no experience (19%), reports of conscious experience (56%) were preceded by high-amplitude EEG slow waves in anterior cortical regions and activation of posterior cortical regions, similar to previously described EEG correlates of dreaming. Recall of the content of the experience (56%), compared to no recall (25%), was associated with higher EEG activation in the right medial temporal region before movement onset. Our work suggests that the EEG correlates of parasomnia experiences are similar to those reported for dreams and may thus reflect core physiological processes involved in sleep consciousness.

EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels

Article

Full-text available

May 2024

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10–10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.

HD-tDCS Over Left Supplementary Motor Area Differentially Modulated Neural Correlates of Motor Planning for Speech vs. Limb Movement

Article

May 2024
INT J PSYCHOPHYSIOL

The supplementary motor area (SMA) is implicated in planning, execution, and control of speech production and limb movement. The SMA is among putative generators of pre-movement EEG activity which is thought to be neural markers of motor planning. In neurological conditions such as Parkinson's disease, abnormal pre-movement neural activity within the SMA has been reported during speech production and limb movement. Therefore, this region can be a potential target for non-invasive brain stimulation for both speech and limb movement. The present study took an initial step in examining the application of high-definition transcranial direct current stimulation (HD-tDCS) over the left SMA in 24 neurologically intact adults. Subsequently, event-related potentials (ERPs) were recorded while participants performed speech and limb movement tasks. Participants' data were collected in three counterbalanced sessions: anodal, cathodal and sham HD-tDCS. Relative to sham stimulation, anodal, but not cathodal, HD-tDCS significantly attenuated ERPs prior to the onset of the speech production. In contrast, neither anodal nor cathodal HD-tDCS significantly modulated ERPs prior to the onset of limb movement compared to sham stimulation. These findings showed that neural correlates of motor planning can be modulated using HD-tDCS over the left SMA in neurotypical adults, with translational implications for neurological conditions that impair speech production. The absence of a stimulation effect on ERPs prior to the onset of limb movement was not expected in this study, and future studies are warranted to further explore this effect.

The Effects of Chiropractic Spinal Adjustment on EEG in Adults with Alzheimer’s and Parkinson’s Disease: A Pilot Randomised Cross-over Trial

Article

Full-text available

Apr 2024
J INTEGR NEUROSCI

Objectives: In this study, we explored the effects of chiropractic spinal adjustments on resting-state electroencephalography (EEG) recordings and early somatosensory evoked potentials (SEPs) in Alzheimer's and Parkinson's disease. Methods: In this randomized cross-over study, 14 adults with Alzheimer's disease (average age 67 ± 6 years, 2 females:12 males) and 14 adults with Parkinson's disease (average age 62 ± 11 years, 1 female:13 males) participated. The participants underwent chiropractic spinal adjustments and a control (sham) intervention in a randomized order, with a minimum of one week between each intervention. EEG was recorded before and after each intervention, both during rest and stimulation of the right median nerve. The power-spectra was calculated for resting-state EEG, and the amplitude of the N30 peak was assessed for the SEPs. The source localization was performed on the power-spectra of resting-state EEG and the N30 SEP peak. Results: Chiropractic spinal adjustment significantly reduced the N30 peak in individuals with Alzheimer's by 15% (p = 0.027). While other outcomes did not reach significance, resting-state EEG showed an increase in absolute power in all frequency bands after chiropractic spinal adjustments in individuals with Alzheimer's and Parkinson's disease. The findings revealed a notable enhancement in connectivity within the Default Mode Network (DMN) at the alpha, beta, and theta frequency bands among individuals undergoing chiropractic adjustments. Conclusions: We found that it is feasible to record EEG/SEP in individuals with Alzheimer's and Parkinson's disease. Additionally, a single session of chiropractic spinal adjustment reduced the somatosensory evoked N30 potential and enhancement in connectivity within the DMN at the alpha, beta, and theta frequency bands in individuals with Alzheimer's disease. Future studies may require a larger sample size to estimate the effects of chiropractic spinal adjustment on brain activity. Given the preliminary nature of our findings, caution is warranted when considering the clinical implications. Clinical trial registration: The study was registered by the Australian New Zealand Clinical Trials Registry (registration number ACTRN12618001217291 and 12618001218280).

Comparing the ‘When’ and the ‘Where’ of Electrocortical Activity in Patients with Tourette Syndrome, Body-Focused Repetitive Behaviors, and Obsessive Compulsive Disorder

Article

Full-text available

Apr 2024

Background/Objectives: Tourette Syndrome (TS), Obsessive Compulsive Disorder (OCD), and Body-Focused Repetitive Behaviors (BFRB) are three disorders that share many similarities in terms of phenomenology, neuroanatomy, and functionality. However, despite the literature pointing toward a plausible spectrum of these disorders, only a few studies have compared them. Studying the neurocognitive processes using Event-Related Potentials (ERPs) offers the advantage of assessing brain activity with excellent temporal resolution. The ERP components can then reflect specific processes known to be potentially affected by these disorders. Our first goal is to characterize ‘when’ in the processing stream group differences are the most prominent. The second goal is to identify ‘where’ in the brain the group discrepancies could be. Methods: Participants with TS (n = 24), OCD (n = 18), and BFRB (n = 16) were matched to a control group (n = 59) and were recorded with 58 EEG electrodes during a visual counting oddball task. Three ERP components were extracted (i.e., P200, N200, and P300), and generating sources were modelized with Standardized Low-Resolution Electromagnetic Tomography. Results: We showed no group differences for the P200 and N200 when controlling for anxiety and depressive symptoms, suggesting that the early cognitive processes reflected by these components are relatively intact in these populations. Our results also showed a decrease in the later anterior P300 oddball effect for the TS and OCD groups, whereas an intact oddball effect was observed for the BFRB group. Source localization analyses with sLORETA revealed activations in the lingual and middle occipital gyrus for the OCD group, distinguishing it from the other two clinical groups and the controls. Conclusions: It seems that both TS and OCD groups share deficits in anterior P300 activation but reflect distinct brain-generating source activations.

Intracortical brain-heart interplay: An EEG model source study of sympathovagal changes

Article

Full-text available

Apr 2024
HUM BRAIN MAPP

The interplay between cerebral and cardiovascular activity, known as the functional brain‐heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain‐heart axis have been proposed to estimate BHI non‐invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies. This study proposes an analytical modeling framework for estimating the BHI at the source‐brain level. This analysis relies on the low‐resolution electromagnetic tomography sources localization from scalp electrophysiological recordings. BHI is then quantified as the functional correlation between the intracortical sources and cardiovascular dynamics. Using this approach, we aimed to evaluate the reliability of BHI estimates derived from source‐localized EEG signals as compared with prior findings from neuroimaging methods. The proposed approach is validated using an experimental dataset gathered from 32 healthy individuals who underwent standard sympathovagal elicitation using a cold pressor test. Additional resting state data from 34 healthy individuals has been analysed to assess robustness and reproducibility of the methodology. Experimental results not only confirmed previous findings on activation of brain structures affecting cardiac dynamics (e.g., insula, amygdala, hippocampus, and anterior and mid‐cingulate cortices) but also provided insights into the anatomical bases of brain‐heart axis. In particular, we show that the bidirectional activity of electrophysiological pathways of functional brain‐heart communication increases during cold pressure with respect to resting state, mainly targeting neural oscillations in the , , and bands. The proposed approach offers new perspectives for the investigation of functional BHI that could also shed light on various pathophysiological conditions.

Neural signatures of imaginary motivational states: desire for music, movement and social play

Article

Full-text available

Apr 2024
BRAIN TOPOGR

The literature has demonstrated the potential for detecting accurate electrical signals that correspond to the will or intention to move, as well as decoding the thoughts of individuals who imagine houses, faces or objects. This investigation examines the presence of precise neural markers of imagined motivational states through the combining of electrophysiological and neuroimaging methods. 20 participants were instructed to vividly imagine the desire to move, listen to music or engage in social activities. Their EEG was recorded from 128 scalp sites and analysed using individual standardized Low-Resolution Brain Electromagnetic Tomographies (LORETAs) in the N400 time window (400–600 ms). The activation of 1056 voxels was examined in relation to the 3 motivational states. The most active dipoles were grouped in eight regions of interest (ROI), including Occipital, Temporal, Fusiform, Premotor, Frontal, OBF/IF, Parietal, and Limbic areas. The statistical analysis revealed that all motivational imaginary states engaged the right hemisphere more than the left hemisphere. Distinct markers were identified for the three motivational states. Specifically, the right temporal area was more relevant for “Social Play”, the orbitofrontal/inferior frontal cortex for listening to music, and the left premotor cortex for the “Movement” desire. This outcome is encouraging in terms of the potential use of neural indicators in the realm of brain-computer interface, for interpreting the thoughts and desires of individuals with locked-in syndrome.

Transcutaneous Auricular Vagus Nerve Stimulation for Visually Induced Motion Sickness: An eLORETA Study

Preprint

Full-text available

Jun 2024

Transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive form of electrical brain stimulation, has shown potent therapeutic potential for a wide spectrum of conditions. How taVNS influences the characterization of motion sickness - a long mysterious syndrome with a polysymptomatic onset - remains unclear. Here, to examine taVNS-induced effects on brain function in response to motion-induced nausea, 64-channel electroencephalography (EEG) was analyzed from 42 healthy participants collected during nauseogenic visual stimulation concurrent with taVNS administration, in a crossover randomized sham-controlled study. Cortical neuronal generators were estimated from the obtained EEG using exact low-resolution brain electromagnetic tomography (eLORETA). While both sham and taVNS increased insula activation during electrical stimulation, compared to baseline, taVNS additionally augmented middle frontal gyrus neuronal activity. Following taVNS, brain regions including the supramarginal, parahippocampal, and precentral gyri were activated. Contrasting sham, taVNS markedly increased activity in the middle occipital gyrus during stimulation. A repeated-measures ANOVA showed that taVNS decreases motion sickness symptoms. This reduction in symptoms correlated with taVNS-induced neural activation. Our findings provide new insights into taVNS-induced brain changes, during and after nauseogenic stimuli exposure, including accompanying behavioral response. Together, these findings suggest that taVNS has promise as an effective neurostimulation tool for motion sickness management.

ESI-GAL: EEG Source Imaging-based Kinematics Parameter Estimation for Grasp and Lift Task

Preprint

Full-text available

Jun 2024

Objective: Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP) has been an active area of research to develop brain-computer interface (BCI) systems such as exosuits, prostheses, and rehabilitation devices. However, EEG source imaging (ESI) based kinematics prediction is sparsely explored in the literature. Approach: In this study, pre-movement EEG features are utilized to predict three-dimensional (3D) hand kinematics for the grasp-and-lift motor task. A public dataset, WAY-EEG-GAL, is utilized for MKP analysis. In particular, sensor-domain (EEG data) and source-domain (ESI data) based features from the frontoparietal region are explored for MKP. Deep learning-based models are explored to achieve efficient kinematics decoding. Various time-lagged and window sizes are analyzed for hand kinematics prediction. Subsequently, intra-subject and inter-subject MKP analysis is performed to investigate the subject-specific and subject-independent motor-learning capabilities of the neural decoders. The Pearson correlation coefficient (PCC) is used as the performance metric for kinematics trajectory decoding. Main results: The rEEGNet neural decoder achieved the best performance with sensor-domain and source-domain features with the time lag and window size of 100 ms and 450 ms, respectively. The highest mean PCC values of 0.790, 0.795, and 0.637 are achieved using sensor-domain features, while 0.769, 0.777, and 0.647 are achieved using source-domain features in x, y, and z-directions, respectively. Significance: This study explores the feasibility of trajectory prediction using EEG sensor-domain and source-domain EEG features for the grasp-and-lift task. Furthermore, inter-subject trajectory estimation is performed using the proposed deep learning decoder with EEG source domain features.

Construction and Validation of a New BrainView qEEG Discriminant Database

Article

Jun 2024

Menstrual cycle phase modulates causal connectivity in the resting-state brain of healthy females

Preprint

Full-text available

Jun 2024

Background Ovarian hormones exert direct and indirect influences on the brain; however, little is known about how these hormones impact causal brain connectivity. Studying the female brain at a single time point may be confounded by distinct hormone phases. Despite this, the menstrual cycle is often overlooked. The primary objective of this pilot study was to evaluate resting-state causal connectivity during the early follicular and mid-luteal menstrual phases corresponding to low vs high estradiol and progesterone, respectively. Methods Fourteen healthy control females ( M = 20.36 years, SD = 2.02) participated in this study. Participants were scheduled for two resting-state electroencephalography (EEG) scans during their monthly menstrual cycle. A saliva sample was also collected at each EEG session for hormone analyses. Causal connectivity was quantified using information flow rate of EEG source data. Demographic information, emotional empathy, and sleep quality were obtained from self-report questionnaires. Results Progesterone levels were significantly higher in the mid-luteal phase compared to the early follicular phase ( p = .041). We observed distinct patterns of causal connectivity along the menstrual cycle. Connectivity in the early follicular phase was centralized and shifted posteriorly during the mid-luteal phase. During the early follicular phase, the primary regions driving activity were the right central and left/right parietal regions, with the left central region being the predominant receiver of activity. During the mid-luteal phase, connections were primarily transmitted from the right side and the main receiver region was the left occipital region. Network topology during the mid-luteal phase was found to be significantly more assortative compared to the early follicular phase. Conclusions The observed difference in causal connectivity demonstrates how network dynamics reorganize as a function of menstrual phase and level of progesterone. In the mid-luteal phase, there was a strong shift for information flow to be directed at visual spatial processing and visual attention areas, whereas in the follicular phase, there was strong information flow primarily within the sensory-motor regions. The mid-luteal phase was significantly more assortative, suggesting greater network efficiency and resilience. These results contribute to the emerging literature on brain-hormone interactions.

Association between gait speed deterioration and EEG abnormalities

Article

Full-text available

Jun 2024
PLOS ONE

Physical and cognitive decline at an older age is preceded by changes that accumulate over time until they become clinically evident difficulties. These changes, frequently overlooked by patients and health professionals, may respond better than fully established conditions to strategies designed to prevent disabilities and dependence in later life. The objective of this study was twofold; to provide further support for the need to screen for early functional changes in older adults and to look for an early association between decline in mobility and cognition. A cross-sectional cohort study was conducted on 95 active functionally independent community-dwelling older adults in Havana, Cuba. We measured their gait speed at the usual pace and the cognitive status using the MMSE. A value of 0.8 m/s was used as the cut-off point to decide whether they presented a decline in gait speed. A quantitative analysis of their EEG at rest was also performed to look for an associated subclinical decline in brain function. Results show that 70% of the sample had a gait speed deterioration (i.e., lower than 0.8 m/s), of which 80% also had an abnormal EEG frequency composition for their age. While there was no statistically significant difference in the MMSE score between participants with a gait speed above and below the selected cut-off, individuals with MMSE scores below 25 also had a gait speed<0.8 m/s and an abnormal EEG frequency composition. Our results provide further evidence of early decline in older adults–even if still independent and active—and point to the need for clinical pathways that incorporate screening and early intervention targeted at early deterioration to prolong the years of functional life in older age.

Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals

Article

Full-text available

May 2024
IEEE T NEUR SYS REH

Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.

Brain health in diverse settings: How age, demographics and cognition shape brain function

Article

Full-text available

May 2024
NEUROIMAGE

Visual network modularity and communication alterations in ADHD subtypes: evidence from source localized EEG and graph theoretical analysis

Preprint

Full-text available

May 2024

The neurobiological basis of ADHD and its subtypes remains unclear, with inconsistent findings from studies using electrophysiology and neuroimaging. Some studies suggest ADHD-I is a distinct disorder, but there is also evidence of similar neural basis in ADHD-I and ADHD-C subtypes. This study investigates the neural basis of ADHD and its subtypes using a subnetwork modularity approach based on graph theoretical analysis of EEG data from 35 children aged 7-11. EEG was recorded in the eyes open condition and preprocessed. After preprocessing, data was analyzed using LORETA algorithm to estimate current densities in 84 regions of interest (ROIs) in the cortex and calculate functional connectivity between these ROIs in different EEG frequency bands. Then, we evaluated modularity of five functional brain networks (default mode, central control, salience, visual, and sensorimotor) using Newman modularity algorithm. Further, we evaluated edge betweenness centrality to assess communications between these functional brain networks. The study found that different brain networks have modularity in certain frequency bands, and ADHD groups showed reduced modularity of the visual network compared to normal groups in the alpha1 band (8-10 Hz). The communication between the visual network and other brain networks, except the salience network, was also reduced in ADHD groups (in the alpha1 band). However, there were no significant differences in the modularity of brain networks and communication among them between two ADHD subtypes. The results suggest a novel mechanism for ADHD involving lower intrinsic modularity in the visual network, disturbed communication between the visual network and other networks, and potential impact on the function of control and sensorimotor networks. Further, our results suggest that there may be a common neural basis for both subtypes, involving a shared disturbance in the modularity and connectivity of the ventral network. This supports the idea that ADHD-I and ADHD-C are subtypes within the same category and contradicts previous studies that suggest they are separate disorders.

Long-lasting negativity in the left motoric brain structures during word memory inhibition in the Think/No-Think paradigm

Article

Full-text available

May 2024

In this study, we investigated the electrical brain responses in a high-density EEG array (64 electrodes) elicited specifically by the word memory cue in the Think/No-Think paradigm in 46 participants. In a first step, we corroborated previous findings demonstrating sustained and reduced brain electrical frontal and parietal late potentials elicited by memory cues following the No-Think (NT) instructions as compared to the Think (T) instructions. The topographical analysis revealed that such reduction was significant 1000 ms after memory cue onset and that it was long-lasting for 1000 ms. In a second step, we estimated the underlying brain generators with a distributed method (swLORETA) which does not preconceive any localization in the gray matter. This method revealed that the cognitive process related to the inhibition of memory retrieval involved classical motoric cerebral structures with the left primary motor cortex (M1, BA4), thalamus, and premotor cortex (BA6). Also, the right frontal-polar cortex was involved in the T condition which we interpreted as an indication of its role in the maintaining of a cognitive set during remembering, by the selection of one cognitive mode of processing, Think, over the other, No-Think, across extended periods of time, as it might be necessary for the successful execution of the Think/No-Think task.

PDE-constrained Optimization for Electroencephalographic Source Reconstruction

Article

Full-text available

May 2024

This paper introduces a novel numerical method for the inverse problem of electroencephalography (EEG). We pose the inverse EEG problem as an optimal control (OC) problem for Poisson's equation. The optimality conditions lead to a variational system of differential equations. It is discretized directly in finite-element spaces leading to a system of linear equations with a sparse Karush-Kuhn-Tucker matrix. The method uses finite-element discretization and thus can handle MRI-based meshes of almost arbitrary complexity. It extends the well-known mixed quasi-reversibility method (mQRM) in that pointwise noisy data explicitly appear in the formulation making unnecessary tedious interpolation of the noisy data from the electrodes to the scalp surface. The resulting algebraic problem differs considerably from that obtained in the mixed quasi-reversibility, but only slightly larger. The algorithm does not require the formation of the lead-field matrix, which can be beneficial for large matrices. Our tests, both with spherical and MRI-based meshes, demonstrates that the method accurately reconstructs cortical activity. Mathematical Subject Classification: 86-08, 86A15, 74L05

Shades of gravity – effects of planetary gravity levels on electrocortical activity and neurocognitive performance

Article

Full-text available

May 2024
BRAIN STRUCT FUNCT

The plans of international space agencies to return to the Moon and explore deep space, including Mars, highlight the challenges of human adaptation and stress the need for a thorough analysis of the factors that facilitate, limit and modify human performance under extreme environments. This study investigates the influence of partial gravity on behavioural (error rate and reaction time) and neuronal parameters (event-related potentials) through parabolic flights. Brain cortical activity was assessed using EEG from 18 participants who solved a neurocognitive task, consisting of a mental arithmetic task and an auditory oddball paradigm, during Earth (1G), Lunar (0.16G + 0.25G) and Martian gravity (0.38G + 0.5G) for 15 consecutive parabolas. Data shows higher electrocortical activity in Earth gravity compared to Lunar and Martian gravity in the parietal lobe. No differences in participants’ performance were found among the gravity levels. Event-related potentials displayed gravity-dependent variations, though limited stimuli recording suggests caution in interpretation. Data suggests a threshold between Earth and Martian gravity within the different gravities responsible for physiological changes, but it seems to vary greatly between individuals. The altered neuronal communication could be explained with a model developed by Kohn and Ritzmann in 2018. The increasing intracranial pressure in weightlessness changes the properties of the cell membrane of neurons and leads to a depolarisation of the resting membrane potential. The findings underscore the individuality of physiological changes in response to gravity alterations, signalling the need for further investigations in future studies.

Article

Apr 2024
J INTEGR NEUROSCI

Background: In our modern world we are exposed to a steady stream of information containing important as well as irrelevant information. Therefore, our brains have to constantly select relevant over distracting items and further process the selected information. Whereas there is good evidence that even in rapid serial streams of presented information relevant targets can be actively selected, it is less clear whether and how distracting information is de-selected and suppressed in such scenarios. Methods: To address this issue we recorded electroencephalographic activity during a rapid serial visual presentation paradigm in which healthy, young human volunteers had to encode visual targets into short-term memory while salient visual distractors and neutral filler items needed to be ignored. Event-related potentials were analyzed in 3D source space and compared between stimulus types. Results: A negative wave between around 170 and 230 ms after stimulus onset resembling the N2pc component was identified that dissociated between target stimuli and distractors as well as filler items. This wave appears to reflect target selection processes. However, there was no electrophysiological signature identified that would indicate an active distractor suppression mechanism. Conclusions: The obtained results suggest that unlike in situations where target stimuli and distractors are presented simultaneously, targets can be selected without the need for active suppression of distracting information in serial presentations with a clear and regular temporal structure. It is assumed that temporal expectation supports efficient target selection by the brain.

Abnormal Resting-State Brain Networks and their Relationship with Cognitive Reappraisal Preferences in College Students with Depressive Tendencies

Preprint

May 2024

This study investigates resting-state brain network characteristics in college students with depressive tendencies (DT) and their link to cognitive reappraisal strategies. A group of 38 DT students and 41 healthy controls (HCs) were assessed using questionnaires on cognitive reappraisal strategies, followed by alpha and beta frequency band EEG feature extraction. Through complex network analysis, significant reductions in cognitive reappraisal preferences were noted among DT participants compared to HCs, alongside abnormalities in brain network centrality, particularly in the frontal and limbic lobes across different frequency bands. A notable correlation was found between the preference for cognitive reappraisal in DT participants and significant changes in graph indices. The findings highlight substantial alterations in the resting-state brain networks of DT individuals, closely associated with cognitive reappraisal strategy preferences. These alterations may affect emotion regulation strategy choices, offering insights into the neural mechanisms of emotional regulation difficulties in DT.

Engineering Applications of Artificial Intelligence Enabling temporal-spectral decoding in multi-class single-side upper limb classification

Article

Full-text available

Apr 2024
ENG APPL ARTIF INTEL

This manuscript presents a novel approach for decoding pre-movement patterns from brain signals using a two-stage-training temporal-spectral neural network (TTSNet). The TTSNet employs a combination of filter bank task-related component analysis (FBTRCA) and convolutional neural network (CNN) techniques to enhance the classification of single-upper limb movements in non-invasive brain-computer interfaces (BCIs). In our previous work, we introduced the FBTRCA method which utilized filter banks and spatial filters to handle spectral and spatial information, respectively. However, we observed limitations in the temporal decoding phase, where correlation features failed to effectively utilize temporal information because of misaligned onset and noisy spikes. To address this issue, our proposed method focuses on analyzing multi-channel signals in the temporal-spectral domain. The TTSNet first divides the signals into various filter banks, employing task-related component analysis to reduce dimensionality and eliminate noise, respectively. Subsequently, a CNN is employed to optimize the temporal characteristics of the signals and extract class-related features. Finally, the class-related features from all filter banks are concatenated and classified using the fully connected layer. To evaluate the effectiveness of our proposed method, we conducted experiments on two publicly available datasets. In binary classification tasks, the TTSNet achieved an improved accuracy of 0.7707 ± 0.1168, surpassing the performance of EEGNet (accuracy: 0.7340 ± 0.1246) and FBTRCA (accuracy: 0.7487 ± 0.1250). In multi-class tasks, TTSNet achieved an accuracy of 0.4588 ± 0.0724, exhibiting a 4.27% and 3.95% accuracy increase over EEGNet and FBTRCA, respectively. The findings of this study suggest that the proposed TTSNet method holds promise for detecting limb movements and assisting in the rehabilitation of stroke patients. The classification of single-side limb movements is expected to facilitate the interaction between patients and external environment by increasing the number of control commands in BCIs.

Nonlinear dynamics of postural control system under visual-vestibular habituation balance practice: evidence from EEG, EMG and center of pressure signals

Article

Full-text available

Apr 2024
FRONT HUM NEUROSCI

Human postural control system is inherently complex with nonlinear interaction among multiple subsystems. Accordingly, such postural control system has the flexibility in adaptation to complex environments. Previous studies applied complexity-based methods to analyze center of pressure (COP) to explore nonlinear dynamics of postural sway under changing environments, but direct evidence from central nervous system or muscular system is limited in the existing literature. Therefore, we assessed the fractal dimension of COP, surface electromyographic (sEMG) and electroencephalogram (EEG) signals under visual-vestibular habituation balance practice. We combined a rotating platform and a virtual reality headset to present visual-vestibular congruent or incongruent conditions. We asked participants to undergo repeated exposure to either congruent (n = 14) or incongruent condition (n = 13) five times while maintaining balance. We found repeated practice under both congruent and incongruent conditions increased the complexity of high-frequency (0.5–20 Hz) component of COP data and the complexity of sEMG data from tibialis anterior muscle. In contrast, repeated practice under conflicts decreased the complexity of low-frequency (<0.5 Hz) component of COP data and the complexity of EEG data of parietal and occipital lobes, while repeated practice under congruent environment decreased the complexity of EEG data of parietal and temporal lobes. These results suggested nonlinear dynamics of cortical activity differed after balance practice under congruent and incongruent environments. Also, we found a positive correlation (1) between the complexity of high-frequency component of COP and the complexity of sEMG signals from calf muscles, and (2) between the complexity of low-frequency component of COP and the complexity of EEG signals. These results suggested the low- or high-component of COP might be related to central or muscular adjustment of postural control, respectively.

Chapter 7 Unlocking Innovation: Exploring Gendered Differences in Neural Activity During Creative Tasks

Chapter

Full-text available

Apr 2024

Creativity and innovation are integral to progress, yet neurocognitive mechanisms underlying creative thought remain unclear. This EEG study examined gender differences in brain activation during verbal and figural creative thinking tasks in 20 adults (10M, 10F). Participants completed verbal (Ask-and-Guess, Just Suppose) and figural (Unusual Uses) divergent thinking tasks from the Abbreviated Torrance Test while EEG was recorded. Analyses examined default mode, executive control, and salience network activity across frequency bands. Results revealed subtle gender differences in network activations and interactions during creative ideation. Females exhibited greater frontoparietal coupling, indicating enhanced cognitive flexibility. Males displayed higher default mode synchronization, suggesting greater persistence within an imaginative space. Findings provide initial neural evidence for specialized neurocognitive profiles between genders, with female advantages in flexible divergent thinking and male strengths in focused conceptual exploration.

Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach

Article

Full-text available

Jun 2024
COMPUT METH PROG BIO

Background and Objective: Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoen-cephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. Methods: To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. Results: Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value < 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. Conclusion: This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.

Comparative electroencephalography analysis: Marathon runners during tapering versus sedentary controls reveals no significant differences

Article

Full-text available

Apr 2024

Introduction Previous studies described various adaptive neuroplastic brain changes associated with physical activity (PA). EEG studies focused mostly on effects during or shortly after short bouts of exercise. This is the first study to investigate the capability of EEG to display PA‐induced long‐lasting plasticity in runners compared to a sedentary control group. Methods Thirty trained runners and 30 age‐ and sex‐matched sedentary controls (SC) were included as a subpopulation of the ReCaP (Running effects on Cognition and Plasticity) study. PA was measured with the International Physical Activity Questionnaire (IPAQ). Resting‐state EEG of the runners was recorded in the tapering phase of the training for the Munich marathon 2017. Power spectrum analyses were conducted using standardized low‐resolution electromagnetic tomography (sLORETA) and included the following frequency bands: delta: 1.5–6 Hz, theta: 6.5–8.0 Hz, alpha1: 8.5–10 Hz, alpha2: 10.5–12.0 Hz, beta1: 12.5–18.0 Hz, beta2: 18.5–21.0 Hz, beta3: 21.5–30.0 Hz, and total power (1.5–30 Hz). Results PA (IPAQ) and BMI differed significantly between the groups. The other included demographic parameters were comparable. Statistical nonparametric mapping showed no significant power differences in EEG between the groups. Discussion Heterogeneity in study protocols, especially in time intervals between exercise cessation and EEG recordings and juxtaposition of acute exercise‐induced effects on EEG in previous studies, could be possible reasons for the differences in results. Future studies should record EEG at different time points after exercise cessation and in a broader spectrum of exercise intensities and forms to further explore the capability of EEG in displaying long‐term exercise‐induced plasticity.

Total neuroelectric brain activity derived from resting-state MEG is invariable across the adult lifespan

Preprint

Jun 2024

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 person's life. The electric power is redistributed during the lifetime: delta rhythm is diminishing, giving slow rise to all other rhythms.

Investigating multilevel cognitive processing within error-free and error-prone feedback conditions in executed and observed car driving

Article

Full-text available

Jun 2024
FRONT HUM NEUROSCI

Hannah Pulferer

Accident analyses repeatedly reported the considerable contribution of runoff road incidents to fatalities in road traffic, and despite considerable advances in assistive technologies to mitigate devastating consequences, little insight into the drivers' brain response during such accident scenarios has been gained. While various literature documents neural correlates to steering motion, the driver's mental state, and the impact of distraction and fatigue on driving performance, the cortical substrate of continuous deviations of a car from the road-i.e., how the brain represents a varying discrepancy between the intended and observed car position and subsequently assigns customized levels of corrective measures-remains unclear. Furthermore, the superposition of multiple subprocesses, such as visual and erroneous feedback processing, performance monitoring, or motor control, complicates a clear interpretation of engaged brain regions within car driving tasks. In the present study, we thus attempted to disentangle these subprocesses, employing passive and active steering conditions within both error-free and error-prone vehicle operation conditions. We recorded EEG signals of 26 participants in 13 sessions, simultaneously measuring pairs of Executors (actively steering) and Observers (strictly observing) during a car driving task. We observed common brain patterns in the Executors regardless of error-free or error-prone vehicle operation, albeit with a shift in spectral activity from motor beta to occipital alpha oscillations within erroneous conditions. Further, significant frontocentral differences between Observers and Executors, tracing back to the caudal anterior cingulate cortex, arose during active steering conditions, indicating increased levels of motor-behavioral cognitive control. Finally, we present regression results of both the steering signal and the car position, indicating that a regression of continuous deviations from the road utilizing the EEG might be feasible.

EEG decoding with spatiotemporal convolutional neural network for visualization and closed‐loop control of sensorimotor activities: A simultaneous EEG‐fMRI study

Article

Full-text available

Jun 2024
HUM BRAIN MAPP

Closed‐loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high‐density whole‐head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain‐computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI‐based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.

Source localization comparison and combination of OPM-MEG and fMRI to detect sensorimotor cortex responses

Article

Jun 2024
COMPUT METH PROG BIO

Age differences in the principal temporo-spatial components of EEG activity during a proactive interference task

Article

Jun 2024
BIOL PSYCHOL

Efficacy of audiovisual neurofeedback training for attention enhancement: a multimodal approach

Article

Jun 2024

Attention is a cognitive process that involves focusing mental resources on specific stimuli and plays a fundamental role in perception, learning, memory, and decision-making. Neurofeedback (NF) is a useful technique for improving attention, providing real-time feedback on brain activity in the form of visual or auditory cues, and allowing users to learn to self-regulate their cognitive processes. This study compares the effectiveness of different cues in NF training for attention enhancement through a multimodal approach. We conducted neurological (Quantitative Electroencephalography), neuropsychological (Mindfulness Attention Awareness Scale-15), and behavioral (Stroop test) assessments before and after NF training on 36 healthy participants, divided into audiovisual (G1) and visual (G2) groups. Twelve NF training sessions were conducted on alternate days, each consisting of five subsessions, with pre- and post-NF baseline electroencephalographic evaluations using power spectral density. The pre-NF baseline was used for thresholding the NF session using the beta frequency band power. Two-way analysis of variance revealed a significant long-term effect of group (G1/G2) and state (before/after NF) on the behavioral and neuropsychological assessments, with G1 showing significantly higher Mindfulness Attention Awareness Scale-15 scores, higher Stroop scores, and lower Stroop reaction times for interaction effects. Moreover, unpaired t -tests to compare voxel-wise standardized low-resolution brain electromagnetic tomography images revealed higher activity of G1 in Brodmann area 40 due to NF training. Neurological assessments show that G1 had better improvement in immediate, short-, and long-term attention. The findings of this study offer a guide for the development of NF training protocols aimed at enhancing attention effectively.

Cortical modulations before lower limb motor blocks are associated with freezing of gait in Parkinson’s disease: an EEG source localization study

Article

Jun 2024
NEUROBIOL DIS

Decomposing Loss Aversion from a Single Neural Signal

Article

Jun 2024

The effects of spatial leakage correction on the reliability of EEG‐based functional connectivity networks

Article

Full-text available

Jun 2024
HUM BRAIN MAPP

Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero‐lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject‐level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting‐state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting‐state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model‐based, as well as individual head model‐based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.

Post-processing of a distributed source method for the localization of somatosensory cortex in a cohort of epilepsy patients

Article

Jun 2024

Brain Interaction Assessment Using EEG Source Localization: sLORETA

Chapter

May 2024

In standardized low-resolution brain electromagnetic tomography (SLORETA), the electrical activity recorded by the electroencephalogram (EEG) electrodes, is transformed into a three-dimensional source distribution within the brain. It helps us in understanding the internal details of human brain and its working. It helps us in visualizing and analyzing the activity of the brain electrically with exceptional precision. The EEG recordings along with mathematical algorithms (advanced) help in reconstruction of neural foundational blocks for the recorded brain signals. This transformation is achieved by solving an inverse problem using linear, weighted minimum norm estimation. By solving this inverse problem, SLORETA estimates the locations and strengths of neural sources underlying the measured EEG signals. The accuracy and reliability of sLORETA have been validated through comparisons with other neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET).

Bayesian estimation of group event-related potential components (BEGEP): testing a model for synthetic and real datasets

Article

May 2024

Objective. The spatial resolution of event-related potentials (ERPs) recorded on the head surface is quite low, since the sensors located on the scalp register mixtures of signals from several cortical sources. Bayesian models for multi-channel ERPs obtained from a group of subjects under multiple task conditions can aid in recovering signals from these sources. Approach. This study introduces a novel model that captures several important characteristics of ERP, including person-to-person variability in the magnitude and latency of source signals. Furthermore, the model takes into account that ERP noise, the main source of which is the background EEG, has the following properties: it is spatially correlated, spatially heterogeneous, and varies over time and from person to person. Bayesian inference algorithms have been developed to estimate the parameters of this model, and their performance has been evaluated through extensive experiments using synthetic data and real ERPs records in a large number of subjects (N=351). Main results. The signal estimates obtained using these algorithms were compared with the results of the analysis of ERPs by conventional methods. This comparison showed that the use of this model is suitable for the analysis of ERPs and helps to reveal some features of source signals that are difficult to observe in their mixture signals recorded on the scalp. Significance. This study shown that the proposed method is a potentially useful tool for analyzing ERPs collected from groups of subjects in various cognitive neuroscience experiments.

Phase Amplitude Coupling Workflow for Mapping EEG Signals to Standard Brain Networks

Conference Paper

Nov 2023

Modulation of the neuronal response in human primary visual cortex by re-entrant projections during retinal input processing as manifest in the visual evoked potential

Article

May 2024

Initial deflections in the visual evoked potential (VEP) reflect the neuronal process of extracting features from the retinal input; a process not modulated by re-entrant projections. Later deflections in the VEP reflect the neuronal process of combining features into an object, a process referred to as ‘object closure’ and modulated by re-entrant projections. Our earlier work indicated that the VEP reflects independent neuronal responses processing temporal – and spatial luminance contrast and that these responses arise from an interaction between forward and re-entrant input. In this earlier work, changing the temporal luminance contrast property of a stimulus altered its spatial luminance contrast property. We recorded the VEP in 12 volunteers viewing image pairs of a windmill, regular dartboard or an RMS dartboard rotated by either Π/4, Π/2, 3Π/4 or Π radians with respect to each other. The windmill and regular dartboard had identical white to black ratio, while the two dartboards identical contrast edges per unit area. Rotation varied temporal luminance contrast of a stimulus without affecting its spatial luminance contrast. N75, P100, N135 and P240 amplitude and latency were compared and a source localisation and temporal frequency analysis performed. P100 amplitude signals a neuronal response processing temporal luminance contrast that is modulated by re-entrant projections with fast axonal conduction velocities. N135 and P240 signal the neuronal response processing spatial luminance contrast and is modulated by re-entrant projections with slow axonal conduction velocities. The dorsal stream is interconnected by fast axonal conduction velocities, the ventral stream by slow axonal conduction velocities.

Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity

Article

May 2024
J NEUROSCI METH

MEG and EEG: source estimation

Chapter

Sep 2012

Neuroscientists employ many different techniques to observe the activity of the brain, from single-channel recording to functional imaging (fMRI). Many practical books explain how to use these techniques, but in order to extract meaningful information from the results it is necessary to understand the physical and mathematical principles underlying each measurement. This book covers an exhaustive range of techniques, with each chapter focusing on one in particular. Each author, a leading expert, explains exactly which quantity is being measured, the underlying principles at work, and most importantly the precise relationship between the signals measured and neural activity. The book is an important reference for neuroscientists who use these techniques in their own experimental protocols and need to interpret their results precisely; for computational neuroscientists who use such experimental results in their models; and for scientists who want to develop new measurement techniques or enhance existing ones.

Research on Motor Imagery EEG Classification Method based on Region of Interest and Electrophysiological Source Imaging Technology

Conference Paper

Dec 2023

Spectrotemporal cortical dynamics and semantic control during sentence completion

Article

Apr 2024
CLIN NEUROPHYSIOL

A Method of EEG Source Imaging Based on TCN and Attention Network

Conference Paper

Jan 2024

Unlocking Innovation: Exploring Gendered Differences in Neural Activity During Creative Tasks

Chapter

Full-text available

Apr 2024

The spatiotemporal brain dynamics of cognitive control

Preprint

Full-text available

Apr 2024

Understanding the neural foundations of response inhibition is pivotal to unraveling parts of the mechanisms of cognitive control. Advanced human neuroimaging is able to converge the temporal precision of electroencephalography (EEG) with the spatial accuracy of simultaneous EEG-functional magnetic resonance imaging (EEG-fMRI) to illustrate the intricate neural dynamics at play during a Go/No-Go (GNG) task, as performed by fifty-five right-handed participants. Our analysis reveals the influence of beta oscillations in the right anterior insular cortex (AIC) and inferior frontal gyrus (IFG)— areas implicated in impulse control— during the early event-related potentials (ERPs) component (N1), therefore marking the commencement of sensory processing and the mobilization of cognitive control networks. Subsequently, the propagation of beta oscillations into the left AIC and IFG underlines their importance to sensory integration. Following the early ERP stages, the engagement of the anterior cingulate cortex (ACC) during the late ERP component (P3) accentuates its function in arbitrating cognitive control and decision-making. Furthermore, the subsequent activations within the supplementary motor area (SMA) and primary motor cortex (M1) demarcate their roles in network operation and action termination. In light of the recent surge in interest in exploring the nuances of response inhibition under diverse stimulus visibility thresholds — specifically supraliminal versus subliminal — we too investigated the impact of perceptual awareness on the activation of the response inhibition network. We discerned divergent neural activations between conditions: the supraliminal state induced uniform activity across the network, whereas subliminal conditions presented a more convoluted, reciprocal pattern among these regions and the visual cortex. Our inquiry furthers the understanding of the temporal and oscillatory mechanisms that fortify response inhibition, offering perspectives on the implicated brain regions and their functional orchestration in response to varying stimulus visibility. This work holds promise for the development of time-efficient, imaging-based biomarkers that could facilitate the diagnosis of conditions characterized by impaired impulse control, such as addiction and attention-deficit/hyperactivity disorder.

The Resolving Power of Gross Earth Data

Article

Full-text available

Oct 1968
GEOPHYS J INT

A gross Earth datum is a single measurable number describing some property of the whole Earth, such as mass, moment of interia, or the frequency of oscillation of some identified elastic-gravitational normal mode. We show how to determine whether a given finite set of gross Earth data can be used to specify an Earth structure uniquely except for fine-scale detail; and how to determine the shortest length scale which the given data can resolve at any particular depth. We apply the general theory to the linear problem of finding the depth-variation of a frequency-independent local Q from the observed quality factors Q of a finite number of normal modes. We also apply the theory to the non-linear problem of finding density vs depth from the total mass, moment, and normal-mode frequencies, in case the compressional and shear velocities are known.

Interpreting measured magnetic fields of the brain: Estimates of current distributions

Article

Full-text available

Dec 1984

Noninvasive Localization of Electromagnetic Epileptic Activity. I. Method Descriptions and Simulations

Article

Full-text available

Feb 2001

This paper considers the solution of the bioelectromagnetic inverse problem with particular emphasis on focal compact sources that are likely to arise in epileptic data. Two linear inverse methods are proposed and evaluated in simulations. The first method belongs to the class of distributed inverse solutions, capable of dealing with multiple simultaneously active sources. This solution is based on a Local Auto Regressive Average (LAURA) model. Since no assumption is made about the number of activated sources, this approach can be applied to data with multiple sources. The second method, EPIFOCUS, assumes that there is only a single focal source. However, in contrast to the single dipole model, it allows the source to have a spatial extent beyond a single point and avoids the non-linear optimization process required by dipole fitting. The performance of both methods is evaluated with synthetic data in noisy and noise free conditions. The simulation results demonstrate that LAURA and EPIFOCUS increase the number of sources retrieved with zero dipole localization error and produce lower maximum error and lower average error compared to Minimum Norm, Weighted Minimum Norm and Minimum Laplacian (LORETA). The results show that EPIFOCUS is a robust and powerful tool to localize focal sources. Alternatives to localize data generated by multiple sources are discussed. A companion paper (Lantz et al. 2001, this issue) illustrates the application of LAURA and EPIFOCUS to the analysis of interictal data in epileptic patients.

Functional imaging with low resolution brain electromagnetic tomography (LORETA): Review, new comparisons, and new validation

Article

Jan 2002

Functional imaging with low resolution brain electromagnetic tomography (LORETA): review, new comparisons, and new validation. Japanese Journal of Clinical Neurophysiology 30:81-94, 2002. Authors' version. Abstract This paper provides a review of several recent publications that have successfully used the functional brain imaging method known as LORETA. Special emphasis is given to: (1) the electrophysiological and neuroanatomical basis of the method, (2) the localization and spatial blurring properties of the method, (3) the experimental validation of the method with real human data, and (4) its limitations. Papers that criticize LORETA are briefly discussed. Two new results are presented. Firstly, LORETA is compared with a recently published tomography (Dale et al., Neuron 26:55-67, 2000), in terms of localization and spatial blurring properties. The results demonstrate by far, that LORETA is the method of choice, especially when the measurements are contaminated with noise. For the sake of reproducible research, all the material (data, program code, and executables) used in the comparative study is available upon request to the corresponding author. The second result consists of the analysis of a face perception event related potential experiment. LORETA was used to test for significant activation in the first 500 milliseconds after stimulus onset. High time resolution activation signals can be computed with this new statistical procedure. Activation of Brodmann areas (BA) 17/18 and of the right fusiform gyrus (BA 37, the "face processing" area) was demonstrated. These results provide further validation for LORETA.

Multivariate Analysis

Book

Jan 1979

The resolving power of gross earth data: Geophys

Article

Jan 1968

Dynamic Statistical Parametric Mapping

Article

Apr 2000
NEURON

Functional magnetic resonance imaging (fMRI) can provide maps of brain activation with millimeter spatial resolution but is limited in its temporal resolution to the order of seconds. Here, we describe a technique that combines structural and functional MRI with magnetoencephalography (MEG) to obtain spatiotemporal maps of human brain activity with millisecond temporal resolution. This new technique was used to obtain dynamic statistical parametric maps of cortical activity during semantic processing of visually presented words. An initial wave of activity was found to spread rapidly from occipital visual cortex to temporal, parietal, and frontal areas within 185 ms, with a high degree of temporal overlap between different areas. Repetition effects were observed in many of the same areas following this initial wave of activation, providing evidence for the involvement of feedback mechanisms in repetition priming.

Inverse Problem Theory: Methods For Data Fitting and Model Parameter Estimation

Article

Jan 1987

Albert Tarantola

The book provides an up-to-date description of the methods used for fitting experimental data, or to estimate model parameters, and to unify these methods into the Inverse Problem Theory. The first part of the book deals with problems and describes Maximum likelihood, Monte Carlo, Least squares, and Least absolute values methods. The second part deals with inverse problems involving functions. Theoretical concepts are emphasized, and the author has all the useful formulas listed, with many special cases included. The book serves as a reference manual.

Theory and Application of Constrained Inverse of Matrices

Article

Jun 1973

The concept of a constrained inverse of a matrix, introduced by Bott and Duffin [1], is extended so as to bring together the various generalized inverses and pseudoinverses studied in literature under a common classification scheme. Applications of this concept are indicated in obtaining restricted solutions of consistent linear equations, E-approximate solutions of inconsistent linear equations, stable values of a quadratic function and generalized least squares analysis of a linear model with a possible singular dispersion matrix.

Co-Planar Stereotaxic Atlas of The Human Brain

Book

Jan 1988

The spatial location of EEG electrodes: locating the best-fitting sphere relative to cortical anatomy

Article

Feb 1993
Electroencephalogr Clin Neurophysiol

The location of the international 10-20 system electrode positions and 14 fiducial landmarks are described in cartesian coordinates (+/- 1.4 mm average accuracy). Six replications were obtained on 3 separate days from 4 normal subjects, who were compared to each other with a best-fit sphere algorithm. Test-retest reliability depended on the electrode position: the parasagittal electrodes were associated with greater measurement errors (maximum 7 mm) than midline locations. Location variability due to head shape was greatest in the temporal region, averaging 5 mm from the mean. For each subject's electrode locations a best-fitting sphere was determined (79-87 mm radius, 6% average error). A surface-fitting algorithm was used to transfer the electrode locations and best-fitting sphere to MR images of the brain and scalp. The center of the best-fitting sphere coincided with the floor of the third ventricle 5 mm anterior to the posterior commissure. The melding of EEG electrode location information with brain anatomy provides an empirical basis for associating hypothetical equivalent dipole locations with their anatomical substrates.

Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain

Article

Nov 1994
INT J PSYCHOPHYSIOL

This paper presents a new method for localizing the electric activity in the brain based on multichannel surface EEG recordings. In contrast to the models presented up to now the new method does not assume a limited number of dipolar point sources nor a distribution on a given known surface, but directly computes a current distribution throughout the full brain volume. In order to find a unique solution for the 3-dimensional distribution among the infinite set of different possible solutions, the method assumes that neighboring neurons are simultaneously and synchronously activated. The basic assumption rests on evidence from single cell recordings in the brain that demonstrates strong synchronization of adjacent neurons. In view of this physiological consideration the computational task is to select the smoothest of all possible 3-dimensional current distributions, a task that is a common procedure in generalized signal processing. The result is a true 3-dimensional tomography with the characteristic that localization is preserved with a certain amount of dispersion, i.e., it has a relatively low spatial resolution. The new method, which we call Low Resolution Electromagnetic Tomography (LORETA) is illustrated with two different sets of evoked potential data, the first showing the tomography of the P100 component to checkerboard stimulation of the left, right, upper and lower hemiretina, and the second showing the results for the auditory N100 component and the two cognitive components CNV and P300. A direct comparison of the tomography results with those obtained from fitting one and two dipoles illustrates that the new method provides physiologically meaningful results while dipolar solutions fail in many situations. In the case of the cognitive components, the method offers new hypotheses on the location of higher cognitive functions in the brain.

Electrical sources of P300 event-related brain potentials revealed by low resolution electromagnetic tomography

Article

Feb 1998

In a double-blind, placebo-controlled study the effects of Actovegin on frontal and parietal electrical P300 sources revealed by low resolution electromagnetic tomography (LORETA) were studied in age-associated memory impairment (AAMI) patients. Actovegin is a protein-free metabolically active hemoderivative improving oxygen and glucose utilization. Each patient had, in randomized order, a treatment of 2 weeks with 250 ml 20% Actovegin and 250 ml placebo daily. Auditory ERPs were recorded before and 5 h after drug administration on day 1 (acute effect) and on day 15 (subacute and superimposed effect). Compared to age- and sex-matched normal controls, AAMI patients showed a trend towards P300 latency prolongation and a significantly reduced P300 global field power (GFP). Maximal LORETA source strength did not differ from controls. After Actovegin parietal P300 scalp amplitudes increased, while frontal and temporal amplitudes decreased as compared to placebo. This increase in hilliness, measured by the GFP, was significant. Moreover, the parietal P300 source strength increased after acute, subacute and superimposed infusion of Actovegin as compared to placebo. This may reflect improved availability of cognitive processing resources in the parietal cortex, an area that on the one hand plays an important role in fundamental aspects of attention and on the other hand has been found to be functionally impaired in dementia.

Electrical Sources of P300 Event-Related Brain Potentials Revealed by Low Resolution Electromagnetic Tomography

Article

Feb 1998

The P300 event-related potential (ERP) is considered to be closely related to cognitive processes. Previous reports regarding major generators contributing to the scalp-recorded P300 suggested widely distributed multiple sources. Based on ERPs recorded in 172 normal healthy subjects aged between 20 and 88 years in an auditory oddball paradigm, electrical activity in the brain corresponding to N1 and P300 components was localized by means of low resolution electromagnetic tomography (LORETA). The N1 LORETA generators, located in both auditory cortices, did not change over age. On the other hand, the P300 LORETA generators, located predominantly in the frontal neocortex and less pronounced in the posterior parietal cortex, decreased over age both in frontal and parietal source strength.

Differential effects of normal aging on sources of standard N1, target N1 and target P300 auditory event-related brain potentials revealed by low resolution electromagnetic tomography (LORETA)

Article

Apr 1998
Electroencephalogr Clin Neurophysiol

The P300 event-related potential (ERP) is considered to be closely related to cognitive processes. In normal aging, P300 scalp latencies increase, parietal P300 scalp amplitudes decrease and the scalp potential field shifts to a relatively more frontal distribution. Based on ERPs recorded in 172 normal healthy subjects aged between 20 and 88 years in an auditory oddball paradigm, the effects of age on the electrical activity in the brain corresponding to N1 and P300 components were estimated by means of low resolution electromagnetic tomography (LORETA). This distributed approach directly computes a unique 3-dimensional electrical source distribution by assuming that neighbouring neurons are simultaneously and synchronously active. N1 LORETA generators, located predominantly in both auditory cortices and also symmetrically in prefrontal areas, increased with advancing age for standards but remained stable for targets. P300 LORETA generators, located symmetrically in the prefrontal cortex, in the parieto-occipital junction and in the inferior parietal cortex (supramarginal gyrus) and medially in the superior parietal cortex, were differentially affected by age. While age did not affect parieto-occipital sources, superior parietal and right prefrontal sources decreased pronouncedly. Thus, in normal aging, P300 current density decreased in regions were a fronto-parietal network for sustained attention was localized.

Three-dimensional tomography of event-related potentials during response inhibition: Evidence for phasic frontal lobe activation

Article

Aug 1998
Electroencephalogr Clin Neurophysiol

Spatial analysis of the evoked brain electrical fields during a cued continuous performance test (CPT) revealed an extremely robust anteriorization of the positivity of a P300 microstate in the NoGo compared to the Go condition (NoGo-anteriorization in a previous study). To allow a neuroanatomical interpretation the NoGo-anteriorization was investigated with a new three-dimensional source tomography method (LORETA) was applied. The CPT contains subsets of stimuli requiring the execution (Go) or the inhibition (NoGo) of a cued motor response which can be considered as mutual control conditions for the event-related potential (ERP) study of inhibitory brain functions 21-channel ERPs were obtained from 10 healthy subjects during a cued CPT, and analyzed with LORETA. Topographic analyses revealed significantly different scalp distributions between the Go and the NoGo conditions in both P100 and P300 microstates, indicating that already at an early stage different neural assemblies are activated. LORETA disclosed a significant hyperactivity located in the right frontal lobe during the NoGo condition in the P300 microstate. The results indicate that right frontal sources are responsible for the NoGo-anteriorization of the scalp P300 which is consistent with animal and human lesion studies of inhibitory brain functions. Furthermore, it demonstrates that frontal activation is confined to a brief microstate and time-locked to phasic inhibitory motor control. This adds important functional and chronometric specificity to findings of frontal activation obtained with PET and Near-Infrared-Spectroscopy studies during the cued CPT, and suggests that these metabolic results are not due to general task demands.

Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia

Article

Jul 1999
PSYCHIAT RES

Functional imaging of brain electrical activity was performed in nine acute, neuroleptic-naive, first-episode, productive patients with schizophrenia and 36 control subjects. Low-resolution electromagnetic tomography (LORETA, three-dimensional images of cortical current density) was computed from 19-channel electroencephalographic (EEG) activity obtained under resting conditions, separately for the different EEG frequencies. Three patterns of activity were evident in the patients: (1) an anterior, near-bilateral excess of delta frequency activity; (2) an anterior-inferior deficit of theta frequency activity coupled with an anterior-inferior left-sided deficit of alpha-1 and alpha-2 frequency activity; and (3) a posterior-superior right-sided excess of beta-1, beta-2 and beta-3 frequency activity. Patients showed deviations from normal brain activity as evidenced by LORETA along an anterior-left-to-posterior-right spatial axis. The high temporal resolution of EEG makes it possible to specify the deviations not only as excess or deficit, but also as inhibitory, normal and excitatory. The patients showed a dis-coordinated brain functional state consisting of inhibited prefrontal/frontal areas and simultaneously overexcited right parietal areas, while left anterior, left temporal and left central areas lacked normal routine activity. Since all information processing is brain-state dependent, this dis-coordinated state must result in inadequate treatment of (externally or internally generated) information.

Nonparametric permutation tests for functional neuroimaging: A primer with examples

Article

Jan 2002

Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices.

Anatomically Informed Basis Functions for EEG Source Localization: Combining Functional and Anatomical Constraints

Article

Aug 2002

Distributed linear solutions have frequently been used to solve the source localization problem in EEG. Here we introduce an approach based on the weighted minimum norm (WMN) method that imposes constraints using anatomical and physiological information derived from other imaging modalities. The anatomical constraints are used to reduce the solution space a priori by modeling the spatial source distribution with a set of basis functions. These spatial basis functions are chosen in a principled way using information theory. The reduced problem is then solved with a classical WMN method. Further (functional) constraints can be introduced in the weighting of the solution using fMRI brain responses to augment spatial priors. We used simulated data to explore the behavior of the approach over a range of the model's hyperparameters. To assess the construct validity of our method we compared it with two established approaches to the source localization problem, a simple weighted minimum norm and a maximum smoothness (Loreta-like) solution. This involved simulations, using single and multiple sources that were analyzed under different levels of confidence in the priors.

Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

Article

Oct 2002

Distributed linear solutions of the EEG source localization problem are used routinely. Here we describe an approach based on the weighted minimum norm method that imposes constraints using anatomical and physiological information derived from other imaging modalities to regularize the solution. In this approach the hyperparameters controlling the degree of regularization are estimated using restricted maximum likelihood (ReML). EEG data are always contaminated by noise, e.g., exogenous noise and background brain activity. The conditional expectation of the source distribution, given the data, is attained by carefully balancing the minimization of the residuals induced by noise and the improbability of the estimates as determined by their priors. This balance is specified by hyperparameters that control the relative importance of fitting and conforming to prior constraints. Here we introduce a systematic approach to this regularization problem, in the context of a linear observation model we have described previously. In this model, basis functions are extracted to reduce the solution space a priori in the spatial and temporal domains. The basis sets are motivated by knowledge of the evoked EEG response and information theory. In this paper we focus on an iterative "expectation-maximization" procedure to jointly estimate the conditional expectation of the source distribution and the ReML hyperparameters on which this solution rests. We used simulated data mixed with real EEG noise to explore the behavior of the approach with various source locations, priors, and noise levels. The results enabled us to conclude: (i) Solutions in the space of informed basis functions have a high face and construct validity, in relation to conventional analyses. (ii) The hyperparameters controlling the degree of regularization vary largely with source geometry and noise. The second conclusion speaks to the usefulness of using adaptative ReML hyperparameter estimates.

Review of Methods for Solving the EEG Inverse Problem

Article

Oct 1999

Roberto Domingo Pascual-marqui

This paper reviews the class of instantaneous, 3D, discrete, linear solutions for the EEG inverse problem. Five different inverse methods are analyzed and compared: minimum norm, weighted minimum norm, Backus and Gilbert, weighted resolution optimization (WROP), and low resolution brain electromagnetic tomography (LORETA). The inverse methods are compared by testing localization errors in the estimation of single and multiple sources. These tests constitute the minimum necessary condition to be satisfied by any tomography. Of the five inverse solutions tested, only LORETA demonstrates the ability of correct localization in 3D space. The other four inverse solutions should not be used if the research aim is to localize the neuronal generators of EEG in a 3D brain. In this sense, minimum norm, weighted minimum norm, Backus and Gilbert, and WROP can be likened to x-rays, where depth information is totally lacking. For the sake of reproducible research, all the material and methods used in this part of the study, consisting of computer programs (source code and executables) and data, are available upon request to the author. In this way, all the results and conclusions can be checked, reproduced, and validated by the interested reader.

Dynamic statistical parametric mapping: combining fMRI and MEG for highresolution imaging of cortical activity

Jan 2000
55-67

Am Dale
Ak Liu
Br Fischl
Rl Buckner
Jw Belliveau
Jd Lewine
E Halgren

Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW, Lewine JD, Halgren E. Dynamic statistical parametric mapping: combining fMRI and MEG for highresolution imaging of cortical activity. Neuron 2000, 26: 55-67.

The surface management system' (SuMS) database: a surface-based database to aid cortical surface reconstruction, visualization and analysis. Philosophical Transactions of the Cortices downloadable at: http://stp.wustl.edu 14) Pascual-Marqui RD. Reply to Comments Made by

Jan 1999
1277-1292

J Dickson
H Drury
Dc Van Essen

Dickson J, Drury H., Van Essen DC. 'The surface management system' (SuMS) database: a surface-based database to aid cortical surface reconstruction, visualization and analysis. Philosophical Transactions of the Royal Society, London, B 2001, 356: 1277-1292. Cortices downloadable at: http://stp.wustl.edu 14) Pascual-Marqui RD. Reply to Comments Made by R. Grave De Peralta Menendez and S.I. Gozalez Andino. International Journal of Bioelectromagnetism 1999, Vol. 1, No. 2, at: http://www.ee.tut.fi/rgi/ijbem/volume1/number2/html/pascual.htm.

Standardized low resolution brain electromagnetic tomography (SLORETA): Technical details

Abstract

No full-text available

Recommended publications

Der Einfluß der Randelementediskretisierung auf die Vorwärtsrechnung und das inverse Problem in Elek...