Electromagnetic Brain Activity Research Articles

EEG entropy insights in the context of physiological aging and Alzheimer's and Parkinson's diseases: a comprehensive review.

In recent decades, entropy measures have gained prominence in neuroscience due to the nonlinear behaviour exhibited by neural systems. This rationale justifies the application of methods from the theory of nonlinear dynamics to cerebral activity, aiming to detect and quantify its variability more effectively. In the context of electroencephalogram (EEG) signals, entropy analysis offers valuable insights into the complexity and irregularity of electromagnetic brain activity. By moving beyond linear analyses, entropy measures provide a deeper understanding of neural dynamics, particularly pertinent in elucidating the mechanisms underlying brain aging and various acute/chronic-progressive neurological disorders. Indeed, various pathologies can disrupt nonlinear structuring in neural activity, which may remain undetected by linear methods such as power spectral analysis. Consequently, the utilization of nonlinear tools, including entropy analysis, becomes crucial for capturing these alterations. To establish the relevance of entropy analysis and its potential to discern between physiological and pathological conditions, this review discusses its diverse applications in studying healthy brain aging and neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). Various entropy parameters, such as approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), and permutation entropy (PermEn), are analysed within this context. By quantifying the complexity and irregularity of EEG signals, entropy analysis may serve as a valuable biomarker for early diagnosis, treatment monitoring, and disease management. Such insights offer clinicians crucial information for devising personalized treatment and rehabilitation plans tailored to individual patients.

Measuring the Electromagnetic Field of the Human Brain at a Distance Using a Shielded Electromagnetic Field Channel.

IntroductionThe electromagnetic field (EMF) of the human brain generated by the movement of ions in the brain can be measured in a novel manner. The measurement can be completed through the skull, in a non-contact, non-invasive, continuous manner using a lightweight helmet. This investigation was conducted to determine if brain activity from movement and thoughts of movement can be measured at a distance and if that measurement can be readily evaluated at a distance using shielding with a shielded helmet and a shielded EMF channel surrounding a sensor.MethodsNon-clinical human subject volunteers donned a lightweight sensor helmet and performed a variety of specific tasks synchronized with an audible tone generated by a metronome. Constructs were created to determine if the human subjects’ brain EMF can be recorded at a distance using sensors surrounded by shielding acting similar to a waveguide in an EMF channel connected to a shielded helmet.ResultsThe EMF sensors appeared to record brain electromagnetic activity as it is funneled into a shielded channel acting as a waveguide at a considerable distance including distances as far as 63 cm away.ConclusionSpecific brain EMFs from movement, thoughts of movement, and emotional thought can be continuously measured in a non-contact fashion at a distance using an EMF waveguide approach with an EMF channel and shielded helmet.

Head magnetomyography (hMMG): A novel approach to monitor face and whole head muscular activity.

Muscular activity recording is of high basic science and clinical relevance and is typically achieved using electromyography (EMG). While providing detailed information about the state of a specific muscle, this technique has limitations such as the need for a priori assumptions about electrode placement and difficulty with recording muscular activity patterns from extended body areas at once. For head and face muscle activity, the present work aimed to overcome these restrictions by exploiting magnetoencephalography (MEG) as a whole head myographic recorder (head magnetomyography, hMMG). This is in contrast to common MEG studies, which treat muscular activity as artifact in electromagnetic brain activity. In a first proof‐of‐concept step, participants imitated emotional facial expressions performed by a model. Exploiting source projection algorithms, we were able to reconstruct muscular activity, showing spatial activation patterns in accord with the hypothesized muscular contractions. Going one step further, participants passively observed affective pictures with negative, neutral, or positive valence. Applying multivariate pattern analysis to the reconstructed hMMG signal, we were able to decode above chance the valence category of the presented pictures. Underlining the potential of hMMG, a searchlight analysis revealed that generally neglected neck muscles exhibit information on stimulus valence. Results confirm the utility of hMMG as a whole head electromyographic recorder to quantify muscular activation patterns including muscular regions that are typically not recorded with EMG. This key advantage beyond conventional EMG has substantial scientific and clinical potential.

Rogue bioelectrical waves in the brain: the Hurst exponent as a potential measure for presurgical mapping in epilepsy

Objective. Brain electromagnetic activity in patients with epilepsy is characterized by abnormal high-amplitude transient events (spikes) and abnormal patterns of synchronization of brain rhythms that accompany epileptic seizures. With the aim of improving methods for identifying epileptogenic sources in magnetoencephalographic (MEG) recordings of brain data, we applied methods previously used in the study of oceanic ‘rogue waves’ and other freak events in complex systems. Approach. For data from three patients who were awaiting surgical treatment for epilepsy, we used a beamformer source model to produce volumetric maps showing areas with a high proportion of spikes that could be classified as ‘rogue waves’, and areas with high Hurst exponent (HE). The HE describes the extent to which a system is exhibiting persistent behavior, may predict the likelihood of freak events. These measures were compared with the more standard measure of kurtosis, which has been shown to be a reliable method for localizing interictal spikes. Main results. There was partial concordance between the three different volumetric maps indicating that each measure provides different information about the underlying brain data. The HE, when combined with a simple connectivity analysis based on phase slope index (PSI), was able to identify the probable epileptogenic zone in all three patients, despite very different patterns of abnormal activity. The differences between distributions of high HE and high kurtosis values indicates that while spikes are propagated through cortex from the epileptogenic zone, the persistent dynamical conditions under which the spikes are generated may not be propagated in a similar way. Finally, the patterns of persistent activity, indicating a departure from ‘healthy criticality’ in brain networks may explain the wide range of social and cognitive impairments that are seen in epilepsy patients. Significance. The HE is a potentially useful addition to the clinician’s battery of measures which may be used convergently to guide surgical intervention.

Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity.

Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms. Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared. The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB. In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.

Recording Brain Electromagnetic Activity During the Administration of the Gaseous Anesthetic Agents Xenon and Nitrous Oxide in Healthy Volunteers.

Anesthesia arguably provides one of the only systematic ways to study the neural correlates of global consciousness/unconsciousness. However to date most neuroimaging or neurophysiological investigations in humans have been confined to the study of γ-Amino-Butyric-Acid-(GABA)-receptor-agonist-based anesthetics, while the effects of dissociative N-Methyl-D-Aspartate-(NMDA)-receptor-antagonist-based anesthetics ketamine, nitrous oxide (N2O) and xenon (Xe) are largely unknown. This paper describes the methods underlying the simultaneous recording of magnetoencephalography (MEG) and electroencephalography (EEG) from healthy males during inhalation of the gaseous anesthetic agents N2O and Xe. Combining MEG and EEG data enables the assessment of electromagnetic brain activity during anesthesia at high temporal, and moderate spatial, resolution. Here we describe a detailed protocol, refined over multiple recording sessions, that includes subject recruitment, anesthesia equipment setup in the MEG scanner room, data collection and basic data analysis. In this protocol each participant is exposed to varying levels of Xe and N2O in a repeated measures cross-over design. Following relevant baseline recordings participants are exposed to step-wise increasing inspired concentrations of Xe and N2O of 8, 16, 24 and 42%, and 16, 32 and 47% respectively, during which their level of responsiveness is tracked with an auditory continuous performance task (aCPT). Results are presented for a number of recordings to highlight the sensor-level properties of the raw data, the spectral topography, the minimization of head movements, and the unequivocal level dependent effects on the auditory evoked responses. This paradigm describes a general approach to the recording of electromagnetic signals associated with the action of different kinds of gaseous anesthetics, which can be readily adapted to be used with volatile and intravenous anesthetic agents. It is expected that the method outlined can contribute to the understanding of the macro-scale mechanisms of anesthesia by enabling methodological extensions involving source space imaging and functional network analysis.

Recording Brain Electromagnetic Activity During the Administration of the Gaseous Anesthetic Agents Xenon and Nitrous Oxide in Healthy Volunteers

Recording Brain Electromagnetic Activity During the Administration of the Gaseous Anesthetic Agents Xenon and Nitrous Oxide in Healthy Volunteers

Silent Expectations: Dynamic Causal Modeling of Cortical Prediction and Attention to Sounds That Weren't.

There is increasing evidence that human perception is realized by a hierarchy of neural processes in which predictions sent backward from higher levels result in prediction errors that are fed forward from lower levels, to update the current model of the environment. Moreover, the precision of prediction errors is thought to be modulated by attention. Much of this evidence comes from paradigms in which a stimulus differs from that predicted by the recent history of other stimuli (generating a so-called "mismatch response"). There is less evidence from situations where a prediction is not fulfilled by any sensory input (an "omission" response). This situation arguably provides a more direct measure of "top-down" predictions in the absence of confounding "bottom-up" input. We applied Dynamic Causal Modeling of evoked electromagnetic responses recorded by EEG and MEG to an auditory paradigm in which we factorially crossed the presence versus absence of "bottom-up" stimuli with the presence versus absence of "top-down" attention. Model comparison revealed that both mismatch and omission responses were mediated by increased forward and backward connections, differing primarily in the driving input. In both responses, modeling results suggested that the presence of attention selectively modulated backward "prediction" connections. Our results provide new model-driven evidence of the pure top-down prediction signal posited in theories of hierarchical perception, and highlight the role of attentional precision in strengthening this prediction. Human auditory perception is thought to be realized by a network of neurons that maintain a model of and predict future stimuli. Much of the evidence for this comes from experiments where a stimulus unexpectedly differs from previous ones, which generates a well-known "mismatch response." But what happens when a stimulus is unexpectedly omitted altogether? By measuring the brain's electromagnetic activity, we show that it also generates an "omission response" that is contingent on the presence of attention. We model these responses computationally, revealing that mismatch and omission responses only differ in the location of inputs into the same underlying neuronal network. In both cases, we show that attention selectively strengthens the brain's prediction of the future.

ASSESSMENT OF NEUROPHYSIOLOGICAL FUNCTIONS OF CENTRAL NERVOUS SYSTEM WHEN EXPOSED TO LEAD

The article presents the results of the response of the central nervous system during lead exposure using the diagnostic complex "Registrar of the spectrum of electromagnetic brain activity induction (MS MAGEE-01)". The principle of operation of MS MAGEE-01 is based on the known physiological principles of segmental structure of the peripheral nervous system and neurophysiological models of integration of autonomic and somatic functions of the nervous system developed by G.A. Shabanov and co. According to the results of functional topical diagnosis with use of the diagnostic complex MS MAGEE - 01, it has been revealed that various functions responsible for the body performance responded to the interaction of persons and lead in the form of changes in the brain tonic activity in certain groups of peripheral receptors (reflex links). The greatest response was observed in the interval from 10 to 20 minutes after exposure, in 30-40 minutes, the effect decreased slowly and all functions returned to the status of the background research phase. After the interaction with lead, there appeared changes in various organs and tissues registered for some time on the diagnostic complex MS MAGEE - 01. This determines the direction of further research and allows identification of effective ways for further screening.

Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity.

Magnetoencephalography (MEG) and electroencephalography (EEG) are invaluable neuroscientific tools for unveiling human neural dynamics in three dimensions (space, time, and frequency), which are associated with a wide variety of perceptions, cognition, and actions. MEG/EEG also provides different categories of neuronal indices including activity magnitude, connectivity, and network properties along the three dimensions. In the last 20 years, interest has increased in inter-regional connectivity and complex network properties assessed by various sophisticated scientific analyses. We herein review the definition, computation, short history, and pros and cons of connectivity and complex network (graph-theory) analyses applied to MEG/EEG signals. We briefly describe recent developments in source reconstruction algorithms essential for source-space connectivity and network analyses. Furthermore, we discuss a relatively novel approach used in MEG/EEG studies to examine the complex dynamics represented by human brain activity. The correct and effective use of these neuronal metrics provides a new insight into the multi-dimensional dynamics of the neural representations of various functions in the complex human brain.

Beamformer-based imaging of phase-amplitude coupling using electromagnetic brain activity.

Phase-amplitude coupling (PAC) between neural oscillations of different frequencies plays a crucial role in cognitive processing. Assessing the PAC at both sensor and source levels may encounter the problem of spurious coupling because of the volume conduction, field spread, and source leakage. This paper presents a novel method, beamformer-based imaging of PAC (BIPAC), to estimate PAC between sources from electromagnetic signals. For each targeted brain region, this method can extract the source component with the maximum PAC to the reference signal. The results from two simulated MEG data sets demonstrated that the proposed method can achieve high localization accuracy and low spurious coupling.

Combined MEG and EEG show reliable patterns of electromagnetic brain activity during natural viewing

Naturalistic stimuli such as movies are increasingly used to engage cognitive and emotional processes during fMRI of brain hemodynamic activity. However, movies have been little utilized during magnetoencephalography (MEG) and EEG that directly measure population-level neuronal activity at a millisecond resolution. Here, subjects watched a 17-min segment from the movie Crash (Lionsgate Films, 2004) twice during simultaneous MEG/EEG recordings. Physiological noise components, including ocular and cardiac artifacts, were removed using the DRIFTER algorithm. Dynamic estimates of cortical activity were calculated using MRI-informed minimum-norm estimation. To improve the signal-to-noise ratio (SNR), principal component analyses (PCA) were employed to extract the prevailing temporal characteristics within each anatomical parcel of the Freesurfer Desikan–Killiany cortical atlas. A variety of alternative inter-subject correlation (ISC) approaches were then utilized to investigate the reliability of inter-subject synchronization during natural viewing. In the first analysis, the ISCs of the time series of each anatomical region over the full time period across all subject pairs were calculated and averaged. In the second analysis, dynamic ISC (dISC) analysis, the correlation was calculated over a sliding window of 200 ms with 3.3 ms steps. Finally, in a between-run ISC analysis, the between-run correlation was calculated over the dynamic ISCs of the two different runs after the Fisher z-transformation. Overall, the most reliable activations occurred in occipital/inferior temporal visual and superior temporal auditory cortices as well as in the posterior cingulate, precuneus, pre- and post-central gyri, and right inferior and middle frontal gyri. Significant between-run ISCs were observed in superior temporal auditory cortices and inferior temporal visual cortices. Taken together, our results show that movies can be utilized as naturalistic stimuli in MEG/EEG similarly as in fMRI studies.

High-resolution EEG (HR-EEG) and magnetoencephalography (MEG)

High-resolution EEG (HR-EEG) and magnetoencephalography (MEG) allow the recording of spontaneous or evoked electromagnetic brain activity with excellent temporal resolution. Data must be recorded with high temporal resolution (sampling rate) and high spatial resolution (number of channels). Data analyses are based on several steps with selection of electromagnetic signals, elaboration of a head model and use of algorithms in order to solve the inverse problem. Due to considerable technical advances in spatial resolution, these tools now represent real methods of ElectroMagnetic Source Imaging. HR-EEG and MEG constitute non-invasive and complementary examinations, characterized by distinct sensitivities according to the location and orientation of intracerebral generators. In the presurgical assessment of drug-resistant partial epilepsies, HR-EEG and MEG can characterize and localize interictal activities and thus the irritative zone. HR-EEG and MEG often yield significant additional data that are complementary to other presurgical investigations and particularly relevant in MRI-negative cases. Currently, the determination of the epileptogenic zone and functional brain mapping remain rather less well-validated indications. In France, in 2014, HR-EEG is now part of standard clinical investigation of epilepsy, while MEG remains a research technique.

Acquired fears reflected in cortical sensory processing: A review of electrophysiological studies of human classical conditioning

The capacity to associate neutral stimuli with affective value is an important survival strategy that can be accomplished by cell assemblies obeying Hebbian learning principles. In the neuroscience laboratory, classical fear conditioning has been extensively used as a model to study learning-related changes in neural structure and function. Here, we review the effects of classical fear conditioning on electromagnetic brain activity in humans, focusing on how sensory systems adapt to changing fear-related contingencies. By considering spatiotemporal patterns of mass neuronal activity, we illustrate a range of cortical changes related to a retuning of neuronal sensitivity to amplify signals consistent with fear-associated stimuli at the cost of other sensory information. Putative mechanisms that may underlie fear-associated plasticity at the level of the sensory cortices are briefly considered, and several avenues for future work are outlined.

Brain electromagnetic activity and lightning: potentially congruent scale-invariant quantitative properties

The space-time characteristics of the axonal action potential are remarkably similar to the scaled equivalents of lightning. The energy and current densities from these transients within their respective volumes or cross-sectional areas are the same order of magnitude. Length–velocity ratios and temporal durations are nearly identical. There are similar chemical consequences such as the production of nitric oxide. Careful, quantitative examination of the characteristics of lightning may reveal analogous features of the action potential that could lead to a more accurate understanding of these powerful correlates of neurocognitive processes.

Bayesian mixture models for source separation in MEG

This paper discusses the problem of imaging electromagnetic brain activity from measurements of the induced magnetic field outside the head. This imaging modality, magnetoencephalography (MEG), is known to be severely ill posed, and in order to obtain useful estimates for the activity map, complementary information needs to be used to regularize the problem. In this paper, a particular emphasis is on finding non-superficial focal sources that induce a magnetic field that may be confused with noise due to external sources and with distributed brain noise. The data are assumed to come from a mixture of a focal source and a spatially distributed possibly virtual source; hence, to differentiate between those two components, the problem is solved within a Bayesian framework, with a mixture model prior encoding the information that different sources may be concurrently active. The mixture model prior combines one density that favors strongly focal sources and another that favors spatially distributed sources, interpreted as clutter in the source estimation. Furthermore, to address the challenge of localizing deep focal sources, a novel depth sounding algorithm is suggested, and it is shown with simulated data that the method is able to distinguish between a signal arising from a deep focal source and a clutter signal.

Electromagnetic evidence of altered visual processing in autism

Individuals with autism spectrum disorder (ASD) demonstrate intact or superior local processing of visual-spatial tasks. We investigated the hypothesis that in a disembedding task, autistic individuals exhibit a more local processing style than controls, which is reflected by altered electromagnetic brain activity in response to embedded stimuli and enhanced activity of early visual areas. Ten autistic and ten matched control participants underwent 151-channel whole-head magnetoencephalography. Participants were presented with 400 embedded or isolated letters (‘S’ or ‘H’) and asked to indicate which of the two letters was shown. Performance was equal in both groups, but event-related magnetic fields differed between groups in an early (100–150ms) and a later (350–400ms) time window. In the early time window, autistic individuals differed from control participants in the embedded, but not in the isolated condition, reflecting reduced processing of the irrelevant context in autistic individuals. In the later time window, amplitude differences between the embedded and isolated conditions were measured in control participants only, suggesting that “disembedding” processes were not required in autistic individuals. Source localisation indicated that activity in individuals with ASD peaked in the primary visual cortex in both conditions and time windows indicating an effortless (automatic, bottom-up) local process, whereas activity in controls peaked outside the visual cortex.

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

The advent of new signal processing methods, such as non-linear analysis techniques, represents a new perspective which adds further value to brain signals' analysis. Particularly, Lempel-Ziv's Complexity (LZC) has proven to be useful in exploring the complexity of the brain electromagnetic activity. However, an important problem is the lack of knowledge about the physiological determinants of these measures. Although a correlation between complexity and connectivity has been proposed, this hypothesis was never tested in vivo. Thus, the correlation between the microstructure of the anatomic connectivity and the functional complexity of the brain needs to be inspected. In this study we analyzed the correlation between LZC and fractional anisotropy (FA), a scalar quantity derived from diffusion tensors that is particularly useful as an estimate of the functional integrity of myelinated axonal fibers, in a group of sixteen healthy adults (all female, mean age 65.56±6.06 years, intervals 58-82). Our results showed a positive correlation between FA and LZC scores in regions including clusters in the splenium of the corpus callosum, cingulum, parahipocampal regions and the sagittal stratum. This study supports the notion of a positive correlation between the functional complexity of the brain and the microstructure of its anatomical connectivity. Our investigation proved that a combination of neuroanatomical and neurophysiological techniques may shed some light on the underlying physiological determinants of brain's oscillations.

Clinical Neurophysiology in Alzheimer′s Disease

The pathophysiological mechanisms underlying normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) have yet to be fully established. Early recognition of mild cognitive impairment (MCI) and AD requests the identification of biomarkers capable of distinguishing individuals with prodromes from healthy aging adults. Physiological brain aging is characterized by a loss of synaptic contacts and neuronal apoptosis even though neural redundancy as well as functional and structural plastic remodelling of brain networking promotes maintenance of brain activity in healthy elderly for everyday life. It is, then, important to implement techniques that are able to measure changes in normal aging brain and to discriminate them from neurodegenerative processes. As oscillatory electromagnetic brain activity is a hallmark of neuronal network function in various brain regions, an integrated approach utilizing modern neurophysiological techniques, including electroencephalography (EEG), event-related potentials (ERPs), and transcranial magnetic stimulation (TMS), together with biological markers and structural and functional imaging are promising for large-scale, affordable, and noninvasive evaluation of at-risk populations both at a group- and probably also at single-subject level. This special issue contains a series of cutting-edge articles that provides innovative information and deal with the broad issue of the role of neurophysiology for the assessment of normal aging and dementia. Of necessity, these articles focus on selected topics but the mixture of novel contributions as well as review papers on EEG, TMS and ERP provide an overview and an insight into current areas of debate. The first review of this special issue by G. Rodriguez et al., addresses an overview on the usefulness to study brain functional networks in the attempt to find noninvasive biomarkers of dementia; the second one, by R. Lizio et al., focuses on the role of modern EEG and on the possibility of combining its use together with biological and neuropsychological markers and structural and functional imaging for a low-cost, noninvasive, and widely available assessment of groups of individuals at-risk. Following these reviews, new EEG data are presented in the papers written by J. Dauwels et al. and F. Vecchio and C. Babiloni, dealing with the slowing and loss of complexity of EEG in AD patients and with the usefulness of EEG direction information flow in both AD and MCI patients, respectively. The subsequent three papers address EEG methodological issues, focusing on aspects helpful to better diagnose the AD: L.R. Trambaiolli et al. discuss in depth the possibility that EEG montage can influence AD diagnosis, F.B. Vialatte et al. provide suggestions on procedures to make EEG more specific in diagnosis, and K. van der Hiele et al. discuss the potential usefulness of electromyography in supporting the diagnosis of the disease. Then two review papers by F. Vecchio and S. Maatta and by A. Guerra et al., focus, respectively on the possibility to use auditory ERPs as well as TMS in complementing AD diagnosis, staging, and followup. The tenth study, by P. Julkunen et al., combines transcranial magnetic stimulation and electroencephalography (TMS-EEG) to assess the severity of AD. Finally, two case reports are presented, in which neurophysiology plays a central role in supporting the diagnosis of dementia with particular attention to corticobasal loss of functionality (F. Mastrolilli et al.) and possible cooccurrence of nonconvulsive seizures and dementia (C. Campana et al.). As a corollary, the last contribution by L. Valeriani reports some considerations on the actual need in the management of AD patients in emergency departments. Florinda Ferreri Sara Maatta Fabrizio Vecchio Giuseppe Curcio Fabio Ferrarelli

Resting State Cortical Electroencephalographic Rhythms in Alzheimer`s Disease

Physiological brain aging is characterized by a loss of synaptic contacts and neuronal apoptosis that provoke age-dependent decline of cognitive functions. Neural/synaptic redundancy and plastic remodelling of brain networking, also secondary to mental and physical training, promotes maintenance of brain activity in healthy elderly for everyday life and fully productive affective and intellectual capabilities. However, age is the main risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) that impact on cognition. Oscillatory electromagnetic brain activity is a hallmark of neuronal network function in various brain regions. Modern neurophysiological techniques including electroencephalography (EEG) can index normal and abnormal brain aging to facilitate non-invasive analysis of cortico- cortical connectivity and neuronal synchronization of firing, and coherence of rhythmic oscillations at various frequencies. The present review provides a perspective of these issues. It is concluded that discrimination between physiological and pathological brain aging clearly emerges at the group level, with applications at the individual level also suggested. Integrated approaches utilizing neurophysiological techniques together with biological markers and structural and functional imaging are promising for large-scale, low-cost and non-invasive evaluation of at-risk populations.