EEG/MEG Source Localization

Brahma Kumaris Rajayoga's open eyed 'seed stage meditation' was studied in 52 experienced meditators. The meditation consists of different stages: concentration on the feeling of peace, realization of being a soul and communion of the individual soul with the Supreme Soul. Brain electric source localization in the frequency domain was used on multichannel EEG recordings to establish activation differences between meditation and open eyed, task-free resting. Meditation showed reduced activity in delta and increased activity in low alpha frequencies. The affected brain areas comprised the following networks during meditation: the central executive network, the mirroring network and both the task-positive and the task-negative network. The altered activations of these networks and other affected brain areas reflect the main cognitive-affective and behavioral specifics of seed stage meditation: attention modulation, self-related processing, experiencing the soul as a point of light between the eyes, endowing the soul with the properties (peacefulness) of the Supreme Soul. Future studies need to differentiate between the stages of the meditation.

The mammalian brain exists in a number of attractors. In order to characterize these attractors we have
collected the time series data from the EEG recording of rat models. The time series was obtained by
recording of the frontoparietal, occipital and temporal regions of the rat brain. Significant changes have
been observed in the dimensionalities of these brain attractors between the normal state, focal ischemic
state and the drug induced state. Thus, these three states were characterized by unique lyapunov exponents,
correlation dimensions and embedding dimensions. The inverse of the lyapunov exponent gave us the long
term coherence of the rat brain and was found to differ for the three states. The autocorrelation function
measured the mean similarity of the EEG signal with itself after a time t. The degree of decay was high
indicating that there was maximum correlation in the time series. Thus, the autocorrelation functions
clearly indicate the effect of focal cerebral ischemia and drugs induced on the rat brain.

Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic (“resting” or “spontaneous”) electroencephalogram (EEG) into five bands: delta (1–5 Hz), alpha-low (6–9 Hz), alpha-high (10–11 Hz), beta (12–19 Hz), and gamma (N21 Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)- based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time–frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains. KEY WORDS: principal components analysis (PCA); exploratory factor analysis (EFA); blind source separation (BSS); resting neural activity; resting EEG; frontal alpha asymmetry; frontal EEG asymmetry.

WLAN has lately been applied to the problem of mobility tracking and behavior analysis. To further the development of the studies in this direction the positioning system that can perform on the network side with minimal human participation is needed. One of the current limitations is the requirement on the number of reference signal available. Thus, methods that require fewer reference signals for positioning are needed. This paper provides the comparison of WLAN based positioning methods that can operate with a single AP.

This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e. 0.5 - 29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data including fully simulated, semi-simulated and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for the detection of seizures from non-seizure epochs have been found to be easily distinguishable after artifacts are removed and consequently the false alarms in seizure detection are reduced. Results from an extensive experiment with these data-sets prove the efficacy of the proposed algorithm, which makes it possible to use it for artifact removal in epilepsy diagnosis as well as other applications regarding neuroscience studies.

In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided.
This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

The basis of the work of electroencephalography (EEG) is the registration of electrical impulses from the brain using a special sensor or electrode. This method is used to treat and diagnose various diseases. In the past few years, due to the development of neural network technologies, the interest of researchers in EEG has noticeably increased. Neural networks for training the model require obtaining data with minimal noise distortion. In the processing of EEG signals to eliminate noise (artifacts), signal filtering and various methods for extracting signs are used. The presented manuscript provides a detailed analysis of modern methods for extracting the signs of an EEG signal used in studies of the last decade. The information presented in this paper will allow researchers to understand how to more carefully process the data of EEG signals before using neural networks to classify the signal. Due to the absence of any standards in the method of extracting EEG signs, the most important moment of this manuscript is a detailed description of the necessary steps for recognizing artifacts, which will allow researchers to maximize the potential of neural networks in the tasks of classifying EEG signal. IJSB

Individuals show marked variation in their responses to threat. Such individual differences in behavioral inhibition play a profound role in mental and physical well-being. Behavioral inhibition is thought to reflect variation in the sensitivity of a distributed neural system responsible for generating anxiety and organizing defensive responses to threat and punishment. Although progress has been made in identifying the key constituents of this behavioral inhibition system in humans, the involvement of dorsolateral prefrontal cortex (DLPFC) remains unclear. Here, we acquired self-reported Behavioral Inhibition System Sensitivity scores and high-resolution electroencephalography from a large sample (n= 51). Using the enhanced spatial resolution afforded by source modeling techniques, we show that individuals with greater tonic (resting) activity in right-posterior DLPFC rate themselves as more behaviorally inhibited. This observation provides novel support for recent conceptualizations of behavioral inhibition and clues to the mechanisms that might underlie variation in threat-induced negative affect.

In recent years, neural networks showed unprecedented growth that ultimately influenced dozens of different industries, including signal processing for the electroencephalography (EEG) process. Electroencephalography, although it appeared in the first half of the 20th century, was not changed the physical principles of work to this day. But signal processing technology made significant progress in this area through the use of neural networks. But many different models of neural networks complicate the process of understanding the real situation in this area. This manuscript summarizes the current state of knowledge on this topic, summarizes and describes the most significant achievements in various fields of application of neural networks for processing EEG signals. We discussed in detail the results presented in recent research papers for various fields in which EEG signals have been involved. We also examined in detail the process of extracting features from EEG signals using neural networks. In conclusion, we have provided recommendations for the correct demonstration of research results in manuscripts on the subject of neural networks and EEG.

The resistive or non-resistive nature of the extracellular space in the brain is still debated, and is an important issue for correctly modeling extracellular potentials. Here, we first show theoretically that if the medium is resistive, the frequency scaling should be the same for electroencephalogram (EEG) and magnetoencephalogram (MEG) signals at low frequencies (<10 Hz). To test this prediction, we analyzed the spectrum of simultaneous EEG and MEG measurements in four human subjects. The frequency scaling of EEG displays coherent variations across the brain, in general between 1/f and 1/f2, and tends to be smaller in parietal/temporal regions. In a given region, although the variability of the frequency scaling exponent was higher for MEG compared to EEG, both signals consistently scale with a different exponent. In some cases, the scaling was similar, but only when the signal-to-noise ratio of the MEG was low. Several methods of noise correction for environmental and instrumental noise were tested, and they all increased the difference between EEG and MEG scaling. In conclusion, there is a significant difference in frequency scaling between EEG and MEG, which can be explained if the extracellular medium (including other layers such as dura matter and skull) is globally non-resistive.Electronic supplementary material The online version of this article (doi:10.1007/s10827-010-0263-2) contains supplementary material, which is available to authorized users.

Electroencephalography (EEG) provides no direct link between electrode positions and underlying signal generators. Inferences based on spatial proximity between scalp positions and cortical structures are not reliable. More accurate source localization is obtained by solving both the forward and the inverse problem, but is technically challenging. In this paper, we provide a reference table of correspondence between EEG sensors and cortical anatomical regions based on a realistic head model. We also present a universal algorithm to compute the solution by using a forward model to determine the sensitivity for electrodes of any defined electrode positioning system and cortical anatomical parcellation.

Brahma Kumaris Rajayoga&#39;s open eyed &#39;seed stage meditation&#39; was studied in 52 experienced meditators. The meditation consists of different stages: concentration on the feeling of peace, realization of being a soul and communion of the individual soul with the Supreme Soul. Brain electric source localization in the frequency domain was used on multichannel EEG recordings to establish activation differences between meditation and open eyed, task-free resting. Meditation showed reduced activity in delta and increased activity in low alpha frequencies. The affected brain areas comprised the following networks during meditation: the central executive network, the mirroring network and both the task-positive and the task-negative network. The altered activations of these networks and other affected brain areas reflect the main cognitive-affective and behavioral specifics of seed stage meditation: attention modulation, self-related processing, experiencing the soul as a point o...

Abstract. To improve the measurement and differentiation of normal and abnormal brain function we are developing new methods to decompose multichannel (electroencephalogram) EEG into elemental components or &quot;atoms.&quot; We estimate EEG atoms using multiway analysis, specifically parallel factor analysis or PARAFAC for modeling. Activation sequences of EEG atoms can identify functional brain networks dynamically, with much finer time resolution than fMRI. For example, EEG atoms activate in specific combinations during the sequential operations of brain networks, such as Default Mode, Somatomotor, Dorsal Attention and others. Guided by the score values of the identified atoms we inferred the volumetric brain sources of the selected networks using the sLORETA pseudoinverse algorithm. To confirm network identities, we compared 2-D and 3-D functional network maps derived from EEG atoms to known functional neuroanatomy of the networks. We find that multichannel EEGs in most individu...

Mild to severe traumatic brain injuries have lasting effects on everyday functioning.
Issues relating to sensory problems are often overlooked or not addressed until well after the onset
of the injury. In particular, vision problems related to ambient vision and the magnocellular pathway
often result in posttrauma vision syndrome or visual midline shift syndrome. Symptoms from these
syndromes are not restricted to the visual domain. Patients commonly experience proprioceptive,
kinesthetic, vestibular, cognitive, and language problems. Neurooptometric rehabilitation often
entails the use of corrective lenses, prisms, and binasal occlusion to accommodate the unstable
magnocellular system. However, little is known regarding the neural mechanisms engaged during
neurooptometric rehabilitation, nor how these mechanisms impact other domains. Event-related
potentials from noninvasive electrophysiological recordings can be used to assess rehabilitation
progress in patients. In this case report, high-density visual event-related potentials were recorded
from one patient with posttrauma vision syndrome and secondary visual midline shift syndrome
during a pattern reversal task, both with and without prisms. Results indicate that two factors
occurring during the end portion of the P148 component
(168–256 milliseconds poststimulus
onset) map onto two separate neural systems that were engaged with and without neurooptometric
rehabilitation. Without prisms, neural sources within somatosensory, language, and executive
brain regions engage inefficient magnocellular system processing. However, when corrective
prisms were worn, primary visual areas were appropriately engaged. The impact of using early
neurooptometric rehabilitation for posttrauma vision syndrome, visual midline shift syndrome,
and other similar subtle vision disorders to support neural reorganization is discussed.

Forward solutions with different levels of complexity are employed for localization of current generators, which are responsible for the electric and magnetic fields measured from the human brain. The influence of brain anisotropy on the forward solution is poorly understood. The goal of this study is to validate an anisotropic model for the intracranial electric forward solution by comparing with the directly measured ‘gold standard’. Dipolar sources are created at known locations in the brain and intracranial electroencephalogram (EEG) is recorded simultaneously. Isotropic models with increasing level of complexity are generated along with anisotropic models based on Diffusion tensor imaging (DTI). A Finite Element Method based forward solution is calculated and validated using the measured data. Major findings are (1) An anisotropic model with a linear scaling between the eigenvalues of the electrical conductivity tensor and water self-diffusion tensor in brain tissue is validated. The greatest improvement was obtained when the stimulation site is close to a region of high anisotropy. The model with a global anisotropic ratio of 10:1 between the eigenvalues (parallel: tangential to the fiber direction) has the worst performance of all the anisotropic models. (2) Inclusion of cerebrospinal fluid as well as brain anisotropy in the forward model is necessary for an accurate description of the electric field inside the skull. The results indicate that an anisotropic model based on the DTI can be constructed non-invasively and shows an improved performance when compared to the isotropic models for the calculation of the intracranial EEG forward solution.Electronic supplementary materialThe online version of this article (doi:10.1007/s10827-009-0205-z) contains supplementary material, which is available to authorized users.

There are so far two main methodological approaches for rejecting ocular artifacts from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals: regression- and Blind Source Separation (BSS)-based techniques, both having merits, as well as, some serious limitations. In this piece of work, a hybrid methodology that combines the main advantages of these two methods is proposed. We hypothesize that the artifactual independent components (ICs) extracted by a BSS method include more ocular and less cerebral activity than the contaminated EEG signals. We thus propose to apply a regression algorithm to the ICs rather than directly to the recorded signals. The analysis was carried out with synthetic mixtures of real EEG and electroocculographic (EOG) recordings. A BSS method, the extended INFOMAX version of Independent Component Analysis (ICA), was initially used to decompose the artificially contaminated EEG signals into spatiotemporal ICs. Then, a regression scheme, based on a stable version of the Recursive Least Squares algorithm, sRLS, was applied to the artifactual components in order to remove only the ocular artifacts, maintaining the underlying neural signals intact. The processed ICs were then projected back, reconstructing the artifact-free EEG signals. The performance of the proposed technique was compared with two automatic techniques; a regression technique based on Least Mean Square (LMS) algorithm and a BSS-based artifact rejection technique called wavelet-ICA (W-ICA) on the artificially contaminated data. For comparison, two metrics were used to assess the different methods’ performance: the first quantified how successful each technique was in removing the ocular artifacts from the EEG recordings, and the second one quantified how much each technique distorted the ongoing brain activity in both time and frequency domains. Confirming our main hypothesis, results have shown that the artifactual ICs contained more ocular and less cerebral activity (p < 0.04) (artifact to signal ratio (ASR) = 1.83 ± 3.65) in contrast to the contaminated electrode signals (ASR = 0.69 ± 3.40). Our results reveal that the proposed methodology, namely REG-ICA, removes the ocular artifacts more successfully than W-ICA (p < 0.01) or LMS (p < 0.01). It also distorts less the brain activity in the time domain when compared to W-ICA and LMS. In the frequency domain, it distorts the brain activity less than the W-ICA in all frequency bands, and less than the LMS for the delta, beta, and gamma bands. Our results suggest that the proposed methodology is evidently an attractive alternative to other already proposed artifact rejection methodologies.