Scalp EEG Signals Research Articles

A longitudinal study of differences in electroencephalographic activity among breastfed, milk formula-fed, and soy formula-fed infants during the first year of life

Background The extent to which adequate nutrition from infant diets differentially influence developmental outcomes in healthy infants has not been determined. Aim To compare the effects of the major infant diets on the development of brain electrical activity during infancy. Study design Scalp EEG signals (124 sites) recorded from the same infants during quiet wakefulness at 3, 6, 9, and 12 months. Subjects Healthy, full-term infants (40/group; gender matched) either breastfed (BF) or fed milk formula (MF) or soy formula (SF) through the first 6 months. Outcome measures Power spectral values for frequencies in the 0.1–30 Hz range. Results Significant diet-related differences were present across frequency bands and included effects that were time- [peaks in 0.1–3 Hz at 6 (MF,SF) and 9 months (BF); 3–6 Hz at 6 months (MF, SF > BF); increases in 6–9 Hz from 3 to 6 months (MF > BF) and from 6 to 9 months (MF > SF)] and gender-related (9–12 Hz and 12–30 Hz: at 9 months BF > MF, SF boys, and MF > SF girls). Conclusions The development of brain electrical activity during infancy differs between those who are breastfed compared with those fed either milk or soy formula, but is generally similar for formula-fed groups. These variations in EEG activity reflect diet-related influences on the development of brain structure and function that could put infants on different neurodevelopmental trajectories along which cognitive and brain function development will proceed.

Cortical Network Analysis in Patients Affected by Schizophrenia

In the present study, we studied the structural changes of the brain functional network in a group of schizophrenic (SCHZ) patients during a 2-back working memory task. Cortical signals were obtained from scalp EEG signals through the high-resolution EEG technique, which relies on realistic head models and linear inverse solutions. Functional networks were estimated by computing the spectral coherence--i.e. a measure of synchronization in the frequency domain--between the time series of all the available cortical sources. To analyze those cortical networks we followed a theoretical graph approach by computing the network density as the total number of links and the node degree as the number of links of each cortical source. The major result suggest that in the Alpha2 frequency band (11-13 Hz) the cortical functional networks of the SCHZ patients present the largest differences when compared with those of a group of control (CTRL) subjects. In particular, the structure of the SCHZ network altered radically during the memory task, as the number of links that were different from the REST condition increased sensibly with respect to the CTRL network. In addition, a compensatory mechanism was found in the SCHZ patients during the correct performance of the memory task where the node degree showed a frontal asymmetry with higher activation of the left frontal lobe--i.e. higher number of connections--in the Alpha2 frequency band.

The Graph Theoretical Approach in Brain Functional Networks: Theory and Applications

The present book illustrates the theoretical aspects of several methodologies related to the possibility of i) enhancing the poor spatial information of the electroencephalographic (EEG) activity on the scalp and giving a measure of the electrical activity on the cortical surface. ii) estimating the directional influences between any given pair of channels in a multivariate dataset. iii) modeling the brain networks as graphs. The possible applications are discussed in three different experimental designs regarding i) the study of pathological conditions during a motor task, ii) the study of memory processes during a cognitive task iii) the study of the instantaneous dynamics throughout the evolution of a motor task in physiological conditions. The main outcome from all those studies indicates clearly that the performance of cognitive and motor tasks as well as the presence of neural diseases can affect the brain network topology. This evidence gives the power of reflecting cerebral states or traits to the mathematical indexes derived from the graph theory. In particular, the observed structural changes could critically depend on patterns of synchronization and desynchronization - i.e. the dynamic binding of neural assemblies - as also suggested by a wide range of previous electrophysiological studies. Moreover, the fact that these patterns occur at multiple frequencies support the evidence that brain functional networks contain multiple frequency channels along which information is transmitted. The graph theoretical approach represents an effective means to evaluate the functional connectivity patterns obtained from scalp EEG signals. The possibility to describe the complex brain networks sub-serving different functions in humans by means of numbers is a promising tool toward the generation of a better understanding of the brain functions.

Utility of Independent Component Analysis for Interpretation of Intracranial EEG

Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20–80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity.

Nonlinear features of surface EEG showing systematic brain signal adaptations with muscle force and fatigue

Nonlinear dynamics has been introduced to the analysis of biological data and increasingly recognized to be functionally relevant. The purpose of this study was to examine chaotic properties of human scalp EEG signals associated with voluntary motor tasks using the largest Lyapunov exponent (L1). 64-channel scalp EEG data were recorded from eight healthy subjects in two tasks: (1) intermittent handgrip contractions at 20, 40, 60, and 80% of maximal voluntary contraction (MVC) with 20 trials at each level. No significant fatigue were induced; (2) intermittent handgrip MVCs (100 trials) that resulted in significant fatigue. The L1 values of all EEG channels were calculated in each trial first then averaged across the 20 trials at each force level (Task 1) or over each of the 5-trial blocks (Task 2) before the group means were obtained. A multivariate statistical model was used to examine the effect of force and fatigue on L1. L1 values were greater with higher force (Task 1), and decreased significantly with fatigue (Task 2). The L1 of the EEG signals changes systematically and correlates significantly with muscle force and fatigue. The results suggest that nonlinear chaotic index L1 may serve as a quantitative measure for motor control-related cortical signal adaptations.

Spatial analysis of intracerebral electroencephalographic signals in the time and frequency domain: identification of epileptogenic networks in partial epilepsy

Electroencephalography (EEG) occupies an important place for studying human brain activity in general, and epileptic processes in particular, with appropriate time resolution. Scalp EEG or intracerebral EEG signals recorded in patients with drug-resistant partial epilepsy convey important information about epileptogenic networks that must be localized and understood prior to subsequent therapeutic procedures. However, this information, often subtle, is ‘hidden’ in the signals. It is precisely the role of signal processing to extract this information and to put it into a ‘coherent and interpretable picture’ that can participate in the therapeutic strategy. Nowadays, the panel of available methods is very wide depending on the objectives such as, for instance, the detection of transient epileptiform events, the detection and/or prediction of seizures, the recognition and/or the classification of EEG patterns, the localization of epileptic neuronal sources, the characterization of neural synchrony, the determination of functional connectivity, among others. The intent of this paper is to focus on a specific category of methods providing relevant information about epileptogenic networks from the analysis of spatial properties of EEG signals in the time and frequency domain. These methods apply to either interictal or ictal recordings and share the common objective of localizing the subsets of brain structures involved in both types of paroxysmal activity. Most of these methods were developed by our group and are routinely used during pre-surgical evaluation. Examples are detailed. Results, as well as limitations of the methods, are also discussed.

Infant diet differentially affects human electroencephalographic activities in the first year of life

The influence of infant diet (milk‐based formula [MF], soy‐based formula [SF], and breast milk [BF]) on brain EEG activities was studied in infants (20 males and 20 females per group) at 3, 6, 9, and 12 months of age. Power spectra were calculated in five frequency bands for scalp EEG signals recorded at 124 sites. ANOVA tests were applied to determine effects of diet, gender, age, brain area, and hemisphere. Diet significantly affected EEG spectral power. In Band 1 (0.1–3 Hz), the highest spectral power was observed at 9 months in BF infants in contrast to 6 months in MF and SF infants. In Band 2 (3–6 Hz), compared to MF and SF infants, BF infants had a smaller increase in EEG spectral power from 3 to 6 months. In Band 3 (6–9 Hz), 9‐month‐old MF infants had higher EEG spectral power in anterior brain areas and lower power in posterior areas relative to BF infants. In Band 4 (9–12 Hz), BF boys showed higher spectral power than BF girls in general, but girls in the MF and SF groups had higher spectral power than boys in those groups. This effect was also observed in Band 5 (12–30 Hz). In addition, EEG spectral power was higher in the left hemisphere relative to the right hemisphere in all infants. Based on these differences for gender, age, brain areas, and hemisphere, our results suggest that infant diet differentially modifies the development of brain physiological functions in the first year of life. (Supported by USDA CRIS 6251‐51000‐004‐01S)

The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

This overview covers recent advances in the field of EEG/MEG signal processing and modeling in epilepsy regarding both interictal and ictal phenomena. In the first part, the main methods used in the analysis of interictal EEG/MEG epileptiform spikes are presented and discussed. Source and volume conductor models are passed in review, namely the equivalent dipole source concept, the requirements for adequate time and spatial sampling, the question of how to validate source solutions, particularly by comparing solutions obtained using scalp and intracranial EEG signals, EEG & MEG data, or EEG simultaneously recorded with fMRI (BOLD signals). In the second part, methods used for the characterization of seizures are considered, namely dipolar modeling of spikes at seizure onset, decomposition of seizure EEG signals into sets of orthogonal spatio-temporal components, and also methods (linear and nonlinear) of estimating seizure propagation. In the third part, the crucial issue of how the transition between interictal and seizure activity takes place is examined. In particular the vicissitudes of the efforts along the road to seizure prediction are shortly reviewed. It is argued that this question can be reduced to the problem of estimating the excitability state of neuronal populations in the course of time as a seizure approaches. The value of active probing methods in contrast with passive analytical methods is emphasized. In the fourth part modeling aspects are considered in the light of two special kinds of epilepsies, absences characterized by spike-and-wave discharges and mesial temporal lobe epilepsy. These two types correspond to different scenarios regarding the transition to epileptic seizures, namely the former is a case of a jump transition and the latter is a typical case of gradual transition. In conclusion, the necessity of developing comprehensive computational models of epileptic seizures is emphasized.

Propagation of interictal discharges in temporal lobe epilepsy: Correlation of spatiotemporal mapping with intracranial foramen ovale electrode recordings

Objective We have investigated intracerebral propagation of interictal epileptiform discharges (IED) in patients with mesial temporal lobe epilepsy (MTLE) by using spatiotemporal source maps based on statistical nonparametric mapping (SNPM) of low resolution electromagnetic tomography (LORETA) values. Methods We analyzed 30 patterns of IED recorded simultaneously with scalp and intracranial foramen ovale (FO) electrodes in 15 consecutive patients with intractable MTLE. The scalp EEG signals were averaged time-locked to the peak activity in bilateral 10-contact FO electrode recordings. SNPM was applied to LORETA values and spatiotemporal source maps were created by allocating the t-values over time to their corresponding Brodmann areas. Propagation was defined as secondary statistically significant involvement of distinct cortical areas separated by >15 ms. The results were correlated with intracranial data obtained from FO electrode recordings and with scalp EEG recordings. All patients underwent subsequent amygdalo-hippocampectomy and outcome was assessed one year after surgery. Results We found mesial to lateral propagation in 6/30 IED patterns (20%, four patients), lateral to mesial propagation in 4/30 IED patterns (13.3%, four patients) and simultaneous (within 15 ms) activation of mesial and lateral temporal areas in 6/30 IED patterns (20%, five patients). Propagation generally occurred within 30 ms and was always limited to ipsilateral cortical regions. Nine/30 IED patterns (30%) showed restricted activation of mesial temporal structures and no significant solutions were found in 5/30 IED patterns (16.7%). There was no clear association between the number or characteristics of IED patterns and the postsurgical outcome. Conclusions Spatiotemporal mapping of SNPM LORETA accurately describes mesial to lateral temporal propagation of IED, and vice versa, which commonly occur in patients with MTLE. Significance Intracerebral propagation must be considered when using non-invasive source algorithms in patients with MTLE. Spatiotemporal mapping might be useful for visualizing this propagation.

Automatic lateralization of temporal lobe epilepsy based on scalp EEG

Objective The objective of this work is the determination of the lateralization of the epileptic seizure onset zone using the scalp EEG signal processing. Methods A comprehensive method based on the evaluation of the evolution of the correlation coefficients computed between bipolar channels (longitudinal montage) was applied to 43 patients (87 seizures). The correlation coefficients are estimated by a nonlinear regression analysis. The methodology that leads to the lateralization is based on several processing steps: segmentation, seizure onset determination and then lateralization. Results Results show that the mean level of the nonlinear correlation values computed between EEG channels at the seizure onset time is significantly higher on the side of the beginning of a seizure. Conclusions The side of the seizure onset was determined for about 80–90% of the seizures studied with a satisfactory high reproducibility level. Significance Comparison of nonlinear correlation coefficients between both sides of the brain leads to the determination of the side of seizure onset.

Bilateral oscillations for lateralized spikes in benign rolandic epilepsy

Purposes To elucidate the oscillatory dynamics with respect to interictal spike occurrence in benign rolandic epilepsy (BRE). Methods Using a whole-scalp magnetoencephalography (MEG), we recorded scalp EEG and MEG signals in 10 BRE patients (age 8–12 years) and visually identified unilateral interictal spikes that were simultaneously present on both EEG and MEG channels. We obtained the peak timing of individual spike complex based on MEG single-dipole modeling, and then applied wavelet transform to analyze the time–frequency components of corresponding MEG signals with respect to spike occurrence. Results In the hemisphere with time-domain spike waveforms, we identified a clear increase of 0.5–40 Hz activity around the spike peak, most prominent at alpha band (8–13 Hz). Notably, at the approximate timing we also observed an increase in 0.5–25 Hz oscillations over the homotopic area in the other hemisphere where no spike signals were found. Conclusions Our results indicate bilateral increases in 0.5–25 Hz oscillations during unilateral spike formation in BRE patients. By using wavelet transform analysis, one could be able to detect some irritative feature that would in visual analysis remain undetected.

Adaptive autoregressive identification with spectral power decomposition for studying movement-related activity in scalp EEG signals and basal ganglia local field potentials

We propose a method that combines adaptive autoregressive (AAR) identification and spectral power decomposition for the study of movement-related spectral changes in scalp EEG signals and basal ganglia local field potentials (LFPs). This approach introduces the concept of movement-related poles, allowing one to study not only the classical event-related desynchronizations (ERD) and synchronizations (ERS), which correspond to modulations of power, but also event-related modulations of frequency. We applied the method to analyze movement-related EEG signals and LFPs contemporarily recorded from the sensorimotor cortex, the globus pallidus internus (GPi) and the subthalamic nucleus (STN) in a patient with Parkinson's disease who underwent stereotactic neurosurgery for the implant of deep brain stimulation (DBS) electrodes. In the AAR identification we compared the whale and the exponential forgetting factors, showing that the whale forgetting provides a better disturbance rejection and it is therefore more suitable to investigate movement-related brain activity. Movement-related power modulations were consistent with previous studies. In addition, movement-related frequency modulations were observed from both scalp EEG signals and basal ganglia LFPs. The method therefore represents an effective approach to the study of movement-related brain activity.

Distinct brain activation patterns for human maximal voluntary eccentric and concentric muscle actions

Eccentric muscle contractions generate greater force at a lower level of activation and subject muscles to more severe damage than do concentric actions. A recent investigation has revealed that electroencephalogram (EEG)-derived movement-related cortical potential (MRCP) is greater and occurs earlier for controlling human eccentric than concentric submaximal muscle contractions. However, whether the central nervous system (CNS) control signals for high-intensity or maximal-effort eccentric movements differ from those for concentric actions is unknown. The purpose of this study was to determine whether the MRCP signals differ between the two types of maximal-effort contractions. Eight volunteers performed 40 maximal voluntary eccentric and 40 maximal voluntary concentric elbow flexor contractions on a Kin-Com isokinetic dynamometer. Scalp EEG signals (62 channels) were measured along with force, joint angle, and electromyographic (EMG) signals of the performing muscles. MRCP-based two-dimensional brain maps were created to illustrate spatial and temporal distributions of the MRCP signals. Although the level of elbow flexor muscle activity was lower during eccentric than concentric movements, MRCP-indicated cortical activation was greater both in amplitude and area dimension for the eccentric task. Detailed comparisons of individual electrode signals suggested that eccentric movements needed a significantly longer time for early preparation and a significantly greater magnitude of cortical activity for later movement execution. The extra preparation time and higher amplitude of activation may reflect CNS activities that account for the higher risk of injury, higher degree of movement difficulty, and unique motor unit activation pattern associated with maximal-level eccentric muscle actions.

Motor imagery task classification for brain computer interface applications using spatiotemporal principle component analysis

Classification of single-trial imagined left- and right-hand movements recorded through scalp EEG are explored in this study. Classical event-related desynchronization/synchronization (ERD/ERS) calculation approach was utilized to extract ERD features from the raw scalp EEG signal. Principle Component Analysis (PCA) was used for feature extraction and applied on spatial, as well as temporal dimensions in two consecutive steps. A Support Vector Machine (SVM) classifier using a linear decision function was used to classify each trial as either left or right. The present approach has yielded good classification results and promises to have potential for further refinement for increased accuracy as well as application in online brain computer interface (BCI).

Independent Component Analysis Compared to Laplacian Filtering as ”Deblurring” Techniques for Event Related Desynchronization/ Synchronization

The aim of the work was to compare two different approaches - one model-dependent, the other data-dependent - for "deblurring" EEG data, in order to improve the estimation of Event-Related Desynchronization/Synchronization. Realistic Surface Laplacian filtering (SL) and Infomax Independent Component Analysis (ICA) were applied on multivariate scalp EEG signals (SL: 128 electrodes with MRI-based realistic modeling; ICA: a subset of 19 electrodes, no MRI) prior to beta Event Related Synchronization (ERS) estimation after finger movement in 8 normal subjects. ERS estimation was performed using standard band-pass filtering. ERS peak amplitudes and latencies in the most responsive channel were calculated and the effect of the two methods above was evaluated by one-way analysis of variance (ANOVA) and Sheffe's test. Both methods and their combination significantly improved ERS estimation (greater ERS peak amplitude, p <0.05). The results obtained after ICA on 19 electrodes were not significantly different than the ones obtained with Realistic SL using 128 electrodes and MRI for scalp modeling (p >0.89). The "low cost" of ICA (19 electrodes, no MRI) imposes such method as a valid alternative to SL filtering. The employ of ICA after SL filtering suggests that the "ideal EEG deblurring method" would unify the two approaches, depending on both the scalp model and the data.

Timely detection of dynamical change in scalp EEG signals.

We present a robust, model-independent technique for quantifying changes in the dynamics underlying nonlinear time-serial data. After constructing discrete density distributions of phase-space points on the attractor for time-windowed data sets, we measure the dissimilarity between density distributions via L(1)-distance and chi(2) statistics. The discriminating power of the new measures is first tested on data generated by the Bondarenko "synthetic brain" model. We also compare traditional nonlinear measures and the new dissimilarity measures to detect dynamical change in scalp EEG data. The results demonstrate a clear superiority of the new measures in comparison to traditional nonlinear measures as robust and timely discriminators of changing dynamics. (c) 2000 American Institute of Physics.

Power changes due to visuo-motor task in scalp EEG and MEG source signals

Event Abstract Back to Event Power changes due to visuo-motor task in scalp EEG and MEG source signals Silvia Erla1, 2*, Christos Papadelis2, Christoph Braun2, Luca Faes1 and Giandomenico Nollo1 1 Nicolaus Copernicus University, Athena project team, Poland 2 University of trento, Functional Neuroimaging Lab, CIMeC, Italy Aim: This work aims to quantify the main oscillatory components of the brain electromagnetic signals, as well as their possible task-induced changes by analyzing the power spectrum of scalp EEG signals and MEG virtual sensors. Methods: MEG and EEG signals were recorded from ten healthy subjects at rest and during performing a visuo-motor compensation task. The analysis was performed on the scalp EEG recordings (33 channels, 10-20 SI), and on 15 virtual sensors estimated from the MEG signals by using Synthetic Aperture Magnetometry (SAM) as implemented in the CTF software (1). Parametric autoregressive spectral decomposition (2) was applied to the two datasets, estimating the alpha, beta and gamma power for each subject, channel and condition. Three brain regions were considered (visual, motor and somatosensory cortex) and the average power was estimated from all electrodes belonging to each region. Three-way ANOVA was performed to test possible differences on power: (i) between the two conditions (rest, task); (ii) among cortex positions (occipital, motor, somatosensory); and (iii) between the two brain hemispheres. When ANOVA yielded a significant p value, post-hoc multiple testing was performed to assess differences between pairs of power distributions (Student t-test for paired data with Holm's correction). Results: In all considered regions, the power contents of EEG and MEG virtual sensors signals decreased during performing the visuo-motor task. Furthermore, during task, power spectra were higher in motor cortex than in occipital and somoatosensory related signals. Finally, alpha beta and gamma powers were significantly different between the two hemispheres. These results were consistent for both scalp EEG and MEG virtual sensors. Conclusion: These findings showed the ability of the considered spectral estimator to reveal the neuronal network activation during task performance engaging somatosensory, visual and motor regions. The agreement between the power spectra changes in scalp EEG and MEG source recording modalities substantiates the role of cortical signals analysis in neurosciences research encouraging a more detailed examination of the advantages of source signals estimation compared to scalp signals power analysis.